CN113219433B - Knowledge-aided SR-STAP method and storage medium - Google Patents

Knowledge-aided SR-STAP method and storage medium Download PDF

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CN113219433B
CN113219433B CN202110577866.9A CN202110577866A CN113219433B CN 113219433 B CN113219433 B CN 113219433B CN 202110577866 A CN202110577866 A CN 202110577866A CN 113219433 B CN113219433 B CN 113219433B
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doppler frequency
space
clutter
frequency
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CN113219433A (en
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高志奇
张欢欢
黄平平
谭维贤
徐伟
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Inner Mongolia University of Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract

The invention discloses a knowledge-aided SR-STAP method and a storage medium, wherein the method comprises the following steps: acquiring sample data of a distance unit to be detected through an airborne radar; uniformly dispersing the spatial frequency of the clutter into a grid point set; acquiring a discrete Doppler frequency grid point set of the clutter; adjusting the Doppler frequency based on a preset condition to obtain a new Doppler frequency group; adjusting the spatial frequency to obtain a new discretization spatial frequency group; constructing a space-time guide vector dictionary according to the new Doppler frequency group and the space-time guide vector corresponding to the new discretization space frequency group; carrying out sparse decomposition on the sample data by utilizing the space-time guiding vector dictionary to obtain a sparse recovery vector; and performing clutter covariance matrix estimation by using the sparse recovery vector. The invention jointly adjusts the space frequency and the Doppler frequency, constructs an ultra-complete space-time guiding vector dictionary, effectively solves the problem of grid mismatch and improves the performance of the STAP method.

Description

Knowledge-aided SR-STAP method and storage medium
Technical Field
The invention relates to the technical field of airborne radars, in particular to a knowledge-aided SR-STAP method and a storage medium.
Background
The airborne radar has excellent moving target detection capability and has great application value in the fields of military and civil use. However, the airborne radar is in a downward-looking working state, and the clutter intensity in the received signal is high. In addition, the movement of the carrier leads the distribution range of the noise waves to be widened and the intensity to be increased. Meanwhile, due to the influence of factors such as terrain variation, electromagnetic interference, system errors and the like, clutter presents obvious non-uniform distribution, and the target detection capability of the radar is seriously influenced. In order to solve the above problems, effectively suppress clutter and improve the target detection capability of an airborne radar, a scholars has proposed a Space-time Adaptive Processing (STAP) method. The method utilizes the coupling of clutter in space and time to carry out self-adaptive filtering in a space-time two-dimensional domain, thereby accurately detecting the moving target. In order to ensure better clutter suppression and moving target detection effects, the conventional STAP algorithm generally needs to acquire enough training samples from a uniform clutter environment. However, in practical scenarios, especially in non-homogeneous clutter environments, it is difficult to meet this requirement. Thus, a drastic performance degradation of the conventional STAP method results.
With the rise of the compressed sensing theory, some scholars propose a sparse recovery method for space-time adaptive filtering, and gradually form a sparse power spectrum recovery-based STAP method (SR-STAP). In the method, a redundant base dictionary formed by space-time guide vectors is used for carrying out sparse representation on sample data, elements with larger coefficients are selected to reconstruct the sample data, high-resolution power spectrum estimation of clutter can be formed, and better clutter suppression performance is realized. The dictionary used by sparse representation is generally composed of space-time two-dimensional guide vectors corresponding to uniformly discretized space frequency and Doppler frequency, and the precondition is that the guide vectors of clutter in sample data are accurately matched with part of guide vectors in the dictionary, namely, the clutter can accurately fall on a discretized space-time grid point. In practical application, however, due to factors such as an array working mode and system parameters, a difference exists between a clutter guide vector and a dictionary guide vector, that is, a part of clutter components are no longer located on a discretization space-time plane grid point, so-called off-grid effect occurs, and thus sparse recovery errors are significantly increased and the performance of the STAP filter is seriously reduced. The conventional SR-STAP method uses a fixed dictionary formed by space-time plane uniform discretization, so that clutter sparse recovery has large errors, and clutter suppression and moving target detection are not facilitated.
