CN116299387B - Target intelligent detection method for interference orthogonal suppression under heterogeneous clutter - Google Patents
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Abstract
The invention discloses an intelligent target detection method for interference quadrature suppression under heterogeneous clutter, and belongs to the field of broadband radar signal processing. 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. Under the condition that external interference and uniform auxiliary data quantity are less, aiming at the difficult problem that the detection performance and mismatch robustness are difficult to be considered in the detection of the multi-channel broadband radar range-extended target, clutter covariance matrix oblique symmetry structure information is fully excavated, the requirement on the auxiliary data quantity is reduced, the estimation precision of the unknown clutter covariance matrix is improved, and powerful support is provided for realizing target self-adaptive detection under the condition of small samples in which interference exists.
Description
Technical Field
The invention belongs to the technical field of broadband radar signal processing, and particularly relates to an intelligent target detection method for interference quadrature suppression under heterogeneous clutter.
Background
With the increase of radar bandwidth, the distance resolution is further improved, and the broadband radar is widely applied to the modern military and civil fields of anti-interference, anti-reconnaissance, accurate detection and imaging, high-precision tracking, target recognition and the like, and the self-adaptive detection of the target around the distance extension of the broadband radar has become one of the hot problems in the radar field. Unlike narrowband radar target echo signals, which typically occupy only one range-resolved element, wideband radar target scattering point energy may spread to neighboring range elements, presenting a "one-dimensional range profile," forming a range-extended target. If the point target detection method is still adopted, carrying out target detection on the echo signals aiming at a single distance unit, and carrying out background clutter statistical characteristic estimation by utilizing adjacent distance unit sampling; on one hand, the energy of the strong scattering point of the distance expansion target is easy to leak to the adjacent distance units to cause signal pollution, and a shielding effect is further formed on the target signal of the single distance unit to be detected, so that the point target detection method is poor in effect; on the other hand, in practical application, radar detection faces a complex electromagnetic environment, natural or artificial interference sources such as electronic countermeasure signals or various civil electromagnetic signals may exist, and in addition, the environment where a target is located is complex and changeable, so that background clutter non-uniformity is enhanced, the quantity of pure clutter auxiliary data meeting independent and same distribution is limited, and compared with a narrow-band radar, the problem is particularly prominent in a broadband radar target detection scene, so that an ideal detection effect is difficult to obtain by the existing range expansion target detection method. 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 a subspace modeling is adopted for the target and the interference signal based on an integral data set formed by main data and auxiliary data of a plurality of distance units to be detected, and detection statistics are constructed by utilizing a Rao detection criterion, a distance expansion target subspace Rao detector (S-Rao-PHE) for interference orthogonal suppression under non-uniform clutter can be obtained; if the GLRT test criterion is used to construct the detection statistic, a distance-extended target subspace GLRT detector (S-GLRT-PHE) for interference quadrature suppression under non-uniform clutter can be obtained. Under the condition that the auxiliary data amount is sufficient, the two detectors can obtain certain detection robustness, but the auxiliary data amount in the actual application scene is often limited, and the detectors are difficult to effectively function. Considering that it is difficult to obtain enough pure clutter assistance data in a practical environment, a special oblique symmetry structure exists for a clutter covariance matrix of a radar receiver using a central symmetry linear array or a central symmetry interval pulse train. The detection performance of the detector can be improved by utilizing the information of the oblique symmetrical structure, and the requirement for auxiliary data quantity can be reduced.
Under the condition that external interference and uniform auxiliary data quantity are less, aiming at the difficult problem that the detection performance and mismatch robustness are difficult to be considered in the detection of the multi-channel broadband radar distance expansion target, how to fully utilize the information of the oblique symmetry structure, reduce the actual demand on the auxiliary data quantity, improve the estimation precision of an unknown clutter covariance matrix, further construct the intelligent detection method of the distance expansion target with a closed form, effectively inhibit interference signals while keeping the characteristic of Constant False Alarm Rate (CFAR), and consider the effective balance between the mismatch robustness and the detection performance, thereby being the key for improving the detection capability of the broadband radar in the complex interference environment and being one of the difficult problems faced by the adaptive detection of the multi-channel broadband radar distance expansion target.
Disclosure of Invention
In order to overcome the problems in the prior art, the invention provides a target intelligent detection method for interference quadrature suppression under non-uniform clutter.
