CN116106829A - Intelligent target detection method under partial uniform clutter and interference - Google Patents
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Abstract
The invention belongs to the field of broadband radar signal processing, and particularly relates to an intelligent target detection method under partial uniform clutter and interference. The clutter is modeled by adopting a partial uniform model, namely, the clutter covariance matrix structures of the main data and the auxiliary data are assumed to be the same, but the power levels of the main data and the auxiliary data are different. The method is characterized in that a two-step detector design program is adopted, and a target intelligent detection method under partial uniform clutter and interference with a closed form is constructed based on a Gradient test criterion so as to solve the problem of radar distance expansion target self-adaptive detection under a complex external interference environment. The constructed detector not only can ensure CFAR characteristics, but also can meet the multi-aspect requirements of the distance expansion target self-adaptive detection algorithm, such as computational complexity, intelligent anti-interference, mismatch robustness and the like, and improves the self-adaptive detection performance of the multi-channel broadband radar on the weak and small targets and the mismatch targets in the complex interference environment.
Description
Technical Field
The invention belongs to the field of broadband radar signal processing, and particularly relates to an intelligent target detection method under partial uniform clutter and interference.
Background
With the increase of radar bandwidth, the broadband radar gradually covers the modern military and civil fields of anti-interference, anti-reconnaissance, accurate detection and imaging, high-precision tracking, target identification and the like, and the self-adaptive detection around the distance expansion target becomes a hotspot problem of the radar world. Unlike narrowband radar target echo signals, which typically occupy only one range-resolved element, wideband radar target energy may spread to adjacent range elements, presenting a "one-dimensional range profile," forming a range-extended target. If a point target detection method is adopted to carry out target detection on echo signals aiming at a single distance unit, and background clutter statistical characteristic estimation is carried out by utilizing adjacent distance unit sampling, on one hand, the phenomenon of adjacent unit signal pollution caused by the energy leakage of a strong scattering point of a distance-expanded target is easy to occur, and a shielding effect is further formed on target signals of the single distance unit to be detected, so that the target detection effect is poor; on the other hand, in practical application, the natural environment of the target is complex and changeable, and meanwhile, natural or artificial interference sources such as electronic countermeasure or civil broadcasting systems may exist, so that clutter non-uniformity is enhanced, and further, an ideal detection effect is difficult to obtain in the existing distance-expanded target detection method.
The distance-extended target adaptation detection is mainly achieved by means of assistance data. The auxiliary data is generally taken from a reference distance unit spatially adjacent to the distance unit to be detected, and is assumed to contain no target signal, but only clutter components which are independently and uniformly distributed with main data clutter components of the distance unit to be detected, and accurate estimation of an unknown clutter covariance matrix can be realized by using sufficient auxiliary data. However, for the situations of severe clutter power change, discrete clutter, clutter edges and other abnormal values faced by an actual radar, the uniformity of clutter background is destroyed, and auxiliary data meeting global uniformity is sometimes difficult to acquire, so that the self-adaptive detection performance of a distance expansion target is seriously affected. In fact, although the global uniformity of the complex clutter background is destroyed, the local uniformity of the clutter is still reflected within a certain radial distance range, and at this time, the clutter can be modeled by using a partial uniformity model, namely, the distance unit to be detected and the clutter components in the reference distance unit have the same covariance matrix structure and different power levels, and the model can fully utilize the local uniformity of the clutter, but the available reference distance unit number is limited by the actual clutter non-uniformity degree.
In addition, in the conventional rank-one signal target detection model, the steering vector of the target is usually assumed to be a known fixed vector, but in practical application, there may be a mismatch condition of the steering vector of the target due to beam pointing error and multipath phenomenon. To address this problem, modeling the target signal with a subspace model may be considered. In the subspace model, the signal is represented as the product of a known subspace matrix and an unknown coordinate matrix. If subspace modeling is adopted for the target and the interference signal based on the whole data set formed by the main data and the auxiliary data of the plurality of distance units to be detected, and the GLRT criterion is utilized to construct detection statistics, a subspace GLRT detector (S-GLRT-PHE) of the distance expansion target under partial uniform clutter and structured interference can be obtained. The detector can obtain better detection performance, but the calculation process is complex, and the solution is inconvenient. If the Rao detection criterion is adopted, a subspace Rao detector (S-Rao-PHE) of the distance expansion target under partial uniform clutter and structured interference can be obtained. Compared with the GLRT detector, the detection performance of the detector is improved under a part of setting environment, but the calculation complexity of detection statistics is higher, and engineering realization is inconvenient.