In order to improve the performance of the SR-STAP method, researchers at home and abroad explore a method for solving the off-grid effect to increase the sparse recovery accuracy. The knowledge-assisted SR-STAP method becomes a research hotspot of scholars at home and abroad in recent years, and scientific research results show that the knowledge-assisted SR-STAP method can better solve the problems of insufficient training samples and performance reduction of the STAP caused by an off-network effect, and relieve the pressure caused by the operation amount and data storage. Researchers at the university of wuhan's science of engineering in 2018 propose an algorithm (KASR-STAP) for alleviating an off-grid effect in the SR-STAP method by adjusting a dictionary by using prior knowledge of a clutter ridge. The algorithm utilizes the prior knowledge of radar system parameters, inertial navigation system information and the like to calculate the clutter ridge distribution, and disperses grids on an angle-Doppler plane, so that the off-grid effect can be well relieved under different array scenes. However, the KASR-STAP method has the problems that when the prior knowledge is used for discretizing grids on an angle-doppler plane, the grid position of doppler frequency is adjusted only according to clutter distribution, the grid position of spatial frequency is not correspondingly adjusted, improvement on off-grid effect (grid mismatch problem) is incomplete, and improvement of the SR-STAP method performance is not ideal. The existing dictionary correction method based on knowledge assistance does not fully consider the coupling of Doppler frequency and spatial frequency, the dictionary correction is not complete, and the performance improvement degree of STAP is not high enough.
Disclosure of Invention
In view of the above technical problems in the prior art, embodiments of the present invention provide a knowledge-aided SR-STAP method, which can solve the technical problems that the existing SR-STAP method is not favorable for clutter suppression and moving target detection, and the existing knowledge-aided dictionary correction method is not comprehensive in improving off-grid effect and the improvement degree of STAP performance is not high enough.
In order to solve the technical problem, the embodiment of the invention adopts the following technical scheme:
a knowledge-aided SR-STAP method comprises the following steps:
acquiring sample data of a distance unit to be detected through an airborne radar, wherein the airborne radar is in front side view work;
uniformly dispersing the spatial frequency of the clutter into a grid point set;
acquiring a discrete Doppler frequency grid point set of the clutter;
adjusting the Doppler frequency based on a preset condition to obtain a new Doppler frequency group;
adjusting the spatial frequency based on the new Doppler frequency group to obtain a new discretization spatial frequency group;
constructing a space-time guide vector dictionary according to the new Doppler frequency group and the space-time guide vector corresponding to the new discretization space frequency group;
carrying out sparse decomposition on the sample data by utilizing the space-time guiding vector dictionary to obtain a sparse recovery vector;
and performing clutter covariance matrix estimation by using the sparse recovery vector.
Further, uniformly discretizing the spatial frequency of the clutter into a set of grid points, comprising:
according to the formula f s = cos ψ uniform dispersion of normalized spatial frequency as N s Set of grid points { f s,i In which N is s ＝ρ s N，ρ s And dividing coefficients for space frequency grids, wherein N is the number of array elements of the airborne radar.
Further, obtaining a set of discrete doppler frequency grid points for the clutter comprises:
according to the formula
Figure GDA0004080347860000031
Obtaining a corresponding discrete Doppler frequency grid point set { f d,i In which θ α Is the angle theta between the array axis and the direction of flight of the aircraft p An angle of yaw for which the beam emitted by the radar antenna is directed, is->
Figure GDA0004080347860000032
Elevation angle for directing radar antenna transmitting beam, beta = 4V/lambda f r Is a folding factor, V is the aircraft flight speed, lambda is the radar operating wavelength, f r Is the pulse repetition frequency.
Further, adjusting the doppler frequency based on the preset condition to obtain a new doppler frequency group, including:
refining the Doppler frequency interval based on a first preset condition;
combining adjacent Doppler frequency intervals based on a second preset condition;
dispersing Doppler frequency meeting a third preset condition by using space frequency interval;
and combining the Doppler frequencies adjusted based on the first preset condition, the second preset condition and the third preset condition to obtain a new Doppler frequency group.