The technical scheme for solving the technical problems is as follows:
an intelligent target detection method for interference quadrature suppression under non-uniform clutter comprises the following steps:
Step 1, main data Z are obtained from K distance units to be detected, and R auxiliary data are obtained from R reference distance units adjacent to the distance units to be detected; in the clutter covariance matrix M, the target coordinate matrix P, the interference coordinate matrix Q and the clutter power factor Under the condition of unknown, utilizing the oblique symmetry characteristic of the clutter covariance matrix to perform unitary transformation on the main data Z, the clutter covariance matrix M, the target coordinate matrix P and the interference coordinate matrix Q;
step2, transforming the main data by using the non-target assumption downskew symmetry And complex Gaussian joint probability density function pair clutter covariance matrix/>, of auxiliary data YSolving the maximum likelihood estimation of the clutter covariance matrix M and the interference coordinate matrix Q under the condition of no target assumption; transforming main data/>, using targeted supposition downskew symmetryAnd complex Gaussian joint probability density function pair clutter covariance matrix/>, of auxiliary data YSolving bias guide of the target signal and interference joint coordinate matrix D, and solving maximum likelihood estimation of the clutter covariance matrix M and the target signal and interference joint coordinate matrix D under the condition of target assumption; combining clutter power factors/>Constructing a distance extended target GLRT detection statistic by maximum likelihood estimation under the assumption of a target and the assumption of no target;
Step 3, setting a detection threshold T according to the preset false alarm probability; will detect statistics Compare with detection threshold T, if/>Judging that the current K distance units to be detected have distance expansion targets; otherwise/>And judging that the current K distance units to be detected have no distance expansion targets.
Further, in the step 1, the unitary transformation of the main data Z is performed to obtain the oblique symmetric transformation main data：
Wherein,
In the formula, the main data are expressed asVitamin complex matrix/>Under the assumption of targets, the t distance unit to be detected is/>The dimensional received complex signal is denoted/>Wherein/>Dimension target complex signal vector/>And/>Wiener complex vector/>Are all assumed to be deterministic and are denoted/>, respectivelyAnd/>，And/>Respectively, known column full rank/>Vitamin target signal subspace complex matrix sum/>Vitamin interference signal subspace complex matrix,/>Vector of dimension p t and/>The dimension complex vector q t represents the unknown complex coordinate vectors of the target signal and the interference signal, respectively; t is in the distance unit to be detected/>Wiry clutter vector/>Is a zero-mean complex circular Gaussian vector expressed as，/>And clutter vectors between different distance units are independently and uniformly distributed, wherein/>Clutter covariance matrix of dimension/>Is an unknown Hermitian positive definite complex matrix, and gamma is an unknown clutter power factor between main data and auxiliary data; d N is a diagonally symmetric matrix.
Further, in the step 2, under the condition of no target hypothesis, the main data is transformed by utilizing the declined symmetry of the non-target hypothesisAnd complex Gaussian joint probability density function pair clutter covariance matrix/>, of auxiliary data YAnd solving the bias guide of the interference coordinate matrix Q, and solving the maximum likelihood estimation of the clutter covariance matrix M and the interference coordinate matrix Q under the condition of no target assumption:
wherein,
Wherein the auxiliary data is expressed asVitamin complex matrix/>Let/>Contains only pure clutter components, wherein the t-th reference distance cell/>Complex vector/>Satisfy the following requirementsWhich are also independently and identically distributed among the different distance units; q p is an unknown coordinate matrix of an interference subspaceIs a unitary transformation of (a).
Further, the step 2 further comprises the step of, under the condition of no target hypothesis,Maximum likelihood estimation/>Is the only positive solution to satisfy the equation of the eigenvalue:
wherein, Is an unknown, s=min (N, K),/>Is/>Is the kth non-zero feature root of (c),Wherein/>Representative/>And (5) a dimensional identity matrix.
Further, in the step 2, under the targeted assumption, the main data is transformed by using the targeted assumption to be obliquely symmetricalAnd complex Gaussian joint probability density function pair clutter covariance matrix/>, of auxiliary data YSolving bias guide of the target signal and interference joint coordinate matrix D, and solving maximum likelihood estimation of the clutter covariance matrix M and the target signal and interference joint coordinate matrix D under the condition of target assumption:
wherein,
In the method, in the process of the invention,Violet full rank augmentation matrix/>Subspace/>And/>Is linearly independent and satisfies/>; D P is a unitary transformation of D,/>Wherein the target subspace is unknown in the coordinate matrix。
Further, under the targeted assumption in the step 2,Maximum likelihood estimation/>Is the only positive solution that satisfies the eigenvalue equation:
wherein, Is/>Is the kth non-zero feature root,/>。
Further, the clutter power factor is combined in the step 2Maximum likelihood estimation under the assumption of a target and under the assumption of no target, and constructing a distance extended target GLRT detection statistic:
wherein, Representing the determinant of the square matrix.