Aiming at complex detection environments comprising heterogeneous clutter, external structured interference and the like faced by multi-channel broadband radar range expansion target self-adaptive detection, how to reasonably design a range expansion target self-adaptive detector form, effectively inhibit interference signals while maintaining Constant False Alarm Rate (CFAR) characteristics, and consider the effective balance among mismatch robustness, algorithm calculation complexity and detection performance, is a key for improving the detection capability of the broadband radar in the complex interference environment, and is one of the difficulties faced by the multi-channel broadband radar range expansion target self-adaptive detection.
Disclosure of Invention
In order to overcome the problems in the prior art, the invention provides an intelligent target detection method under partial uniform clutter and interference.
The technical scheme for solving the technical problems is as follows:
a target intelligent detection method under partial uniform clutter and interference comprises the following steps:
step 3, obtaining maximum likelihood estimation of unknown clutter power factor gamma under the non-target hypothesis by solving a unique positive value solution of a eigenvalue equation under the non-target hypothesis, and bringing the maximum likelihood estimation of the clutter power factor gamma into the distance extended target two-step Gradient detection statistic obtained in the step 2 to replace the unknown clutter power factor gamma, so as to construct detection statistic lambda of the target intelligent detection method under partial uniform clutter and interference;
Further, in the step 1, when the clutter covariance matrix M and the clutter power factor γ are known, the two-step Gradient detection statistic of the target intelligent detector under the partial uniform clutter and interference is:
wherein ,
in the formula, the main data z= [ Z ] 1 ,z 2 ,...,z K ]The NxK-dimensional complex matrix is represented by the Nx1-dimensional received complex signal in the t th distance unit to be detected as z t ＝s t +j t +c t (t=1, 2,., K), where N x 1-dimensional target complex signal vector s t And an N x 1-dimensional interference complex vector j t Are all assumed to be deterministic and are denoted s, respectively t ＝Ηp t and jt ＝Jq t H and J are respectively known column full rank N x p dimension target signal subspace complex matrix and N x q dimension interference signal subspace nullM-complex matrix, p x 1-dimensional complex vector p t And a qx1-dimensional complex vector q t Unknown complex coordinate vectors representing the target signal and the interfering signal, respectively; subspaces h and J are linearly independent, constructing an nxx (p+q) dimensional column-full rank augmentation matrix b= [ H J ]]And p+q is less than or equal to N; superscript (·) T and (·)H Representing the transpose and the conjugate transpose, respectively, & determinant representing the square matrix, tr function representing the trace taking the square matrix; i m Representing an m x m dimensional identity matrix.
Further, in the step 2, the complex gaussian probability density function of the auxiliary data Y is used to derive and zero the clutter covariance matrix M, i.e. the corresponding derivative is zero, so that the maximum likelihood estimate of M based on the auxiliary data is obtained as the sample covariance matrix S, i.e
Auxiliary data y= [ Y ] 1 ,y 2 ,...,y R ]Expressed as an NxR-dimensional complex matrix, R observation data y are obtained from R reference distance units adjacent to the distance unit to be detected t (t=1, 2., (v), R), assuming y t (t=1, 2,.,. R) contains only pure clutter components, with the n×1-dimensional complex vector y of the t-th reference distance cell t (t=1, 2,) R satisfies
Which are also independently and identically distributed among the different distance units.
Further, in the step 2, the maximum likelihood estimation of the clutter covariance matrix M is carried into the two-step Gradient detection statistic of the distance expansion target obtained in the step 1, the unknown clutter covariance matrix is replaced, and the two-step Gradient detection statistic of the distance expansion target under the condition of the known clutter covariance matrix is constructed:
wherein ,
further, the eigenvalue equation constructed in the step 3 is:
wherein v represents an unknown parameter, lambda k,0 Representation matrix
Is the kth non-zero eigenvalue of (c).
