CN115113155A - Airborne distributed aperture coherent synthetic radar testing and evaluating method - Google Patents
Airborne distributed aperture coherent synthetic radar testing and evaluating method Download PDFInfo
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Abstract
The invention provides an airborne distributed aperture coherent synthetic radar testing and evaluating method, which comprises the steps of constructing a distributed aperture coherent synthetic radar polymerization capability index evaluation system, establishing a distributed aperture coherent synthetic radar polymerization capability index system simulation model, constructing an integrated darkroom verification system, constructing an experiment system according to an anti-interference capability index simulation method, acquiring and managing data in the integrated darkroom verification system, and finally constructing a distributed aperture coherent synthetic radar polymerization and application capability evaluation system. The method for testing and evaluating the performance of the cloud cooperative architecture can be widely applied to the field of distributed aperture coherent synthetic radars, and can effectively improve the accuracy of evaluating the polymerization capability of airborne distributed aperture coherent synthetic radars.
Description
Technical Field
The invention relates to the field of distributed aperture coherent synthetic radars, in particular to a coherent synthetic radar testing and evaluating method used for evaluating the capability of a distributed aperture coherent synthetic radar.
Background
The distributed aperture coherent synthetic radar obviously improves the detection distance and the measurement precision of a radar system by effectively utilizing the resources of airspace, energy domain and the like of a plurality of apertures, has a plurality of technical advantages of strong viability, high cost effectiveness ratio, high angular resolution, strong expansibility, good realizability and the like, and becomes one of important directions and research hotspots for the development of modern radars.
When the coherent synthesis is carried out by adopting an airborne radar, the short baseline synthesis among multiple platforms is difficult to realize due to the constraint limits of detection distance, radar wave band platform formation distance and the like. However, it is considered to install unit apertures at different positions in the platform, and to fuse the unit aperture signals and the target echoes to realize aperture coherence.
The distributed radio frequency coherent synthesis has extremely high requirements on navigation precision, time frequency synchronization precision and the like, and faces various problems of grating lobe inhibition, airspace ambiguity resolution and the like, and the previous research does not relate to the problem of the polymerization capability evaluation of the airborne distributed aperture coherent synthesis radar. The problem has an important influence on the application and combat effectiveness evaluation in the field of distributed aperture coherent synthetic radar.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a method for testing and evaluating an airborne distributed aperture coherent synthetic radar.
The traditional distributed aperture coherent synthesis evaluation method only relates to single indexes such as signal-to-noise ratio gain, distance measurement and direction measurement accuracy and the like, and does not consider the polymerization capability and the comprehensive combat capability of the traditional distributed aperture coherent synthesis evaluation method. Therefore, the invention provides a test and evaluation method of an airborne distributed aperture coherent synthetic radar aiming at the defects of evaluation capability in the traditional method and the characteristic of distributed aperture coherent synthetic. The method adopts multi-level index comprehensive evaluation to effectively represent the polymerization capability of the distributed aperture radar. The cloud collaborative architecture performance testing and evaluating method provided by the invention can be widely applied to the field of distributed aperture coherent synthetic radars, can effectively improve the accuracy of evaluating the aggregation capability of airborne distributed aperture coherent synthetic radars, and simultaneously constructs distributed aperture aggregation and application capability evaluating system software.
The technical scheme adopted by the invention for solving the technical problem comprises the following steps:
step 1: construction of distributed aperture coherent synthetic radar polymerization capacity index evaluation system
Designing a distributed aperture radar aggregation capability evaluation index from a signal and a system application to a system and an external action:
the first part is a signal layer, wherein signal-to-noise ratio gain is the most important evaluation index, the signal-to-noise ratio gain is defined as the ratio of the signal-to-noise ratio of a coherent waveform to the signal-to-noise ratio of a single-path waveform corresponding to single transmission and single reception before the coherent, and when a mobile platform carries out distributed radar coherent synthesis, the coherent performance is mainly influenced by two aspects including errors caused by target motion and errors of platform processing information.
The second part is an application level, after the aperture is polymerized to form a distributed coherent radar system, the application efficiency of the system level is evaluated, the system action efficiency refers to the performance of the distributed radar as a large aperture, and the sub-indexes comprise target positioning accuracy, target direction finding accuracy and detection distance;
the third part is the interaction with the outside, and the anti-interference capability and the capability of resisting the random influence of the environment of the system are evaluated in the process;
step 2: establishing a simulation model of a distributed aperture polymerization capability evaluation index system: the evaluation index of the aggregation capability of the overall system is established according to the three parts in the step 1, the simulation of the signal-to-noise ratio is considered at a signal level, the simulation of the tracking precision is considered at an application level, and the simulation of the anti-interference capability is considered in interaction with the outside;
step 2.1: establishing a signal level index;
the signal-to-noise ratio measuring method comprises the following steps:
the radar equation under the working condition of a single radar is as follows:
where σ is the effective scattering cross-sectional area of the target, P t For peak power of radar transmitter, G t Antenna gain in target direction, λ radar wavelength, R target-to-radar distance, L r For radar reception of combined losses, L t For radar transmission, L Atm For losses in transmission of electromagnetic waves in the atmosphere, G t For receiving antenna gain, T e For equivalent noise temperature of the receiving system, B is the receiver noise bandwidth, (S/N) min K is a Boltzmann constant for detecting a required minimum signal-to-noise ratio of the receiver;
according to the formula (1), the inverse ratio relation between the echo signal-to-noise ratio at the aperture surface of the radar receiving antenna and the fourth power of the distance between the radar and the target is known, namely the signal-to-noise ratio and the distance relation curve generally obey the inverse ratio relation, but due to the fluctuation of the target, the atmospheric attenuation and other reasons, the signal-to-noise ratio curve locally has fluctuation, and the average change rule of the signal-to-noise ratio of a single radar along with the time can be obtained through multiple Monte Carlo simulations;
taking the maximum signal-to-noise ratio as a signal-to-noise ratio curve under the networking condition according to the same time; the index calculation step is as follows:
1) giving the relation between the average value of the signal to noise ratio of a certain target of a single radar and the flight time of the target, namely SNR (i, j, t), wherein i is more than or equal to 1 and less than or equal to N, wherein i represents the ith radar, j represents the jth target, and t represents the flight time of the target;
2) calculating a relation curve of the target signal-to-noise ratio and the target flight time under the networking condition as follows:
(SNR d (i,j,t)) NET ＝maxSNR(i,j,t) (2)；
step 2.2, establishing an application level index;
in the aspect of system-level application efficiency evaluation, the target positioning precision, the target direction finding precision and the detection distance of the detection system are evaluated, and the target track quality and the tracking stability are embodied in an application layer;
the tracking precision is one of the most intuitive evaluation indexes for measuring the track quality, the core of networking fusion is also to improve the tracking precision, and for the networking data fusion, the evaluation of the tracking precision is carried out under a rectangular coordinate system or a geocentric coordinate system with a unified fusion center;
if the target at a certain moment fuses the track
From N pore sizes, it is clear that the optimal fusion protocol should satisfy:
P i (k)＝[P i1 -1 (k)+P i2 -1 (k)+…+P iN -1 (k)] -1 (5)
in the formula (I), the compound is shown in the specification,
the state filtering value of the s-th radar at the ith test and the kth moment is shown; p is (k) For the covariance to be taken into account, it can be seen from equation (5) that the accuracy after fusion should be slightly higher than the filtering accuracy of a single radar.