Compared with the prior art, the invention has the following technical effects:
1) The intelligent target detector for interference orthogonal suppression under the heterogeneous clutter is constructed, and the detector has an expression in a closed form and does not need iterative operation;
2) The clutter covariance matrix oblique symmetry information is fully utilized, the estimation accuracy of the unknown clutter covariance matrix is improved, the requirement for auxiliary data quantity is reduced, and a powerful support is provided for realizing the self-adaptive detection of a distance expansion target under the condition of small samples with interference;
3) Aiming at the interference environment with subspace structuring, the intelligent fusion detection method for the radar target under subspace interference can effectively inhibit interference signals with different intensities, and has good intelligent anti-interference performance;
4) Aiming at the situation of target signal steering vector mismatch, the target intelligent detection method for interference quadrature suppression under heterogeneous clutter can effectively detect a mismatch signal, and has stronger detection robustness on the mismatch signal;
5) The detection method disclosed by the invention has the advantages that the performance balance of detection performance and mismatch robustness is considered while the CFAR characteristic is maintained, and 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 is improved;
6) 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 target intelligent detection method for interference quadrature suppression under non-uniform clutter in accordance with 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=12, k=15, r=12, p=2, q=2, false alarm probability P fa=10-3, interference clutter power ratio icr=15 dB;
In fig. 3, n=12, k=15, r=12, p=2, 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 problems that the existing broadband radar distance expansion target self-adaptive detector under the partial uniform Gaussian background is difficult to consider CFAR characteristics, detection performance and mismatch robustness, meanwhile, the problem that pure clutter auxiliary data is difficult to obtain due to actual clutter non-uniformity is solved, how to fully mine clutter covariance matrix structure information, further reduce the requirement on auxiliary data amount, improve estimation accuracy of unknown clutter covariance matrix, further construct a target intelligent detection method with closed-form heterogeneous clutter interference quadrature suppression, ensure CFAR characteristics, and simultaneously consider the intelligent anti-interference, mismatch robustness, detection performance and other multi-aspect requirements of a distance expansion target self-adaptive detection algorithm, and improve self-adaptive detection performance of the multichannel broadband radar on weak and small targets and mismatch targets under complex interference environments.
The invention discloses a target intelligent detection method for interference quadrature suppression under heterogeneous clutter, which comprises the following steps:
Step 1, main data Z are obtained from K distance units to be detected, and R auxiliary data are obtained from R reference distance units adjacent to the distance units to be detected; in the clutter covariance matrix M, the target coordinate matrix P, the interference coordinate matrix Q and the clutter power factor Under the condition of unknown, utilizing the oblique symmetry characteristic of the clutter covariance matrix to perform unitary transformation on the main data Z, the clutter covariance matrix M, the target coordinate matrix P and the interference coordinate matrix Q;
The method comprises the following specific steps:
For a coherent radar system with the space-time joint channel number of N, considering the binary hypothesis test problem of H 0 and H 1, wherein the H 0 assumes that the target does not exist and only clutter and interference exist; h 1 assumes that the lower target, clutter and interference are all present.
Assuming that the target may occupy K consecutive distance cells to be detected, under assumption H 1, the t th distance cell to be detectedThe dimensional received complex signal is denoted/>Wherein/>Dimension target complex signal vectorAnd/>Wiener complex vector/>Are assumed to be deterministic and can be expressed as/>, respectivelyAnd/>，/>And/>Respectively, known column full rank/>Vitamin target signal subspace complex matrix sum/>The complex matrix of the subspace of the interference signal is maintained,Vector of dimension p t and/>The dimension complex vector q t represents the unknown complex coordinate vector of the target signal and the interference signal, respectively, and the main data can be expressed as/>Vitamin complex matrix/>. Note subspace/>And/>Is linearly independent, constructViolet full rank augmentation matrix/>And meet/>. T is in the distance unit to be detected/>Wiry clutter vector/>Is a zero-mean complex circular Gaussian vector, expressed as/>，/>And clutter vectors between different distance units are independently and uniformly distributed, wherein/>Clutter covariance matrix of dimension/>Is an unknown Hermitian positive definite complex matrix, and gamma is an unknown clutter power factor between main data and auxiliary data.
In addition, R observation data are acquired from R reference distance units adjacent to the distance unit to be detectedLet/>Including only pure clutter components, the assistance data may be expressed as/>Vitamin complex matrix/>Wherein t is the/>, of the reference distance cellComplex vector/>Satisfy the following requirementsWhich are also independently and identically distributed among the different distance units.