Further, the refining the doppler frequency interval based on the first preset condition includes:
according to the formula
Figure GDA0004080347860000041
Refining the Doppler frequency interval so that the original Doppler frequency interval is Delta d,i Not greater than the spatial frequency interval Δ s Wherein, Δ s ＝|f s,i+1 -f s |，△ d,i ＝|f d,i+1 -f d,i |,i＝1,...,Ns-1。/>
Further, the merging adjacent doppler frequency intervals based on a second preset condition includes:
according to the formula
Figure GDA0004080347860000042
Combining adjacent Doppler frequency intervals to ensure that the original Doppler frequency interval is larger than a delta s Wherein a is an adjusting parameter, and a is more than 0 and less than 1.
Further, the discretizing, by using the spatial frequency interval, the doppler frequency meeting the third preset condition includes:
using the spatial frequency interval Delta s Discrete coincidence a delta s ≤△ d,i ≤△ s The doppler frequency of the condition.
Further, adjusting the spatial frequency based on the new doppler frequency set to obtain a new discretized spatial frequency set, including:
according to the formula
Figure GDA0004080347860000043
Adjusting the spatial frequency to obtain a new set of discretized spatial frequency groups->
Figure GDA0004080347860000044
Further, the method further comprises:
from the estimated clutter covariance matrix
Figure GDA0004080347860000045
By means of a formula>
Figure GDA0004080347860000046
Calculating adaptive filtering weights, wherein S t Is a space-time steering vector, mu is a non-zero constant;
and filtering the sample data of the distance unit to be detected according to the calculated adaptive filtering weight.
Embodiments of the present invention further provide a computer-readable storage medium, on which computer-executable instructions are stored, and when executed by a processor, the computer-executable instructions implement the steps of the knowledge-aided SR-STAP method described above.
Compared with the prior art, the knowledge-aided SR-STAP method provided by the embodiment of the invention combines the knowledge-aided method and the SR-STAP method, jointly adjusts the space frequency and the Doppler frequency interval by means of the priori knowledge of the airborne radar array, the space-time coupling of clutter distribution and other priori information, constructs an ultra-complete space-time guide vector dictionary, comprehensively corrects the dictionary, effectively solves the problem of grid mismatch, improves the sparse recovery precision of the clutter, and improves the performance of the STAP method. In addition, the invention reduces the number of samples required by the algorithm, and is particularly suitable for moving target detection in a clutter non-uniform environment.
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FIG. 1 is a flow diagram of a knowledge-aided SR-STAP method according to an embodiment of the present invention;
FIG. 2 (a) is a diagram of a clutter power spectrum simulation result obtained by using a knowledge-aided SR-STAP method according to an embodiment of the present invention;
FIG. 2 (b) is a diagram of a clutter power spectrum simulation result obtained by using a conventional SR-STAP method;
FIG. 2 (c) is a graph showing the simulation result of clutter power spectrum obtained by the KASR-STAP method;
FIG. 3 is a graph of the signal to noise ratio loss of the output of the method of the present invention, the conventional SR-STAP method and the KASR-STAP method;
FIG. 4 is a comparison graph of the detection capability of the method of the embodiment of the present invention, the conventional SR-STAP method and the KASR-STAP method for a moving object in a sample of distance units to be detected.
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
It will be understood that various modifications may be made to the embodiments disclosed herein. Accordingly, the foregoing description should not be considered as limiting, but merely as exemplifications of embodiments. Those skilled in the art will envision other modifications within the scope and spirit of the application.
The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the application and, together with a general description of the application given above and the detailed description of the embodiments given below, serve to explain the principles of the application.
These and other characteristics of the present application will become apparent from the following description of preferred forms of embodiment, given as non-limiting examples, with reference to the attached drawings.
It should also be understood that, although the present application has been described with reference to some specific examples, a person of skill in the art shall certainly be able to achieve many other equivalent forms of application, having the characteristics as set forth in the claims and hence all coming within the field of protection defined thereby.
The above and other aspects, features and advantages of the present application will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.
Specific embodiments of the present application are described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are merely examples of the application, which can be embodied in various forms. Well-known and/or repeated functions and constructions are not described in detail to avoid obscuring the application of unnecessary or unnecessary detail. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present application in virtually any appropriately detailed structure.