The index implementation step is that the detection tracks and the fusion tracks of all radars are respectively paired according to the real battle conditions, the tracking precision corresponding to each moment is calculated, and if the Monte Carlo simulation times are insufficient, the time average is used for replacing the statistical average;
step 2.3 establishment of interaction index with the outside world
The anti-interference capability of the index parameters interacting with the outside is mainly considered, and the strong anti-interference capability of the networking radar is attributed to the anti-interference capability of a single radar in the network and the tactical technical characteristics of the networking radar, so two factors are considered for measuring the anti-interference capability of the networking radar;
1) measuring the anti-interference capability of a single radar;
the product of the inherent capability and the additional factor capability is used as the anti-interference measurement of the whole radar system, so that the anti-interference capability of the radar system can be completely and reasonably evaluated, and on one hand, the inherent factor represents the anti-interference performance of the radar; on the other hand, the additional factors basically comprise anti-interference measures commonly used by the radar;
2) networking radar anti-interference capability additional factor
The networking radar anti-interference capability additional factors comprise radar number, space domain overlapping coefficients, frequency domain overlapping coefficients, polarization type coefficients, signal type coefficients and information fusion capability coefficients;
the networking radar anti-interference capacity measurement formula comprehensively considers the main contribution of the anti-interference capacity of the networking radar to the whole system, combines the advantages brought by the networking characteristic to the anti-interference of the system, imitates the single radar anti-interference capacity measurement formula, and quantitatively calculates the anti-interference capacity of the networking radar. The networking radar anti-interference capability measurement formula can be used as a static index for measuring the anti-interference capability of the networking radar and is used for representing the strength of the anti-interference capability of the networking radar.
The anti-interference capability calculation formula of the distributed aperture system considers the main contribution of the anti-interference capability of the aperture in the system to the whole system, simultaneously considers the improvement of the anti-interference capability of the distributed aperture system caused by a networking mode, and can complete the calculation of the inherent anti-interference capability of the distributed aperture system according to the aperture configuration set by simulation;
and step 3: constructing an integrated darkroom verification system, and building an experimental system according to an anti-interference capability index simulation method, wherein in the integrated darkroom verification system, data is collected and managed, namely the data is collected at high speed in parallel;
and 4, step 4: system for constructing aggregation and application capability evaluation of distributed aperture coherent synthetic radar
Preprocessing the output track of the system application capability evaluation index in the step 2, fusing the associated tracks after the track association preprocessing is finished, performing track fusion by adopting a covariance weighting method to obtain the fused tracks and the processed single-aperture tracks, calculating the capability index of an application layer, namely a tracking precision index, respectively outputting the tracking precision and the fused tracking precision of each single aperture, and respectively corresponding to the application evaluation index value and the fused comprehensive evaluation index value of each single aperture.
In step 2.3, the step of calculating the interference resistance of the single radar is as follows:
the product of the inherent capability and the additional factor capability is used as the anti-interference measurement of the whole radar system, so that the anti-interference capability of the radar system can be completely and reasonably evaluated, and on one hand, the inherent factor represents the anti-interference performance of the radar; on the other hand, the additional factors basically comprise anti-interference measures commonly used by the radar;
the measurement expression of the anti-interference capability of the single aperture system is as follows:
AJC＝(PT 0 B S G)·S A ·S S ·S M ·S P ·S C ·S N ·S J (6)
wherein P is the transmit power (W) of the radio aperture; t is 0 Is the signal duration(s); b is S Is the signal bandwidth (Hz); g is an aperture antenna gain value; other parameters are anti-interference improvement factors brought by the processing strategy respectively;
frequency hopping factor S A Comprises the following steps:
in the formula B a Is the allowed maximum frequency hopping range (Hz);
antenna side lobe factor S S Is composed of
In the formula, G M Is the main lobe level of the antenna power pattern; g L A side lobe level for the antenna power pattern;
quality factor S M Comprises the following steps:
S M (dB)＝SCV-25 (9)
wherein SCV is visibility in clutter and is a basic measure for extracting the performance of the pulse radar of a target;
antenna polarization variable factor S P Comprises the following steps:
false alarm processing factor S C Comprises the following steps:
S C (dB)＝10lgΔM-L CF -25 (11)
in the formula, Δ M is the dynamic expansion amount of the receiver after introducing the constant false alarm; l is CF The insertion loss of the constant false alarm is 1-2 dB when the coherent constant false alarm is adopted for processing;
'Wide-limit-narrow' circuit quality factor S N Comprises the following steps:
S N (dB)＝(EIF) D -8 (12)
in the formula (EIF) D The anti-interference improvement factor of the 'wide-limit-narrow' circuit is adopted;
seventhly, repeating the frequency dithering factor S J Comprises the following steps:
S J (dB)＝J-8 (13)
wherein J is a repetition frequency dithering factor;
in the step 2.3, the step of calculating the networking radar anti-interference capability additional factor is as follows:
the networking radar anti-interference capability additional factors comprise radar number, space domain overlapping coefficients, frequency domain overlapping coefficients, polarization type coefficients, signal type coefficients and information fusion capability coefficients;
number of radars
The more the number of radars in a given area is, the stronger the anti-interference capability of the system is theoretically, the more the radar network is, the parameter N is one of the additional indexes for determining the anti-interference capability of the system if the radar network is composed of N radars and N is greater than 2;
second spatial domain overlap factor
The airspace overlapping coefficient reflects the condition that a plurality of radars irradiate a certain airspace simultaneously, N radars are arranged according to a certain plane figure, A is the coverage area of the networking radar, the radar detection area is divided into M layers in the vertical direction according to a certain height, and the detection area on the jth height layer of the ith radar is A ij ＝{(x,y,h)；f ij (x,y,h)≤r ij 1,2, …, N, 1,2, … M, where r is ij The coverage area of the ith radar on the jth height layer is
The average spatial overlap coefficient, K, is defined as:
in equation (14), the coverage area a of the distributed aperture system is:
③ frequency domain overlap coefficient
The countermeasure in the frequency domain is an area where the distributed aperture system is most important and effective in resisting active interference, and is an important means for obtaining spectrum dominance. The wider the bandwidth occupied by the entire distributed system, the greater the immunity of the entire system to interference. When the number of the apertures in the system is determined, if the frequency bands of the apertures in the system are seriously overlapped, the anti-interference performance of the whole system is affected. The occupied frequency band should be as wide as possible for the entire distributed aperture system.