The target intelligent detector for interference orthogonal suppression under the heterogeneous clutter is constructed based on the GLRT test criterion, and the distance expansion target GLRT test statistic can be expressed as follows:
(1)
wherein, And/>Representing the detection statistic and the threshold value respectively; unknown coordinate matrix of target subspaceInterference subspace unknown coordinate matrix/>；And/>Representing the main data/>, under assumptions H 0 and H 1, respectivelyAnd auxiliary data/>Is a complex gaussian joint Probability Density Function (PDF) and can be expressed as:
(2)
(3)
wherein, Representing the determinant of the square matrix, the tr function representing the trace taking the square matrix, and
(4)
Wherein, the sample covariance matrix S=YY H,; Superscript/>And/>Representing the transpose and conjugate transpose, respectively.
By using the oblique symmetry structure in the covariance matrix M, it can be deduced
(5)
Thus (2) and (3) can be rewritten as respectively
(6)
(7)
Wherein the method comprises the steps of
(8)
(9)
(10)
(11)
(12)
(13)
(14)
Step 2. Down-converted main data using non-target hypothesisAnd complex Gaussian joint probability density function pair clutter covariance matrix/>, of auxiliary data YSolving the maximum likelihood estimation of the clutter covariance matrix M and the interference coordinate matrix Q under the condition of no target assumption; using main data after targeted hypothesis down-conversion/>And complex Gaussian joint probability density function pair clutter covariance matrix/>, of auxiliary data YSolving the bias guide of the target signal and interference joint coordinate matrix D, and solving the maximum likelihood estimation of the clutter covariance matrix M and the target signal and interference joint coordinate matrix D under the condition of target assumption; combining clutter power factors/>Constructing a distance extended target GLRT detection statistic by maximum likelihood estimation under the assumption of a target and the assumption of no target;
The method comprises the following specific steps:
by targeting main data under no-target assumption And the complex Gaussian joint probability density function of the auxiliary data Y performs partial derivation on the clutter covariance matrix M, namely (6) performs partial derivation on the clutter covariance matrix M, and the result of the partial derivation is set to zero, so that a given interference coordinate matrix Q and/>, under the assumption of H 0, can be obtainedThe maximum likelihood estimation of the time clutter covariance matrix M is
(15)
Then substituting the formula (15) into the formula (6) to obtain
(16)
Wherein,Representative/>And (5) a dimensional identity matrix. The interference coordinate matrix Q is subjected to partial derivation by utilizing the method (16), and the derivation result is set to be zero, so that the maximum likelihood estimation of the Q p under the assumption of H 0 can be obtained
(17)
Wherein,. Then substituting (17) into (6) to obtain
(18)
Then, transforming the main data by targeted hypothesis downward skew symmetryAnd the complex Gaussian joint probability density function of the auxiliary data Y performs partial derivation on the clutter covariance matrix M, namely, the formula (7) performs partial derivation on the clutter covariance matrix M, and the result of the partial derivation is set to zero, so that a given target signal and interference joint coordinate matrix D and/>, under the assumption of H 1, can be obtainedMaximum likelihood estimation of clutter covariance matrix M of (2) is
(19)
Then, the formula (19) is substituted into the formula (7) to obtain
(20)
The (20) is utilized to calculate the bias derivative of the target signal and the interference joint coordinate matrix D, and the result of the derivation is set to zero, so that the maximum likelihood estimation of the target signal and the interference joint coordinate matrix D under the assumption of H 1 can be obtained
(21)
Wherein,. Substituting (21) into (7) to obtain
(21)
Wherein,. Then substituting (18) and (21) into (1), and performing algebraic operation for multiple times to obtain the power factor/>, which is known in clutterThe GLRT test intermediate statistics in the case are
(22)
Assume thatAnd/>Represents the ML estimate of γ under assumptions H 0 and H 1, respectively. Under assumption H 0,/>Maximum likelihood estimation of (2)Is the only positive solution that satisfies the eigenvalue equation of equation (23):
(23)
wherein, Is an unknown, s=min (N, K),/>Is/>Is the kth non-zero feature root of (c).
Under the assumption that H 1 is present,Maximum likelihood estimation/>Is the only positive solution to satisfy the eigenvalue equation of equation (24):(24)
wherein, Is/>Is the kth non-zero feature root of (c).