Fig. 1 is a flowchart of a knowledge-aided SR-STAP method according to an embodiment of the present invention.
As shown in fig. 1, the SR-STAP method based on knowledge assistance provided by the embodiment of the present invention includes:
step S1: and acquiring sample data of the distance unit to be detected through an airborne radar, wherein the airborne radar is in front side view work.
The method comprises the steps that an airborne radar transmits pulse signals to a target to be detected and receives radar echo signals, and the radar echo signals of each distance unit of the airborne radar are recorded as sample data of one distance unit to be detected.
The sample data X of an arbitrary distance unit can be expressed as: clutter + noise, according to the sparse representation model, X can be represented as follows:
X＝Φ×α+n
wherein phi is a space-time steering vector matrix, alpha is a sparse recovery vector, and n is noise. By utilizing the sparsity of the clutter power spectrum on a space-time plane, the clutter can be sparsely decomposed into phi multiplied by alpha.
In this embodiment, the airborne radar is an airborne uniform linear array radar, and the flight speed V of the airborne vehicle and the included angle θ between the array axis and the flight direction of the airborne vehicle are determined α When theta is α And when the angle is =0 degrees, the array is a uniform linear array viewed from the front side, and the airborne radar works in the front side view. At this time, the spatial cone angle ψ of the clutter and the normalized Doppler frequency f thereof d There is a coupling relationship between them, and therefore, the clutter should be located on the clutter ridge.
Step S2: and uniformly dispersing the spatial frequency of the clutter into a grid point set.
According to the formula f s = cos ψ uniform dispersion of normalized spatial frequency as N s Set of grid points { f s,i In which N is s ＝ρ s N，ρ s And dividing coefficients for space frequency grids, wherein N is the number of array elements of the airborne radar.
And step S3: a set of discrete doppler frequency grid points of clutter is acquired.
According to the formula
Figure GDA0004080347860000071
Obtaining a corresponding discrete Doppler frequency grid point set { f d,i In which θ α Is the angle theta between the array axis and the direction of flight of the aircraft p An aircraft yaw angle, which points in for the beam emitted by the radar antenna>
Figure GDA0004080347860000072
Elevation angle for directing radar antenna transmitting beam, beta = 4V/lambda f r Is a folding factor, V is the aircraft flight speed, lambda is the radar operating wavelength, f r Is the pulse repetition frequency. Namely, the Doppler frequency is calculated in a discrete space-time plane by using a knowledge-aided method.
And step S4: and adjusting the Doppler frequency based on a preset condition to obtain a new Doppler frequency group.
Step S4 specifically includes:
(1) The doppler frequency interval is refined based on a first preset condition.
According to the formula
Figure GDA0004080347860000073
Refining Doppler frequency interval to ensure original Doppler frequency interval Delta d,i Not greater than the spatial frequency interval Δ s Wherein, Δ s ＝|f s,i+1 -f s,i |，△ d,i ＝|f d,i+1 -f d,i |,i＝1,...,Ns-1。
Spatial frequency interval Δ s Can be expressed as:
Figure GDA0004080347860000074
by the above-mentioned spatial frequency interval Delta s By the expression of (a), the spatial frequency interval Δ s Is constant, so the original Doppler interval Δ between the ith +1 grid point and the ith grid point before the un-refinement is scaled d,i And the spatial frequency interval Delta s Comparison, at Δ d,i Greater than Δ s In the process, the interval delta between the ith grid point and the (i + 1) th grid point is set s And (6) uniformly dividing.
(2) Adjacent doppler frequency intervals are combined based on a second preset condition.
According to the formula
Figure GDA0004080347860000075
Merging adjacent Doppler frequency intervals to ensure the original Doppler frequency interval Delta d,i Greater than a Δ s Wherein a is an adjusting parameter, and a is more than 0 and less than 1. Due to the original doppler frequency interval Δ obtained according to step S3 d,i Not evenly divided, so Δ d,i Different sizes, and the adjustment parameter a is selected in the interval 0 < a < 1, so delta d,i Is not necessarily greater than a Δ s . Therefore, in this step, Δ is used d,i Less than a Δ s The ith and (i + 1) th Doppler intervals are based on the formula>
Figure GDA0004080347860000081
Combining, i.e. may be expressed as combining adjacent doppler frequency intervals.