Further, in the step 3, the maximum likelihood estimation of the clutter power factor gamma is performed
And (3) substituting unknown clutter power factors gamma in the two-step Gradient detection statistics of the distance-expanded target obtained in the step (2) to construct detection statistics lambda of the target intelligent detection method under partial uniform clutter and interference:
compared with the prior art, the invention has the following technical effects:
1) The intelligent target detection method under partial uniform clutter and interference is constructed, and the detector has an expression in a closed form, has lower calculation complexity and is convenient for engineering realization;
2) Aiming at the interference environment with subspace structuring, the intelligent target detection method under partial uniform clutter and interference can effectively inhibit interference signals with different intensities, and has good intelligent anti-interference performance;
3) Aiming at the situation of target signal guide vector mismatch, the target intelligent detection method under partial uniform clutter addition interference can effectively detect the mismatch signal, and has stronger detection robustness on the mismatch signal;
4) The detection method disclosed by the invention maintains the CFAR characteristics, simultaneously gives consideration to the performance balance of algorithm calculation complexity, detection performance and mismatch robustness, and improves the self-adaptive detection performance of the multi-channel broadband radar on the weak and small targets and the mismatch targets in a complex environment;
5) The method is suitable for partial non-broadband radar detection situations, for example, detection of large targets or detection of space adjacent point target groups (conditions of naval vessel formation, airplane formation, vehicle formation and the like) moving at the same speed by using low/medium resolution radars, and has good application prospect.
Drawings
FIG. 1 is a functional block diagram of a method for intelligent detection of targets under partial uniform clutter plus interference according to the present invention;
FIG. 2 is a graph comparing the detection performance of the method of the present invention and the detection performance of the existing detection method for the matching signal;
FIG. 3 is a graph comparing the detection performance of the method of the present invention with that of the existing detection method for mismatch signals;
in fig. 2, n= 8,K =15, r=16, p=3, q=2, false alarm probability P fa ＝10 -3 Interference clutter power ratio icr=15 dB;
in fig. 3, n= 8,K =15, r=16, p=3, q=2, p fa ＝10 -3 Icr=15 dB, mismatch angle square cos 2 φ＝0.5。
Detailed Description
The principles and features of the present invention are described below with reference to the drawings, the examples are illustrated for the purpose of illustrating the invention and are not to be construed as limiting the scope of the invention.
Aiming at the difficult problem that the existing broadband radar distance expansion target self-adaptive detector is difficult to consider the algorithm calculation complexity, CFAR characteristics and mismatch performance, the target intelligent detection method under partial uniform clutter and interference with a closed form is constructed based on the Gradient test criterion of the two-step detector design program, the CFAR characteristics are ensured, the multi-aspect requirements of the distance expansion target self-adaptive detection algorithm such as calculation complexity, intelligent anti-interference and mismatch robustness are met, and the self-adaptive detection performance of the multichannel broadband radar on weak and small targets and mismatch targets in a complex interference environment is improved.
The intelligent target detection method under the partial uniform clutter and interference comprises the following steps:
The step 1 specifically comprises the following substeps:
for a coherent radar system with a space-time joint channel number of N, consider H 0 and H1 Binary hypothesis test problem of (1), wherein H 0 Assuming that the lower target is not present, only pure clutter is present; h 1 The lower target, clutter and interference are assumed to be present.
Assuming that the target may occupy K consecutive distance cells to be detected, under assumption H 1 Next, the Nx1-dimensional received complex signal in the t-th distance to be detected unit is denoted as z t ＝s t +j t +c t (t=1, 2,., K), where N x 1-dimensional target complex signal vector s t And an N x 1-dimensional interference complex vector j t Are all assumed to be deterministic and can be denoted s, respectively t ＝Ηp t and jt ＝Jq t H and J are respectively known column-full rank N x p-dimensional target signal subspace complex matrix and N x q-dimensional interference signal subspace complex matrix, p x 1-dimensional complex vector p t And a qx1-dimensional complex vector q t The unknown complex coordinate vectors representing the target signal and the interference signal, respectively, and the main data may be represented as n×k-dimensional complex matrix z= [ Z 1 ,z 2 ,...,z K ]. Note that subspaces h and J are linearly independent, build an nxx (p+q) dimensional column-full rank augmentation matrix b= [ H J]And p+q is less than or equal to N. The t thN x 1 dimension clutter vector c in distance unit to be detected t Is a zero-mean complex circular Gaussian vector expressed as
And the clutter vectors between different distance units are independently and uniformly distributed, wherein the clutter covariance matrix M of N multiplied by N dimension is an unknown Hermitian positive complex matrix.