The number of apertures in the distributed aperture system is N, and the bandwidth of each aperture is Deltaf i I is 1,2, …, and M apertures in N apertures generate aperture frequency band overlapping phenomenon, and the bandwidth of the overlapping part is delta f j J — 1,2, …, M, defining the frequency domain overlap coefficient as:
as can be seen from the formula (16), the value range of the coefficient is 0 to 1, when the radar network is completely composed of radars in the same frequency band, η is 1, the interference resistance of the system is the worst, when the radar in the network is composed of radars in completely different frequency bands, η is 0, the interference resistance is the best, and the frequency band configuration is the most reasonable at the time. From the perspective of radar frequency domain anti-interference, the wider the frequency band occupied by the radar and the smaller the frequency domain overlapping coefficient, the stronger the anti-interference capability of the radar.
The anti-interference capability of the radar network is expressed by (2-eta), and the greater the (2-eta), the stronger the anti-interference capability is.
Polarization type coefficient
The polarization modes of the aperture comprise wired polarization (vertical polarization, horizontal polarization), circular polarization (left-hand polarization, right-hand polarization) and elliptical polarization (left-hand polarization and right-hand polarization), and the more the number of polarization types in the distributed aperture system is, the better the anti-interference capability of the whole system is; thus, the polarization type coefficients are defined as:
in the formula (17), m is the total number of polarization types of the distributed aperture system;
the more polarization types a radar net possesses, the stronger its interference rejection capability is. But J is always less than or equal to 1, so that the polarization type of the radar network has a factor of several influences on the interference resistance of the system, which is represented by (1+ J);
signal type coefficient
For distributed aperture systems, the more complex the signal type, the more difficult it is to capture and replicate the waveforms and apply the interference within the system. Therefore, the signal type and the complexity thereof are also used as the indication indexes of the anti-interference capability, and the signal type coefficient is defined as:
in the above formula, k is the total number of signal types of the distributed aperture system;
the more signal types the radar network has, the stronger the anti-detection and anti-interference capability of the radar network, but S is always less than or equal to 1, so that (S +1) represents a factor of the influence of the number of the radar network signal types on the anti-interference capability of the system;
information fusion ability coefficient
In a distributed aperture system, the application of the fusion technology of multiple data sensors is very important. Indexes for evaluating the information fusion comprehensive capacity are many, such as data transmission speed, fusion center processing capacity, fusion mode selection and the like.
And taking the aperture which fails when the distributed aperture system is interfered as an index for measuring the information fusion capability. Defining the information fusion capability coefficient as:
n in formula (19) i The number of apertures that fail to be disturbed;
the efficiency of the radar network is 1 when not interfered and eta when interfered r (≦ 1), defining a parameter describing the anti-interference capability of the radar network as:
η e ＝η r ·η i (20)
the factor describes the interference rejection capability of the radar network based on information comprehensive processing.
After having accomplished the definition to single aperture interference killing feature and distributed aperture system anti-interference additional capacity factor, in order to define the inherent anti-interference measurement factor of whole distributed aperture system, imitate single radar interference killing feature formula, distributed aperture system's anti-interference capacity measurement comprises above-mentioned two parts, one part is the interference killing feature of radar itself in the net, another part is the additional factor that networking technology brought, the interference killing feature of aperture self carries out far and near weighting according to acting distance separately in the distributed aperture system, define distributed aperture system's inherent anti-interference capacity as:
in the formula, k i (i-1, …,5) is a weighting coefficient because of the polarization type coefficient J, the signal type coefficient S, and the information fusion capability coefficientAll eta i The number is 0-1, and 1 is added to the value for convenient calculation.
Or the anti-interference capability of a single radar (AJC) i When (i ═ 1,2, …, n) is expressed in dBW, the overall interference rejection capability of the radar network is also written in dBW, that is:
in the formula: r is i Is the detection range (m), r of the i-th radar av Defining the average detection distance of the radar network as an algebraic average value (m), k) of all the detection distances of the radars in the network i The factor i for each parameter contributing to the interference rejection of the radar network is 1,2, …, 5).
The pretreatment step in the step 4 is as follows:
spatial registration: converting the radar station from a three-dimensional rectangular coordinate system to a geocentric three-dimensional rectangular coordinate system, and converting the geocentric three-dimensional rectangular coordinate system to a geodetic coordinate system;
time registration: acquiring sampling periods of all apertures, determining a common time interval, and taking the sampling periods as registration time points; the method comprises the steps of dividing a flight path by using a common time interval point, dividing the flight path by using the time length of the common time interval as an interval in a sampling period, performing time registration on each divided interval by using a linear interpolation algorithm, performing time registration by using an interpolation function, inserting a piecewise linear interpolation function into a corresponding interpolation node, aligning to an adjacent common moment by using other interpolation nodes as 0, and thus obtaining position information after time registration.
The invention has the beneficial effect that the invention provides a method for testing and evaluating the airborne distributed aperture coherent synthetic radar. Compared with the conventional evaluation method, the aggregation capability of the distributed aperture radar can be effectively represented by adopting multi-level index comprehensive evaluation, the cloud cooperative architecture performance testing and evaluation method provided by the invention can be widely applied to the field of the distributed aperture coherent synthetic radar, and the aggregation capability evaluation accuracy of the airborne distributed aperture coherent synthetic radar can be effectively improved.
Drawings
FIG. 1 is a flow chart of the distributed aperture aggregation capability architecture of the present invention.
Fig. 2 is a distributed aperture aggregation capability assessment index system of the present invention.
Figure 3 is a distributed aperture track processing framework of the present invention.
FIG. 4 is a flow chart of the aggregation evaluation software of the present invention.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
Step 1: designing a distributed aperture aggregation capability evaluation flow;
and 2, step: establishing a distributed aperture polymerization capacity index evaluation system;
and step 3: establishing an index measuring method;
and 4, step 4: and constructing a distributed aperture aggregation and application capability track processing scheme.
The flow of the distributed aperture polymerization capacity system is shown in fig. 1. In order to be able to make an assessment of the aggregation and application capabilities of distributed aperture systems. Firstly, the workflow of the distributed aperture system needs to be analyzed, so that corresponding evaluation index systems are constructed according to different requirements, and then evaluation can be carried out.