In this embodiment, by adaptively adjusting the doppler frequency interval of the parameter a and performing theoretical analysis, the optimal value interval of a is as follows:
Figure GDA0004080347860000082
wherein, delta d1,m For the Doppler frequency interval of the echo signal, taking four values of 0.1, 0.2, 0.3 and 0.4 in the optimal value range of a, and obtaining the loss of the output signal-to-noise ratio (SCNR) of the radar signal through experimental verification when the value of a is 0.2 Loss ) Therefore, in this embodiment, a is 0.2.
(3) And dispersing the Doppler frequency meeting the third preset condition by using space frequency interval.
In this step, when the original Doppler frequency interval Δ d,i Satisfies the condition a Δ s ≤△ d,i ≤△ s The original doppler frequency and separation are unchanged. Using the spatial frequency spacing Δ s Discrete coincidence a delta s ≤△ d,i ≤△ s The conditional doppler frequency is sufficient. That is, the third preset condition is determined based on the first preset condition and the second preset condition.
(4) Combining the Doppler frequencies adjusted based on the first preset condition, the second preset condition and the third preset condition to obtain a new Doppler frequency group
Figure GDA0004080347860000083
The condition delta meeting can be obtained by refining and combining and adjusting the Doppler interval through the steps (1) and (2) d,i ≥△ s And Δ d,i ≤a△ s The new Doppler frequency of (4) and the condition a Δ satisfied in step (3) s ≤△ d,i ≤△ s Combining the temporally unadjusted Doppler frequencies to obtain a new set of adjusted Doppler frequencies
Figure GDA0004080347860000084
Step S5: and adjusting the spatial frequency based on the new Doppler frequency group to obtain a new discretization spatial frequency group.
According to the formula
Figure GDA0004080347860000085
Adjusting the spatial frequency to obtain a new set of discretized spatial frequency groups->
Figure GDA0004080347860000086
I.e. each doppler frequency is adjusted in step S4 to obtain a new group of doppler frequencies->
Figure GDA0004080347860000087
Then, the spatial frequency is adjusted by utilizing the prior knowledge to obtain each new spatial frequency to form a group of new discretized spatial frequency groups/based on the previous knowledge>
Figure GDA0004080347860000088
Step S6: according to new Doppler frequency set
Figure GDA0004080347860000091
And the new discretized spatial frequency group->
Figure GDA0004080347860000092
And constructing a space-time steering vector dictionary phi by the corresponding space-time steering vectors.
Step S7: and carrying out sparse decomposition on the sample data X by utilizing the space-time guiding vector dictionary phi to obtain a sparse recovery vector alpha.
Step S1 shows that the clutter component can be sparsely decomposed into Φ × α, and after the space-time steering vector dictionary Φ is determined, the sparse vector α can be obtained by matrix inversion operation.
Step S8: and performing clutter covariance matrix estimation by using the sparse recovery vector.
Specifically, the sample x is replaced by the sparse recovery vector α obtained in step S7 l According to the formula
Figure GDA0004080347860000093
Estimating clutter covariance matrix ≥>
Figure GDA0004080347860000094
In some embodiments, after step S8, the method further comprises:
from the estimated clutter covariance matrix
Figure GDA0004080347860000095
By means of a formula>
Figure GDA0004080347860000096
Calculating adaptive filtering weights, wherein S t Is a space-time steering vector, mu is a non-zero constant;
and filtering the sample data of the distance unit to be detected according to the calculated adaptive filtering weight.
And performing adaptive filtering on the sample data according to the adaptive filtering weight, so that clutter signals in the sample can be suppressed, and a moving target signal can be detected.