At H 0 and H1 Under the assumption, the complex gaussian Probability Density Function (PDF) of the main data Z can be expressed as:
wherein the unknown target coordinate matrix
Unknown interference coordinate matrix
Superscript (·) T and (·)H The transpose and the conjugate transpose, respectively, & determinant for the square matrix, and tr function for the trace for the square matrix.
Under the known conditions of the clutter covariance matrix M and the clutter power factor gamma, a partial uniform clutter and interference target intelligent detector is constructed based on a Gradient detection criterion of a two-step detector design criterion, and the two-step Gradient detection statistic of the distance expansion target can be expressed as follows:
wherein ,
target parameter vector->
Unknown, interference parameter vector +.>
Is unknown; />
Represents Θ at H 0 Maximum likelihood estimation under the assumption; theta (theta) r0 Representing Θ r At H 0 Values under the assumption +_>
Representing Θ r At H 1 Maximum likelihood estimation under the assumption; the vec function implements vectorization of the matrix.
By complex Gaussian probability density function pair Θ of main data Z under target hypothesis r Derivation, i.e. equation (2) vs. Θ r The deviation is calculated, and the following steps are obtained:
the interference coordinate matrix Q is subjected to partial derivation by using the method (1), and the derivation result is set to be zero, so that H can be obtained 0 The maximum likelihood estimate for the lower interference coordinate matrix Q is assumed to be:
wherein ,
then substituting the formula (5) into the formula (4) to obtain
wherein ,
I m representing an m x m dimensional identity matrix.
The deviation of D is calculated by using the step (2), and the result of the deviation calculation is set to be zero, so that H can be obtained 1 The maximum likelihood estimate for D is assumed to be:
wherein ,
note H 1 Let us assume a maximum likelihood estimation (denoted as
) Is->
Then the target parameter vector at H can be obtained 1 Maximum likelihood estimation under the assumption +.>
The method comprises the following steps:
in addition, note H 0 Assuming that the target signal is not present, there is a r0 ＝0。
Substituting the formula (6) and the formula (8) into the formula (3), and obtaining through algebraic operation for multiple times, wherein when the clutter covariance matrix M and the clutter power factor gamma are known, the two-step Gradient detection statistic of the target intelligent detector under partial uniform clutter and interference is as follows:
wherein ,
the method comprises the following specific steps:
to estimate the clutter covariance matrix M, R observation data y are obtained from R reference distance units adjacent to the distance unit to be detected t (t=1, 2., (v), R), assuming y t (t=1, 2,) R contains only pure clutter components, then the auxiliary data can be represented as n×r-dimensional complex matrix y= [ Y 1 ,y 2 ,...,y R ]Wherein the N x 1-dimensional complex vector y of the t-th reference distance unit t (t=1, 2,) R satisfies
Which are also independently and identically distributed among the different distance units.