The whole test and analysis comprises evaluation index establishment, test system establishment and comparative analysis. Wherein:
the establishment of the evaluation index is mainly divided into two parts, namely, the design of relevant parameters of each single-aperture index is completed, the index structure is analyzed, the calculation method of the index is constructed, the specific realization of the simulation method is constructed, and the simulation realization method of the whole system index is constructed by realizing the algorithm of the polymerization aperture.
The experimental system establishment part is mainly used for establishing a darkroom experimental environment according to established indexes, carrying out simulation experiments according to parameters corresponding to the indexes, acquiring corresponding target data, namely specific index values, in a parallel and high-speed data acquisition mode, providing experimental comparison for establishment of a software system, and confirming reliability and practical capability of software.
And the contrast analysis module is mainly used for performing correlation contrast analysis on the data acquired by testing and the data acquired by software prediction analysis, if the software testing output index result is similar to the experiment acquisition index result, the software construction meets the requirement, otherwise, the software construction is adjusted according to the experiment index value until the experiment index requirement is met, and the software construction is established.
Step 1: construction of distributed aperture coherent synthetic radar polymerization capacity index evaluation system
The aggregation capability evaluation index of the distributed aperture radar is designed from the signal and system application to the system and the external action, and the index system is shown in figure 2.
The first part is a signal layer, wherein signal-to-noise ratio gain is the most important evaluation index, the signal-to-noise ratio gain is defined as the ratio of the signal-to-noise ratio of a coherent waveform to the signal-to-noise ratio of a single-path waveform corresponding to single transmission and single reception before the coherent, and when a mobile platform carries out distributed radar coherent synthesis, the coherent performance is mainly influenced by two aspects including errors caused by target motion and errors of platform processing information.
The second part is an application level, after the aperture is polymerized to form a distributed coherent radar system, the application efficiency of the system level is evaluated, the system action efficiency refers to the performance of the distributed radar as a large aperture, and the sub-indexes comprise target positioning accuracy, target direction finding accuracy and detection distance;
the third part is the interaction with the outside, and the anti-interference capability and the capability of resisting the random influence of the environment of the system are evaluated in the process;
step 2: establishing a simulation model of a distributed aperture aggregation capability assessment index system: the evaluation index of the aggregation capability of the overall system is established according to the three parts in the step 1, the simulation of the signal-to-noise ratio is considered at a signal level, the simulation of the tracking precision is considered at an application level, and the simulation of the anti-interference capability is considered in interaction with the outside;
step 2.1: establishing a signal level index;
the signal-to-noise ratio measuring method comprises the following steps:
the radar equation under the working condition of a single radar is as follows:
where σ is the effective scattering cross-sectional area of the target, P t For peak power of radar transmitter, G t Antenna gain in target direction, λ radar wavelength, R target-to-radar distance, L r For radar reception of combined losses, L t For radar emission combined losses, L Atm For losses in transmission of electromagnetic waves in the atmosphere, G t For receiving antenna gain, T e For equivalent noise temperature of the receiving system, B is the receiver noise bandwidth, (S/N) min K is a Boltzmann constant for detecting a required minimum signal-to-noise ratio of the receiver;
according to the formula (1), the inverse ratio relation between the echo signal-to-noise ratio at the aperture surface of the radar receiving antenna and the fourth power of the distance between the radar and the target is known, namely the signal-to-noise ratio and the distance relation curve generally obey the inverse ratio relation, but due to the fluctuation of the target, the atmospheric attenuation and other reasons, the signal-to-noise ratio curve locally has fluctuation, and the average change rule of the signal-to-noise ratio of a single radar along with the time can be obtained through multiple Monte Carlo simulations;
taking the maximum signal-to-noise ratio as a signal-to-noise ratio curve under the networking condition according to the same time; the index calculation steps are as follows:
1) giving the relation between the average value of the signal to noise ratio of a certain target of a single radar and the flight time of the target, namely SNR (i, j, t), wherein i is more than or equal to 1 and less than or equal to N, wherein i represents the ith radar, j represents the jth target, and t represents the flight time of the target;
2) calculating a relation curve of the target signal-to-noise ratio and the target flight time under the networking condition as follows:
(SNR d (i,j,t)) NET ＝maxSNR(i,j,t) (2)；
step 2.2, establishing an application level index;
in the aspect of system-level application efficiency evaluation, the target positioning precision, the target direction finding precision and the detection distance of the detection system are evaluated, and the target track quality and the tracking stability are embodied in an application layer;
the tracking precision is one of the most intuitive evaluation indexes for measuring the track quality, the core of networking fusion is also to improve the tracking precision, and for the networking data fusion, the evaluation of the tracking precision is carried out under a rectangular coordinate system or a geocentric coordinate system with a unified fusion center;
if the target at a certain moment fuses the track
From N pore sizes, it is clear that the optimal fusion protocol should satisfy:
P i (k)＝[P i1 -1 (k)+P i2 -1 (k)+…+P iN -1 (k)] -1 (5)
in the formula (I), the compound is shown in the specification,
the results of the i-th test are shown,at the kth time, the state filter value of the s-th radar; p is (k) For the covariance to be satisfied, the accuracy after the fusion should be higher than the filtering accuracy of the single radar according to equation (5).