The SR-STAP method based on knowledge assistance provided by the embodiment of the invention combines the knowledge assistance and the SR-STAP method, and performs joint adjustment on the space frequency and the Doppler frequency interval by means of the priori knowledge of the airborne radar array, the space-time coupling of clutter distribution and other priori information, so as to construct an ultra-complete space-time guided vector dictionary, comprehensively correct the dictionary, effectively solve the problem of grid mismatch, improve the sparse recovery precision of clutter and improve the performance of the STAP method. In addition, the invention reduces the number of samples required by the algorithm, and is particularly suitable for moving target detection in a clutter non-uniform environment.
According to the embodiment of the invention, simulation experiments are carried out according to the steps, and experimental verification and analysis are carried out on the method of the embodiment of the invention. Wherein, table 1 shows the simulation parameters when the airborne radar is in the side-looking operational mode, when the simulation experiment is performed according to the above steps.
TABLE 1 simulation parameters
Figure GDA0004080347860000101
Fig. 2 (a) to 2 (c) show schematic diagrams of simulation results of clutter power spectrum, wherein fig. 2 (a) shows a simulation result diagram of clutter power spectrum calculated by the method of the present invention, fig. 2 (b) shows a simulation result diagram of clutter power spectrum obtained by the conventional SR-STAP method, and fig. 2 (c) shows a simulation result diagram of clutter power spectrum obtained by the KASR-STAP method. As shown in fig. 2 (a) to 2 (c), clutter power spectrums estimated by the method of the present invention are all concentrated on clutter ridges, and the occurrence of off-grid effect is suppressed, so that the influence of grid mismatch SR-STAP method is suppressed.
Fig. 3 shows output signal-to-noise ratio loss-contrast diagrams of the method of the present invention, the conventional SR-STAP method and the KASR-STAP method. As shown in fig. 3, the curve of the present invention has small fluctuation, and the notch is only generated in the main clutter region, and the depth is deepened by about 4dB compared with the other two algorithms, and the curve outside the main clutter region is also very smooth, which indicates that the adaptive filtering weight calculated by the method of the present invention is not affected by the off-network effect, and the clutter suppression performance is good, thereby improving the detection performance of the moving target.
FIG. 4 shows a comparison of the detection capabilities of the method of the present invention, the conventional SR-STAP method and the KASR-STAP method for a moving object in a sample of range cells to be detected. As shown in fig. 4, the residual clutter power of the method of the present invention is smaller than that of the other two methods. That is to say, the SCNR output by the method is higher than that of the other two methods, the clutter suppression capability is better, and the stronger moving target detection capability is embodied.
Therefore, the knowledge-assisted SR-STAP method can inhibit the influence of the mesh mismatch on the SR-STAP method, effectively improve the detection capability of the moving object and improve the performance of the SR-STAP method.
An embodiment of the present invention further provides a computer-readable storage medium, on which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, the SR-STAP method based on knowledge assistance in the embodiment of the present invention is implemented.
The processor executing the computer-executable instructions described above may be a processing device, such as a microprocessor, central Processing Unit (CPU), graphics Processing Unit (GPU), etc., including one or more general purpose processing devices. More specifically, the processor may be a Complex Instruction Set Computing (CISC) microprocessor, reduced Instruction Set Computing (RISC) microprocessor, very Long Instruction Word (VLIW) microprocessor, processor running other instruction sets, or processors running a combination of instruction sets. The processor may also be one or more special-purpose processing devices such as an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), a system on a chip (SoC), or the like.
The storage medium may be a memory such as Read Only Memory (ROM), random Access Memory (RAM), phase change random access memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), electrically Erasable Programmable Read Only Memory (EEPROM), other types of Random Access Memory (RAM), flash disk or other forms of flash memory, cache, registers, static memory, compact disc read only memory (CD-ROM), digital Versatile Disc (DVD) or other optical storage, magnetic cassettes or other magnetic storage devices, or any other potentially non-transitory medium that may be used to store information or instructions that may be accessed by a computer device, and so forth.
The above embodiments are only exemplary embodiments of the present invention, and are not intended to limit the present invention, and the scope of the present invention is defined by the claims. Various modifications and equivalents may be made by those skilled in the art within the spirit and scope of the present invention, and such modifications and equivalents should also be considered as falling within the scope of the present invention.