The complex Gao Sifu gaussian probability density function PDF of Y can be expressed as:
deriving the clutter covariance matrix M by using the method (10) and setting zero, namely setting the corresponding derivative to be zero, so that the maximum likelihood estimation of the clutter covariance matrix M based on auxiliary data can be obtained as a sample covariance matrix S, namely
Maximum likelihood estimation of clutter covariance matrix of (11)
And (3) substituting the unknown clutter covariance matrix M in the formula (9) to obtain the target intelligent detector under partial uniform clutter and interference under the condition of the known clutter power factor, wherein the detection statistic is as follows:
wherein ,
step 3, obtaining maximum likelihood estimation of unknown clutter power factors under the non-target hypothesis by solving a unique positive value solution of a eigenvalue equation under the non-target hypothesis, and bringing the maximum likelihood estimation of the clutter power factors into the distance extended target two-step Gradient detection statistic obtained in the step 2 to replace the unknown clutter power factors and construct detection statistic lambda of the target intelligent detection method under partial uniform clutter and interference;
the method comprises the following specific steps:
constructing a eigenvalue equation (14), and obtaining the maximum likelihood estimation of the unknown clutter power factor gamma under the no-target hypothesis by solving the unique positive value solution of the equation (14)
In hypothesis H 0 Under, maximum likelihood estimation of gamma
Is the only positive solution that satisfies the eigenvalue equation of equation (14):
wherein v represents an unknown parameter, lambda k,0 Representation matrix
Is the kth non-zero eigenvalue of (c).
Will assume H 0 Maximum likelihood estimation of lower unknown clutter power factor
Substitution (12), substitution of unknown clutter power factor under no target hypothesis, i.e. with +.>
The detection statistic lambda of the target intelligent detection method under partial uniform clutter plus interference can be obtained by simplified operation by replacing gamma in the formula (12) as follows:
the method constructs the target intelligent detector under the condition of partial uniform clutter and interference. As can be seen from the equation (15), the proposed target intelligent detection method under the partial uniform clutter plus interference has a closed form detection statistic expression without iterative operation. In addition, compared with an S-GLRT-PHE detector of a distance extension target, the method for intelligently detecting the target under partial uniform clutter and interference has lower algorithm computation complexity and stronger detection robustness on a guide vector mismatch signal. In a comprehensive view, the target intelligent detection method under partial uniform clutter and interference can effectively balance algorithm calculation complexity, mismatch robustness and detection performance while maintaining CFAR characteristics.
To verify the effectiveness of the method of the present invention, this embodiment provides two examples, the first example being directed to a sea detection environment and the second example being directed to a ground detection environment.
Example 1:
referring to fig. 1 of the specification, the embodiment of example 1 is divided into the following steps:
step A1, carrying out radar irradiation on a sea area to be detected by using a sea detection radar to obtain main data Z of K distance units to be detected; send main data Z to H 0 Maximum likelihood estimation solving module under assumption, H 1 A derivative module of probability density function under assumption and a maximum likelihood estimation solving module under H1 assumption; at H 0 In the assumed maximum likelihood estimation solving module, H is obtained according to the formulas (5) and (14) 0 Assuming maximum likelihood estimation of Q and γ
and />
At H 1 In the derivative module of the probability density function under the assumption, H is obtained according to the formula (4) 1 Supposing that complex Gaussian probability density function of main data Z is opposite to target parameter vector Θ r Is a result of the derivation of (1); at H 1 In the assumed maximum likelihood estimation solving module, H is obtained according to the formula (8) 1 Supposing that theta r Maximum likelihood estimation of (2)
Will be described above in H 0 Maximum likelihood estimation solving module under assumption, H 1 Derivation module and H of probability density function under assumption 1 And (3) sending a result obtained by the maximum likelihood estimation solving module under the assumption to a two-step Gradient detection statistic constructing module under the condition of a known covariance matrix, constructing a distance extension target two-step Gradient detection statistic under the condition of the known clutter covariance matrix according to a formula (9), and sending the distance extension target two-step Gradient detection statistic to a target intelligent detector constructing module under the condition of partial uniform clutter and interference.
It is noted that in step A1, the complex gaussian distribution is used to model the sea clutter component, and meanwhile, the fact that external interference may exist in the actual marine environment has adverse effect on adaptive detection of the distance expansion target is considered, so that the external interference is also considered in the design process of the detector, and subspace signals are adopted to model the interference, so that mismatch effect possibly existing in the interference signals is reduced. Aiming at the interference environment with subspace structuring, the two-step Gradient intelligent fusion detection method for the distance extension target can effectively inhibit interference signals with different intensities, and has good intelligent anti-interference performance. Aiming at the target signal guide vector mismatch condition, the distance extension target two-step Gradient intelligent fusion detection method can effectively detect mismatch signals and has stronger detection robustness on the mismatch signals.