The index implementation step is that the detection tracks and the fusion tracks of all radars are respectively paired according to the real battle conditions, the tracking precision corresponding to each moment is calculated, and if the Monte Carlo simulation times are insufficient, the time average is used for replacing the statistical average;
step 2.3 establishment of interaction index with the outside world
The anti-interference capability of index parameters interacting with the outside is mainly considered, and the strong anti-interference capability of the networking radar is attributed to the anti-interference capability of a single radar in the network and the tactical technical characteristics of the networking radar, so that two factors are considered for measuring the anti-interference capability of the networking radar;
1) measuring the anti-interference capability of a single radar;
the product of the inherent capability and the additional factor capability is used as the anti-interference measurement of the whole radar system, so that the anti-interference capability of the radar system can be completely and reasonably evaluated, and on one hand, the inherent factor represents the anti-interference performance of the radar; on the other hand, the additional factors basically comprise anti-interference measures commonly used by the radar;
the measurement expression of the anti-interference capability of the single aperture system is as follows:
AJC＝(PT 0 B S G)·S A ·S S ·S M ·S P ·S C ·S N ·S J (6)
wherein P is the transmit power (W) of the radio aperture; t is 0 Is the signal duration(s); b is S Is the signal bandwidth (Hz); g is an aperture antenna gain value; other parameters are anti-interference improvement factors brought by the processing strategy respectively;
frequency hopping factor S A Comprises the following steps:
in the formulaB a Is the allowed maximum frequency hopping range (Hz);
second lobe factor S of antenna S Is composed of
In the formula, G M Is the main lobe level of the antenna power pattern; g L A side lobe level for the antenna power pattern;
quality factor S M Comprises the following steps:
S M (dB)＝SCV-25 (9)
wherein SCV is visibility in clutter and is a basic measure for extracting the performance of the pulse radar of a target;
antenna polarization variable factor S P Comprises the following steps:
false alarm processing factor S C Comprises the following steps:
S C (dB)＝10lgΔM-L CF -25 (11)
in the formula, Δ M is the dynamic expansion amount of the receiver after introducing the constant false alarm; l is a radical of an alcohol CF The insertion loss of the constant false alarm is 1-2 dB when the coherent constant false alarm is adopted for processing;
'Wide-limit-narrow' circuit quality factor S N Comprises the following steps:
S N (dB)＝(EIF) D -8 (12)
in the formula (EIF) D The anti-interference improvement factor of the 'wide-limit-narrow' circuit is adopted;
seventhly, repeating the frequency dithering factor S J Comprises the following steps:
S J (dB)＝J-8 (13)
wherein J is a repetition frequency dithering factor;
2) networking radar anti-interference capability additional factor
The networking radar anti-interference capability additional factors comprise radar number, space domain overlapping coefficients, frequency domain overlapping coefficients, polarization type coefficients, signal type coefficients and information fusion capability coefficients;
number of radars
The more the number of radars in a given area is, the stronger the anti-interference capability of the system is theoretically, the more the radar network is, the parameter N is one of the additional indexes for determining the anti-interference capability of the system if the radar network is composed of N radars and N is greater than 2;
second spatial domain overlap factor
The airspace overlap coefficient reflects the condition that a plurality of radars irradiate a certain airspace simultaneously, N radars are arranged according to a certain plane figure, A is the coverage area of networking radar, and divide the radar detection area into M layers in the vertical direction according to a certain height, and the detection area on the jth height layer of the ith radar is A ij ＝{(x,y,h)；f ij (x,y,h)≤r ij 1,2, …, N, 1,2, … M, where r is ij The coverage area of the ith radar on the jth height layer is
The average spatial overlap coefficient, K, is defined as:
in equation (14), the coverage area a of the distributed aperture system is:
③ frequency domain overlap coefficient
The countermeasure in the frequency domain is the field in which the distributed aperture system is most important and effective in resisting active interference, and is an important means for obtaining the spectrum advantage. When the bandwidth occupied by the whole distributed system is wider, the interference resistance of the whole system is stronger. When the number of the apertures in the system is determined, if the frequency bands of the apertures in the system are seriously overlapped, the anti-interference performance of the whole system is affected. The occupied frequency band should be as wide as possible for the entire distributed aperture system.
The number of apertures in the distributed aperture system is N, and the bandwidth of each aperture is Deltaf i I is 1,2, …, and M apertures in N apertures generate aperture frequency band overlapping phenomenon, and the bandwidth of the overlapping part is delta f j J — 1,2, …, M, defining the frequency domain overlap coefficient as:
according to the formula (16), the value range of the coefficient is 0-1, when the radar network is completely composed of radars with the same frequency band, eta is 1, the anti-interference capability of the system is worst, when the radar in the network is composed of radars with different frequency bands, eta is 0, the anti-interference capability is best, and the frequency band configuration is most reasonable at the moment. From the perspective of radar frequency domain anti-interference, the wider the frequency band occupied by the radar and the smaller the frequency domain overlapping coefficient, the stronger the anti-interference capability of the radar.
The anti-interference capability of the radar network is expressed by (2-eta), and the greater the (2-eta), the stronger the anti-interference capability is.
Polarization type coefficient
The polarization modes of the aperture comprise wired polarization (vertical polarization, horizontal polarization), circular polarization (left-hand polarization, right-hand polarization) and elliptical polarization (left-hand polarization and right-hand polarization), and the more the number of polarization types in the distributed aperture system is, the better the anti-interference capability of the whole system is; thus, the polarization type coefficients are defined as:
in the formula (17), m is the total number of polarization types of the distributed aperture system;
the more polarization types a radar network has, the stronger its interference rejection capability is. But J is always less than or equal to 1, so that the polarization type of the radar network has a factor of several influences on the interference resistance of the system, which is represented by (1+ J);
signal type coefficient
For distributed aperture systems, the more complex the signal type, the more difficult it is to capture and replicate the waveforms and apply the interference within the system. Therefore, the signal type and the complexity thereof are also used as the indication indexes of the anti-interference capability, and the signal type coefficient is defined as:
in the above formula, k is the total number of signal types of the distributed aperture system;
the more signal types the radar network has, the stronger the anti-detection and anti-interference capability of the radar network, but S is always less than or equal to 1, so that (S +1) represents a factor of the influence of the number of the radar network signal types on the anti-interference capability of the system;
information fusion ability coefficient
In a distributed aperture system, the application of a fusion technology of multiple data sensors is very important. Indexes for evaluating the information fusion comprehensive capability are many, such as data transmission speed, fusion center processing capability, fusion mode selection and the like.
And taking the aperture which fails when the distributed aperture system is interfered as an index for measuring the information fusion capability. Defining the information fusion capability coefficient as:
n in formula (19) i The number of apertures that fail to be disturbed;
the efficiency of the radar network is 1 when not interfered and eta when interfered r (≦ 1), defining a parameter describing the anti-interference capability of the radar network as:
η e ＝η r ·η i (20)
the factor describes the interference rejection capability of the radar network based on information comprehensive processing.
After having accomplished the definition to single aperture interference killing feature and distributed aperture system anti-interference additional capacity factor, in order to define the inherent anti-interference measurement factor of whole distributed aperture system, imitate single radar interference killing feature formula, distributed aperture system's anti-interference capacity measurement comprises above-mentioned two parts, one part is the interference killing feature of radar itself in the net, another part is the additional factor that networking technology brought, the interference killing feature of aperture self carries out far and near weighting according to acting distance separately in the distributed aperture system, define distributed aperture system's inherent anti-interference capacity as:
in the formula, k i (i ═ 1, …,5) are weighting coefficients because the polarization type coefficient J, the signal type coefficient S, and the information fusion capability coefficient are all η i The number is 0-1, and 1 is added to the value for convenient calculation.