Claims (6)

1. A knowledge-aided SR-STAP method is characterized by comprising the following steps:
acquiring sample data of a distance unit to be detected through an airborne radar, wherein the airborne radar is in front side view work;
uniformly dispersing the spatial frequency of the clutter into a grid point set;
acquiring a discrete Doppler frequency grid point set of the clutter;
adjusting the Doppler frequency based on a preset condition to obtain a new Doppler frequency group;
adjusting the spatial frequency based on the new Doppler frequency group to obtain a new discretization spatial frequency group;
constructing a space-time guide vector dictionary according to the new Doppler frequency group and the space-time guide vector corresponding to the new discretization space frequency group;
carrying out sparse decomposition on the sample data by utilizing the space-time guiding vector dictionary to obtain a sparse recovery vector;
performing clutter covariance matrix estimation by using the sparse recovery vector;
the adjusting the doppler frequency based on the preset condition to obtain a new doppler frequency group includes:
refining the Doppler frequency interval based on a first preset condition;
combining adjacent Doppler frequency intervals based on a second preset condition;
dispersing the Doppler frequency meeting a third preset condition by using space frequency intervals;
combining the Doppler frequencies adjusted based on the first preset condition, the second preset condition and the third preset condition to obtain a new Doppler frequency group;
the refining the Doppler frequency interval based on the first preset condition comprises the following steps:
according to the formula
Figure FDA0004094328960000011
Refining the Doppler frequency interval to make the original Doppler frequency interval delta d,i Not greater than spatial frequency interval Δ s Wherein, Δ s ＝|f s,i+1 -f s |，Δ d,i ＝|f d,i+1 -f d,i |,i＝1,...,Ns-1；
The merging of adjacent doppler frequency intervals based on a second preset condition includes:
according to the formula
Figure FDA0004094328960000012
Combining adjacent Doppler frequency intervals to ensure that the original Doppler frequency interval is greater than a delta s Wherein a is an adjusting parameter, and a is more than 0 and less than 1;
the discretizing the Doppler frequency meeting a third preset condition by using the space frequency interval comprises:
using spatial frequency separation Δ s Discrete coincidence a Δ s ≤Δ d,i ≤Δ s The doppler frequency of the condition.
2. The knowledge-aided SR-STAP method according to claim 1, wherein uniformly discretizing the spatial frequency of the clutter into a set of grid points comprises:
according to the formula f s = cos ψ uniform dispersion of normalized spatial frequency as N s Set of grid points { f s,i In which N is s ＝ρ s N，ρ s The space frequency grid division coefficient is adopted, N is the number of array elements of the airborne radar, and psi is the space cone angle of the clutter.
3. The knowledge-aided SR-STAP method according to claim 2, wherein obtaining the set of discrete doppler frequency grid points of the clutter comprises:
according to the formula
Figure FDA0004094328960000021
Obtaining a corresponding discrete Doppler frequency grid point set { f d,i In which θ α Is the angle theta between the array axis and the direction of flight of the aircraft p An aircraft yaw angle, which points in for the beam emitted by the radar antenna>
Figure FDA0004094328960000022
Elevation angle for directing radar antenna transmitting beam, beta = 4V/lambda f r Is a folding factor, V is the aircraft flight speed, lambda is the radar operating wavelength, f r Is the pulse repetition frequency.
4. The knowledge-aided SR-STAP method of claim 1, wherein adjusting spatial frequencies based on the new doppler frequency set to obtain a new discretized spatial frequency set comprises:
according to the formula
Figure FDA0004094328960000023
Adjusting the spatial frequency to obtain a new set of discretized spatial frequency groups->
Figure FDA0004094328960000024
5. The knowledge-aided SR-STAP method according to claim 1, wherein the method further comprises:
from the estimated clutter covariance matrix
Figure FDA0004094328960000025
By means of a formula>
Figure FDA0004094328960000026
Adaptive filtering weights are calculated, wherein, t is a space-time steering vector, mu is a non-zero constant;
and filtering the sample data of the distance unit to be detected according to the calculated adaptive filtering weight.
6. A computer-readable storage medium having stored thereon computer-executable instructions that, when executed by a processor, implement the knowledge-based assisted SR-STAP method according to any of claims 1-5.
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