A2, carrying out radar irradiation on a non-target range around a sea area to be detected to obtain auxiliary data Y of R reference distance units only containing pure sea clutter; sending the auxiliary data Y to a clutter covariance matrix maximum likelihood estimation module, deriving the clutter covariance matrix by using a complex Gaussian probability density function of Y, and setting zero, and obtaining clutter covariance matrix maximum likelihood estimation based on the auxiliary data according to (11)
Will->
Sending the target information to a target intelligent detector construction module under partial uniform clutter and interference
Carrying out two steps Gr of distance expansion target obtained in step A1and (3) the client detection statistic is used for replacing an unknown clutter covariance matrix, constructing detection statistic lambda of the target intelligent detection method under partial uniform clutter and interference according to a formula (15), and transmitting lambda to a detection judgment module.
Notably, in step A2, compared with the S-GLRT-PHE detector of the distance extension target, the algorithm of the method of the present invention has lower computational complexity and stronger detection robustness to the steering vector mismatch signal. In addition, the constructed target intelligent detection method under partial uniform clutter and interference has an expression in a closed form, compared with the existing distance extension target self-adaptive detection method, the performance balance of algorithm calculation complexity, detection performance and mismatch robustness is considered while the CFAR characteristic is maintained, and the self-adaptive detection capability of the multi-channel broadband radar on the sea surface weak and small targets and the mismatch targets in a complex electromagnetic environment is improved.
Step A3, setting a detection threshold T according to a preset false alarm probability: specifically, the false alarm probability is set as P fa According to the Monte Carlo method, the accumulated 100/P is used in the early stage fa And calculating a detection threshold T according to the actual measurement sea clutter data. Considering that the sea clutter acquisition difficulty is large, if the actual acquired pure sea clutter measured data quantity R is less than 100/P fa 100/P lacking fa R clutter data can be obtained by simulation by using a sea clutter simulation model, wherein model parameters are reasonably estimated and set according to the obtained pure sea clutter actual measurement data. Further, the detection statistic lambda is compared with a detection threshold T, if lambda is more than or equal to T, the existence of a distance expansion target of the current K distance units to be detected is judged, and the main data are not used as auxiliary data of other subsequent distance units to be detected; on the contrary if lambda<And T, judging that the current K distance units to be detected do not have a distance expansion target, and taking the main data as auxiliary data of other subsequent distance units to be detected.
The comparison result of the detector performance under the target guide vector matching environment is shown in fig. 2. The result shows that compared with the existing detectors of distance expansion targets S-GLRT-PHE, S-Rao-PHE and the like, the detector of the method has better detection performance in a matching environment.
Example 2:
referring to fig. 1 of the specification, the embodiment of example 2 is divided into the following steps:
b1, carrying out radar irradiation on a region to be detected by using a ground detection radar to obtain main data Z of K distance units to be detected; send main data Z to H 0 Maximum likelihood estimation solving module under assumption, H 1 Derivation module and H of probability density function under assumption 1 A maximum likelihood estimation solving module under the assumption; at H 0 In the assumed maximum likelihood estimation solving module, H is obtained according to the formulas (5) and (14) 0 Assuming maximum likelihood estimation of Q and γ
and />
At H 1 In the derivative module of the probability density function under the assumption, H is obtained according to the formula (4) 1 Supposing that complex Gaussian probability density function of main data Z is opposite to target parameter vector Θ r Is a result of the derivation of (1); at H 1 In the assumed maximum likelihood estimation solving module, H is obtained according to the formula (8) 1 Supposing that theta r Maximum likelihood estimation of (2)
Will be described above in H 0 Maximum likelihood estimation solving module under assumption, H 1 Derivation module and H of probability density function under assumption 1 And (3) sending a result obtained by the maximum likelihood estimation solving module under the assumption to a two-step Gradient detection statistic constructing module under the condition of a known covariance matrix, constructing a distance extension target two-step Gradient detection statistic under the condition of the known clutter covariance matrix according to a formula (9), and sending the distance extension target two-step Gradient detection statistic to a target intelligent detector constructing module under the condition of partial uniform clutter and interference.