Or the anti-interference capability of a single radar (AJC) i When (i ═ 1,2, …, n) is expressed in dBW, the overall interference rejection capability of the radar network is also written in dBW, i.e.:
in the formula: r is i Detection Range (m) of the ith Radar
r av -radar net average detection distance, defined as algebraic mean of all radar detection distances in the net
(m)
k i -factor i of each parameter contributing to the radar network anti-interference capability is 1,2, …,5)
The networking radar anti-interference capacity measurement formula comprehensively considers the main contribution of the anti-interference capacity of the networking radar to the whole system, combines the advantages brought by the networking characteristic to the anti-interference of the system, imitates the single radar anti-interference capacity measurement formula, and quantitatively calculates the anti-interference capacity of the networking radar. The networking radar anti-interference capability measurement formula can be used as a static index for measuring the anti-interference capability of the networking radar and is used for representing the strength of the anti-interference capability of the networking radar.
The anti-interference capability calculation formula of the distributed aperture system considers the main contribution of the anti-interference capability of the aperture in the system to the whole system, and simultaneously considers the improvement of the anti-interference capability of the distributed aperture system brought by a networking mode. According to the aperture configuration set by simulation, the calculation of the inherent anti-interference capability of the distributed aperture system can be completed;
and step 3: constructing an integrated darkroom verification system, and building an experimental system according to an anti-interference capability index simulation method, wherein in the integrated darkroom verification system, data is collected and managed, namely the data is collected at high speed in parallel;
and 4, step 4: system for constructing aggregation and application capability evaluation of distributed aperture coherent synthetic radar
The purpose of the cloud aggregation evaluation software is to evaluate the overall performance improvement condition of the airborne SAR. In step three, the following were analyzed: the method comprises the steps that a radar device is a signal layer, a target state is acquired, namely an application layer, a platform is an aperture interference layer, namely an interaction layer with the outside, and the aggregation capability of the whole system is determined through three aspects, wherein indexes of the application layer mainly relate to track processing. For the networking data fusion, the estimation of the tracking accuracy is generally performed in a rectangular coordinate system or a geocentric coordinate system unified by a fusion center, so that the output track of the system application capability evaluation index in the step 2 is preprocessed:
spatial registration: converting the radar station from a three-dimensional rectangular coordinate system to a geocentric three-dimensional rectangular coordinate system, and converting the geocentric three-dimensional rectangular coordinate system to a geodetic coordinate system;
time registration: acquiring sampling periods of all apertures, determining a common time interval, and taking the sampling periods as registration time points; dividing the flight path by using a common time interval point, dividing the flight path by using the time length of the common time interval as an interval in a sampling period, performing time registration on each divided interval by using a linear interpolation algorithm, performing time registration by using an interpolation function, inserting a piecewise linear interpolation function into a corresponding interpolation node, aligning to an adjacent common moment by using other interpolation nodes as 0, and thus obtaining position information after time registration;
after the track association preprocessing is finished, the associated tracks are subjected to fusion processing to improve the tracking precision of the same target, a distributed aperture aggregation and application capability evaluation system adopts a covariance weighting method to perform track fusion, and the simulation result shows that the error of the fused tracks is reduced to some extent compared with the error of the tracks independently observed in each aperture, so that the improvement of the performance index of the aggregated aperture radar is demonstrated. The specific flow chart is shown in fig. 3. The fusion adopts Kalman filtering fusion or covariance fusion;
the general flow of the simulation process of the invention is shown in fig. 1, firstly, the evaluation index of the system needs to be constructed, the research and consideration of the whole system are integrated, the evaluation index is decomposed into three indexes for analysis, the three indexes are respectively the index of a signal level, the index of an application level and the index of an interaction level with the outside, wherein the simulation of each index is specifically realized by being given in fig. 2, the three aspects of signal-to-noise ratio, tracking accuracy and anti-interference capability are respectively and sequentially corresponded, and the specific realization methods are respectively given in step 2; it can be known from the construction method that the measurement of the signal-to-noise ratio and the anti-interference capability can be realized by a specific mathematical method, so the verification of the accuracy can be realized by adjusting the input and output values of the parameters, and the specific method is as follows: on the basis of establishing an evaluation system, the input and output values of the evaluation system need to be adjusted and verified to improve the reliability of the whole simulation soft armor, an integrated darkroom verification system is mainly constructed to perform feedback adjustment, the darkroom experiment system is established according to the simulation method given in the step 2, under the condition that the input is equal, when the output value of the actual experiment system is greatly different from the output value of the simulation software, the input of the simulation software needs to be adjusted, and when the simulation output is close to the actual output, the result of the simulation can be determined to be credible.
Aiming at the simulation of the tracking precision, the evaluation value is obtained by processing the flight path, so a flight path file returned by a darkroom is needed to be obtained, the flight path is processed, an actual flight path file of an experiment is returned by each darkroom experiment, the flight path file returned when the signal-to-noise ratio and the anti-interference are reliable is taken as a final flight path file, flight path files corresponding to different apertures are returned by different apertures, the processing process of the flight path is given in figure 3, firstly, the single flight path is needed to be preprocessed, the space registration and the time registration are respectively carried out, the specific method is given in step 4, the preprocessed flight path is fused, the fused flight path and each processed single aperture flight path are obtained, the capability index of an application layer, namely the tracking precision index, is calculated, the specific calculation method is given in step 2, the tracking precision and the fused tracking precision of each single aperture are respectively output, and respectively corresponding to the application evaluation index value of each single aperture and the integrated comprehensive evaluation index value.
By combining the requirements of a battlefield situation cognition prototype system framework and a model system, and surrounding two aspects of framework technical requirements and battlefield situation cognition application requirements, the architectural design in the aspects of clear and easily-realized logic architecture, advanced and evolvable technical architecture, distributed virtualized physical architecture, elastically extensible functional composition, diversified and easily-integrated application, comprehensive and easily-used auxiliary tools, simple and easily-handed secondary development and the like is developed through analysis, extraction and induction. Based on the above analysis, the data flow and framework of the distributed aperture aggregation and application capability evaluation system software of the present invention are shown in fig. 4. The software is realized by adopting Python and PyQt5 technologies.
The invention provides a method for testing and evaluating an airborne distributed aperture coherent synthetic radar. Compared with the conventional evaluation method, the aggregation capability of the distributed aperture radar can be effectively represented by adopting multi-level index comprehensive evaluation, the cloud cooperative architecture performance testing and evaluation method provided by the invention can be widely applied to the field of the distributed aperture coherent synthetic radar, and the aggregation capability evaluation accuracy of the airborne distributed aperture coherent synthetic radar can be effectively improved.