It should be noted that in step B1, the ground clutter component is modeled by using complex gaussian distribution, and meanwhile, the external interference may exist in the actual ground environment to adversely affect the adaptive detection of the distance expansion target, so that the external interference is also considered in the design process of the detector, and subspace signals are adopted to model the interference, so as to reduce the mismatch effect possibly existing in the interference signals. Aiming at the interference environment with subspace structuring, the two-step Gradient intelligent fusion detection method for the distance extension target can effectively inhibit interference signals with different intensities, and has good intelligent anti-interference performance. Aiming at the target signal guide vector mismatch condition, the distance extension target two-step Gradient intelligent fusion detection method can effectively detect mismatch signals and has stronger detection robustness on the mismatch signals.
B2, carrying out radar irradiation on a non-target range around a region to be detected to obtain auxiliary data Y of R reference distance units only containing pure ground clutter; sending the auxiliary data Y to a clutter covariance matrix maximum likelihood estimation module, deriving the clutter covariance matrix by using a complex Gaussian probability density function of Y, and setting zero, and obtaining clutter covariance matrix maximum likelihood estimation based on the auxiliary data according to (11)
Will->
Sending the target information to a target intelligent detector construction module under partial uniform clutter and interference
And B1, substituting unknown clutter covariance matrix in the two-step Gradient detection statistics of the distance expanded target obtained in the step B1, constructing detection statistics lambda of the target intelligent detection method under partial uniform clutter and interference according to a formula (15), and sending lambda to a detection judgment module.
Notably, in step B2, compared with the S-GLRT-PHE detector of the distance extension target, the algorithm of the method of the present invention has lower computational complexity and stronger detection robustness to the steering vector mismatch signal. In addition, the constructed target intelligent detection method under partial uniform clutter and interference has an expression in a closed form, compared with the existing distance extension target self-adaptive detection method, the performance balance of algorithm calculation complexity, detection performance and mismatch robustness is considered while the CFAR characteristic is maintained, and the self-adaptive detection capability of the multichannel broadband radar on the ground weak target and the mismatch target in a complex electromagnetic environment is improved.
Step B3, setting a detection threshold T according to a preset false alarm probability: specifically, the false alarm probability is set as P fa According to the Monte Carlo method, the accumulated 100/P is used in the early stage fa And calculating a detection threshold T according to the measured ground clutter data. Further, the detection statistic lambda is compared with a detection threshold T, if lambda is more than or equal to T, the existence of a distance expansion target of the current K distance units to be detected is judged, and the main data are not used as auxiliary data of other subsequent distance units to be detected; on the contrary if lambda<And T, judging that the current K distance units to be detected do not have a distance expansion target, and taking the main data as auxiliary data of other subsequent distance units to be detected.
The comparison result of the detector performance under the target-oriented vector mismatch environment is shown in fig. 3. The result shows that compared with the existing detectors of distance expansion targets S-GLRT-PHE, S-Rao-PHE and the like, the detector of the method has better detection robustness in a mismatch environment.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (6)
1. The intelligent target detection method under the condition of partial uniform clutter and interference is characterized by comprising the following steps:
step 1, main data Z are obtained from K distance units to be detected; under the condition that a clutter covariance matrix M, a target coordinate matrix P, an interference coordinate matrix Q and a clutter power factor gamma are unknown, a complex Gaussian probability density function of main data Z under a target hypothesis is utilized to calculate a bias guide for a target parameter vector, and a distance extension target two-step Gradient detection statistic under the conditions of the known clutter covariance matrix M and the clutter power factor gamma is constructed by combining the maximum likelihood estimation of the unknown target coordinate matrix P under the target hypothesis and the maximum likelihood estimation of the unknown interference coordinate matrix P under the non-target hypothesis;
step 2, acquiring auxiliary data Y from R reference distance units adjacent to the distance unit to be detected, obtaining clutter covariance matrix Mmax likelihood estimation based on the auxiliary data Y, bringing the clutter covariance matrix Mmax likelihood estimation into the two-step Gradient detection statistic of the distance expansion target obtained in the step 1, replacing the unknown clutter covariance matrix, and constructing the two-step Gradient detection statistic of the distance expansion target under the condition of the known clutter covariance matrix;
step 3, obtaining maximum likelihood estimation of unknown clutter power factor gamma under the non-target hypothesis by solving a unique positive value solution of a eigenvalue equation under the non-target hypothesis, and bringing the maximum likelihood estimation of the clutter power factor gamma into the distance extended target two-step Gradient detection statistic obtained in the step 2 to replace the unknown clutter power factor gamma, so as to construct detection statistic lambda of the target intelligent detection method under partial uniform clutter and interference;
step 4, setting a detection threshold T according to the preset false alarm probability; comparing the detection statistic lambda with a detection threshold T, if lambda is more than or equal to T, judging that the current distance unit to be detected has a distance expansion target, wherein the main data is not used as auxiliary data of other subsequent distance units to be detected; otherwise, if lambda is less than T, judging that the distance unit to be detected does not have a distance expansion target, and taking the main data as auxiliary data of other subsequent distance units to be detected.
2. The method for intelligent detection of a target under partial uniform clutter addition interference according to claim 1, wherein when the clutter covariance matrix M and the clutter power factor γ are known in the step 1, the two-step Gradient detection statistics of the target intelligent detector under partial uniform clutter addition interference are:
wherein ,
in the formula, the main data z= [ Z ] 1 ,z 2 ,...,z K ]The NxK-dimensional complex matrix is represented by the Nx1-dimensional received complex signal in the t th distance unit to be detected as z t ＝s t +j t +c t (t=1, 2,., K), where N x 1-dimensional target complex signal vector s t And an N x 1-dimensional interference complex vector j t Are all assumed to be deterministic and are denoted s, respectively t ＝Ηp t and jt ＝Jq t H and J are respectively known column-full rank N x p-dimensional target signal subspace complex matrix and N x q-dimensional interference signal subspace complex matrix, p x 1-dimensional complex vector p t And a qx1-dimensional complex vector q t Unknown complex coordinate vectors representing the target signal and the interfering signal, respectively; subspaces h and J are linearly independent, constructing an nxx (p+q) dimensional column-full rank augmentation matrix b= [ H J ]]And p+q is less than or equal to N; superscript (·) T and (·)H Respectively representing a transpose and a conjugate transpose, wherein |and| represent determinant of the square matrix, and tr represents trace of the square matrix; i m Representing an m x m dimensional identity matrix.
3. The method according to claim 2, wherein in the step 2, the complex gaussian probability density function of the auxiliary data Y is used to derive and zero the clutter covariance matrix M, i.e. the corresponding derivative is zero, and the maximum likelihood estimate of M based on the auxiliary data is obtained as the sample covariance matrix S
Wherein the auxiliary data y= [ Y ] 1 ,y 2 ,...,y R ]Expressed as an NxR-dimensional complex matrix, R observation data y are obtained from R reference distance units adjacent to the distance unit to be detected t (t=1, 2., (v), R), assuming y t (t=1, 2,.,. R) contains only pure clutter components, with the n×1-dimensional complex vector y of the t-th reference distance cell t (t=1, 2,) R satisfies
Which are also independently and identically distributed among the different distance units.
4. The method for intelligently detecting a target under partial uniform clutter and interference according to claim 3, wherein in the step 2, the maximum likelihood estimation of a clutter covariance matrix mmax is carried into the two-step Gradient detection statistic of the distance expansion target obtained in the step 1, the unknown clutter covariance matrix is replaced, and the two-step Gradient detection statistic of the distance expansion target under the condition of the known clutter covariance matrix is constructed:
wherein ,
5. the method for intelligent detection of a target under partial uniform clutter and interference according to claim 4, wherein the eigenvalue equation constructed in the step 3 is:
wherein the method comprises the steps ofV represents an unknown parameter, lambda k,0 Representation matrix
Is the kth non-zero eigenvalue of (c).
6. The method for intelligent detection of target under partial uniform clutter and interference according to claim 5, wherein in step 3, the maximum likelihood estimation of clutter power factor γ is performed
And (3) substituting unknown clutter power factors gamma in the two-step Gradient detection statistics of the distance-expanded target obtained in the step (2) to construct detection statistics lambda of the target intelligent detection method under partial uniform clutter and interference:
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