Claims (4)
1. A method for testing and evaluating an airborne distributed aperture coherent synthetic radar is characterized by comprising the following steps:
step 1: construction of distributed aperture coherent synthetic radar polymerization capacity index evaluation system
Designing a distributed aperture radar aggregation capability evaluation index from a signal and a system application to a system and an external action:
the first part is a signal layer, wherein signal-to-noise ratio gain is the most important evaluation index, the signal-to-noise ratio gain is defined as the ratio of the signal-to-noise ratio of a coherent waveform to the signal-to-noise ratio of a single-path waveform corresponding to single transmission and single reception before the coherent, when a mobile platform carries out distributed radar coherent synthesis, the coherent performance is mainly influenced by two aspects, including errors caused by target motion and errors of platform processing information, the simulation of the signal-to-noise ratio is carried out by adopting a calculation method of a radar equation, and the signal-to-noise ratios under different target flight times are measured on the signal layer by obtaining a relation curve of the target signal-to-noise ratio and the target flight time;
the second part is an application level, after the aperture aggregation forms a distributed coherent radar system, the application efficiency of the system level is evaluated, the system action efficiency refers to the performance level of the distributed radar as a large aperture, and the sub-indexes comprise target positioning accuracy, target direction finding accuracy and detection distance;
the third part is the interaction with the outside, and the anti-interference capability and the capability of resisting the random influence of the environment of the system are evaluated in the process;
step 2: establishing a simulation model of a distributed aperture polymerization capacity evaluation index system: the evaluation index of the aggregation capability of the overall system is established according to the three parts in the step 1, the simulation of the signal-to-noise ratio is considered at a signal level, the simulation of the tracking precision is considered at an application level, and the simulation of the anti-interference capability is considered in interaction with the outside;
step 2.1: establishing a signal level index;
the signal-to-noise ratio measuring method comprises the following steps:
the radar equation under the working condition of a single radar is as follows:
where σ is the effective scattering cross-sectional area of the target, P t For peak power of radar transmitter, G t Antenna gain in target direction, λ radar wavelength, R target-to-radar distance, L r For radar reception of combined losses, L t For radar emission combined losses, L Atm For losses in transmission of electromagnetic waves in the atmosphere, G t For receiving antenna gain, T e For equivalent noise temperature of the receiving system, B is the receiver noise bandwidth, (S/N) min K is a Boltzmann constant for detecting a required minimum signal-to-noise ratio of the receiver;
according to the formula (1), the inverse ratio relation between the echo signal-to-noise ratio at the aperture surface of the radar receiving antenna and the fourth power of the distance between the radar and the target is known, namely the signal-to-noise ratio and the distance relation curve generally obey the inverse ratio relation, and the average change rule of the signal-to-noise ratio of a single radar along with time can be obtained through multiple Monte Carlo simulations;
taking the maximum signal-to-noise ratio as a signal-to-noise ratio curve under the networking condition according to the same time; the index calculation step is as follows:
1) giving the relation between the average value of the signal to noise ratio of a certain target of a single radar and the flight time of the target, namely SNR (i, j, t), wherein i is more than or equal to 1 and less than or equal to N, wherein i represents the ith radar, j represents the jth target, and t represents the flight time of the target;
2) calculating a relation curve of the target signal-to-noise ratio and the target flight time under the networking condition as follows:
(SNR d (i,j,t)) NET ＝maxSNR(i,j,t) (2)；
step 2.2, establishing an application level index;
in the aspect of system-level application efficiency evaluation, the target positioning precision, the target direction finding precision and the detection distance of the detection system are evaluated, and the target track quality and the tracking stability are embodied in the application level;
the tracking precision is one of the most intuitive evaluation indexes for measuring the track quality, the core of networking fusion is also to improve the tracking precision, and for the networking data fusion, the evaluation of the tracking precision is carried out under a rectangular coordinate system or a geocentric coordinate system with a unified fusion center;
if the target at a certain moment fuses the track
From N pore sizes, it is clear that the optimal fusion protocol should satisfy:
P i (k)＝[P i1 -1 (k)+P i2 -1 (k)+…+P iN -1 (k)] -1 (5)
in the formula (I), the compound is shown in the specification,
the state filtering value of the s-th radar at the ith test and the kth moment is shown; p is (k) Is the corresponding covariance;
the index implementation step is that the detection tracks and the fusion tracks of all radars are respectively paired according to the real battle conditions, the tracking precision corresponding to each moment is calculated, and if the Monte Carlo simulation times are insufficient, the time average is used for replacing the statistical average;
step 2.3 establishment of interaction index with the outside world
The anti-interference capability of index parameters interacting with the outside is mainly considered, and the strong anti-interference capability of the networking radar is attributed to the anti-interference capability of a single radar in the network and the tactical technical characteristics of the networking radar, so that two factors are considered for measuring the anti-interference capability of the networking radar;
1) measuring the anti-interference capability of a single radar;
the product of the inherent capability and the additional factor capability is used as the anti-interference measurement of the whole radar system, so that the anti-interference capability of the radar system can be completely and reasonably evaluated;
2) networking radar anti-interference capability additional factor
The networking radar anti-interference capability additional factors comprise radar number, space domain overlapping coefficients, frequency domain overlapping coefficients, polarization type coefficients, signal type coefficients and information fusion capability coefficients;
the anti-interference capability calculation formula of the distributed aperture system considers the main contribution of the anti-interference capability of the aperture in the system to the whole system, simultaneously considers the improvement of the anti-interference capability of the distributed aperture system brought by a networking mode, and can complete the calculation of the inherent anti-interference capability of the distributed aperture system according to the aperture configuration set by simulation;
and step 3: constructing an integrated darkroom verification system, and building an experimental system according to an anti-interference capability index simulation method, wherein in the integrated darkroom verification system, data is collected and managed, namely the data is collected at high speed in parallel;
and 4, step 4: system for constructing aggregation and application capability evaluation of distributed aperture coherent synthetic radar
Preprocessing the output track of the system application capability evaluation index in the step 2, fusing the associated tracks after the track association preprocessing is finished, performing track fusion by adopting a covariance weighting method to obtain the fused tracks and the processed single-aperture tracks, calculating the capability index of an application layer, namely a tracking precision index, respectively outputting the tracking precision and the fused tracking precision of each single aperture, and respectively corresponding to the application evaluation index value and the fused comprehensive evaluation index value of each single aperture.
2. The method for testing and evaluating the airborne distributed aperture coherent synthetic radar according to claim 1, wherein:
in step 2.3, the step of calculating the interference resistance of the single radar is as follows:
the measurement expression of the anti-interference capability of the single aperture system is as follows:
AJC＝(PT 0 B S G)·S A ·S S ·S M ·S P ·S C ·S N ·S J (6)
wherein P is the transmit power (W) of the radio aperture; t is 0 Is the signal duration(s); b is S Is the signal bandwidth (Hz); g is an aperture antenna gain value; other parameters are anti-interference improvement factors brought by the processing strategy respectively;
frequency hopping factor S A Comprises the following steps:
in the formula B a Is the allowed maximum frequency hopping range (Hz);
antenna side lobe factor S S Is composed of
In the formula, G M Is the main lobe level of the antenna power pattern; g L A side lobe level for the antenna power pattern;
quality factor S M Comprises the following steps:
S M (dB)＝SCV-25 (9)
wherein SCV is visibility in clutter and is a basic measure for extracting the performance of the pulse radar of a target;
antenna polarization variable factor S P Comprises the following steps:
false alarm processing factor S C Comprises the following steps:
S C (dB)＝10lg△M-L CF -25 (11)
in the formula, the delta M is the dynamic expansion amount of the receiver after introducing the constant false alarm; l is CF The insertion loss of the constant false alarm is 1-2 dB when the coherent constant false alarm is adopted for processing;
'Wide-limit-narrow' circuit quality factor S N Comprises the following steps:
S N (dB)＝(EIF) D -8 (12)
in the formula (EIF) D The anti-interference improvement factor of the 'wide-limit-narrow' circuit is adopted;
seventhly, repeating the frequency dithering factor S J Comprises the following steps:
S J (dB)＝J-8 (13)
wherein J is the repetition frequency dithering factor.
3. The method according to claim 1, wherein the method comprises the following steps:
in the step 2.3, the step of calculating the networking radar anti-interference capability additional factor is as follows:
the networking radar anti-interference capability additional factors comprise radar number, space domain overlapping coefficients, frequency domain overlapping coefficients, polarization type coefficients, signal type coefficients and information fusion capability coefficients;
number of radars
The more the number of radars in a given area is, the more N radars constitute a radar network, N > 2;
second spatial domain overlap factor
The airspace overlapping coefficient reflects the condition that a plurality of radars irradiate a certain airspace simultaneously, N radars are arranged according to a certain plane figure, A is the coverage area of the networking radar, the radar detection area is divided into M layers in the vertical direction according to a certain height, and the detection area on the jth height layer of the ith radar is A ij ＝{(x,y,h)；f ij (x,y,h)≤r ij 1,2, …, N, 1,2, … M, where r is ij The coverage area of the ith radar on the jth height layer is
The average spatial overlap coefficient, K, is defined as:
in equation (14), the coverage area a of the distributed aperture system is:
③ frequency domain overlap coefficient
The number of the apertures in the distributed aperture system is N, and the bandwidth of each aperture is Deltaf i N, M apertures among 1,2, …, the aperture frequency band overlapping phenomenon occurs, the overlapping part bandwidth is Δ f j Where j is 1,2, …, M, the frequency domain overlap coefficient is defined as:
as shown in the formula (16), the value range of the coefficient is 0-1;
the anti-interference capability of the radar network is represented by (2-eta);
polarization type coefficient
The polarization mode of the aperture comprises a wired polarization mode, a circular polarization mode and an elliptical polarization mode, and the polarization type coefficient is defined as follows:
in the formula (17), m is the total number of polarization types of the distributed aperture system;
the (1+ J) represents that the polarization type of the radar network has a factor which influences the interference resistance of the system;
signal type coefficient
The signal type and the complexity thereof are also used as the indication indexes of the anti-interference capability, and the signal type coefficient is defined as:
in the above formula, k is the total number of signal types of the distributed aperture system;
the factor of influence of the signal type possession number of the radar network on the anti-interference capability of the system is represented by (S + 1);
information fusion ability coefficient
Taking the aperture which fails when the distributed aperture system is interfered as an index for measuring the information fusion capability, and defining the coefficient of the information fusion capability as follows:
n in formula (19) i The number of apertures that fail to be disturbed;
the efficiency of the radar network is 1 when not interfered and eta when interfered r (≦ 1), defining a parameter describing the anti-interference capability of the radar network as:
η e ＝η r ·η i (20)
the factor describes the anti-interference capability of the radar network based on information comprehensive processing;
after having accomplished the definition to single aperture interference killing feature and distributed aperture system anti-interference additional capacity factor, in order to define the intrinsic anti-interference measurement factor of whole distributed aperture system, imitate single radar interference killing feature formula, distributed aperture system's interference killing feature measurement comprises above-mentioned two parts, one part is the interference killing feature of radar itself in the net, another part is the additional factor that networking technology brought, the interference killing feature of aperture self carries out far and near weighting according to acting distance separately in the distributed aperture system, define distributed aperture system's intrinsic interference killing feature as:
in the formula, k i (i ═ 1, …,5) are weighting coefficients because the polarization type coefficient J, the signal type coefficient S, and the information fusion capability coefficient are all η i The number is 0-1, and 1 is added to the value for convenient calculation;
or the anti-interference capability of a single radar (AJC) i When (i ═ 1,2, …, n) is expressed in dBW, the overall interference rejection capability of the radar network is also written in dBW, i.e.:
in the formula: r is i Is the detection range (m), r of the i-th radar av Defining the average detection distance of the radar network as an algebraic average value (m), k) of all the detection distances of the radars in the network i The factor i for each parameter contributing to the interference rejection of the radar network is 1,2, …, 5).
4. The method for testing and evaluating the airborne distributed aperture coherent synthetic radar according to claim 1, wherein:
the pretreatment step in the step 4 is as follows:
spatial registration: converting the radar station from a three-dimensional rectangular coordinate system to a geocentric three-dimensional rectangular coordinate system, and converting the geocentric three-dimensional rectangular coordinate system to a geodetic coordinate system;
time registration: acquiring sampling periods of all apertures, determining a common time interval, and taking the sampling periods as registration time points; the method comprises the steps of dividing a flight path by using a common time interval point, dividing the flight path by using the time length of the common time interval as an interval in a sampling period, performing time registration on each divided interval by using a linear interpolation algorithm, performing time registration by using an interpolation function, inserting a piecewise linear interpolation function into a corresponding interpolation node, aligning to an adjacent common moment by using other interpolation nodes as 0, and thus obtaining position information after time registration.
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| CN116827424A (en) * | 2023-08-25 | 2023-09-29 | 湖南力研光电科技有限公司 | Anti-interference method for multi-frequency multi-mode phased array antenna |
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| CN116520307A (en) * | 2023-02-20 | 2023-08-01 | 扬州宇安电子科技有限公司 | Radar cooperative detection system and method based on target tracking |
| CN116520307B (en) * | 2023-02-20 | 2023-10-27 | 扬州宇安电子科技有限公司 | Radar cooperative detection system and method based on target tracking |
| CN116827424A (en) * | 2023-08-25 | 2023-09-29 | 湖南力研光电科技有限公司 | Anti-interference method for multi-frequency multi-mode phased array antenna |
| CN116827424B (en) * | 2023-08-25 | 2023-11-10 | 湖南力研光电科技有限公司 | Anti-interference method for multi-frequency multi-mode phased array antenna |
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