Will beAnd/>Replace/>, respectively in the (25) numerator and denominatorThe detection statistics of the fusion detector capable of obtaining intelligent interference suppression under the non-uniform background are as follows:
(25)
The method constructs the target intelligent detector for interference orthogonal suppression under the heterogeneous clutter. As can be seen from equation (25), the proposed target smart detector for non-uniform clutter interference quadrature suppression has a closed form of detection statistic expression without iterative operation. In addition, compared with detectors such as S-GLRT-PHE and S-Rao-PHE of a distance extension target, the intelligent target detector for interference quadrature suppression under the heterogeneous clutter has better detection performance in a matching environment and stronger detection robustness on a guide vector mismatch signal. In a comprehensive view, the target intelligent detector for interference quadrature suppression under heterogeneous clutter can effectively consider the reasonable balance between detection performance and mismatch robustness while maintaining CFAR characteristics.
Step 3, setting a detection threshold T according to preset false alarm probability in order to maintain the CFAR characteristic of the detection method; will detect statisticsCompare with detection threshold T, if/>Judging that the current K distance units to be detected have distance expansion targets; otherwise/>And judging that the current K distance units to be detected have no distance expansion targets.
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; and carrying out radar irradiation on the non-target range around the sea area to be detected to obtain auxiliary data Y of R reference distance units only containing pure sea clutter. The main data Z and the auxiliary data Y are sent to a data conversion module; in the data conversion module, the oblique symmetry conversion main data is obtained according to the formula (9) ; Transform the oblique symmetry into main data/>The auxiliary data Y is sent to a maximum likelihood estimation solving module under the assumption of H 0 and a maximum likelihood estimation solving module under the assumption of H 1; in the maximum likelihood estimation solving module under the assumption of H 0, M, Q and/>, under the assumption of H 0, are obtained according to the formulas (15), (17) and (23), respectivelyMaximum likelihood estimation/>、/>And; In the maximum likelihood estimation solving module under the assumption of H 1, M, D and/>, under the assumption of H 1, are obtained according to the equation (19), the equation (21) and the equation (24), respectivelyMaximum likelihood estimation/>、/>And/>。
Notably, in the step A1, the subspace distance extension target signal model constructed by the method can effectively solve the problem that the rank-one signal model is difficult to process target steering vector mismatch, and the robustness of the broadband radar to the offshore target steering vector mismatch condition is improved; meanwhile, the fact that external interference possibly exists in an actual marine environment is considered to have adverse effect on adaptive detection of a 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 interference, so that mismatch influence possibly existing in an interference signal is reduced. Aiming at the interference environment with subspace structuring, the intelligent detection method of the distance extension target GLRT target can effectively inhibit interference signals with different intensities, and has good intelligent anti-interference performance; meanwhile, the detector of the method can be suitable for partial uniform and other non-uniform sea clutter environments, and the intelligent adaptability of the detector to the non-uniform sea clutter environments is improved by fully excavating the partial uniformity of the sea clutter and utilizing the maximum likelihood estimation of the clutter power factor under the non-target hypothesis.
A2, sending the results obtained by the maximum likelihood estimation solving module under the assumption of H 0 and the maximum likelihood estimation solving module under the assumption of H 1 to a target intelligent detector constructing module for interference orthogonal suppression under non-uniform clutter, and constructing the detection statistic of the target intelligent detection method for interference orthogonal suppression under non-uniform clutter according to the method (25)And will/>And sending the detection result to a detection judgment module.
It is noted that in step A2, compared with the detectors such as S-GLRT-PHE, S-Rao-PHE, etc. of the distance extension target, the method of the present invention has better detection performance in the matching environment and has stronger detection robustness to the guide vector mismatch signal. In addition, the constructed target intelligent detection method for interference orthogonal suppression under the heterogeneous clutter has an expression in a closed form, compared with the existing distance extension target self-adaptive detection method, the performance balance of 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 small target and the mismatch target in the 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 asAccording to the Monte Carlo method, a detection threshold T is calculated according to 100/P fa actually measured sea clutter data accumulated in the earlier stage. Considering that the sea clutter acquisition difficulty is high, if the actually obtained pure sea clutter measured data quantity R is less than 100/P fa, the missing 100/P fa -R clutter data can be obtained by simulating a sea clutter simulation model, and model parameters are reasonably estimated and set according to the obtained pure sea clutter measured data. Further, detection statistics/>Compare with detection threshold T, if/>Judging that the current K distance units to be detected have distance expansion targets, wherein the main data are not used as auxiliary data of other subsequent distance units to be detected; otherwise/>And 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; and carrying out radar irradiation on the non-target range around the region to be detected to obtain auxiliary data Y of R reference distance units only containing pure ground clutter. The main data Z and the auxiliary data Y are sent to a data conversion module; in the data conversion module, the oblique symmetry conversion main data is obtained according to the formula (9) ; Transform the oblique symmetry into main data/>The auxiliary data Y is sent to a maximum likelihood estimation solving module under the assumption of H 0 and a maximum likelihood estimation solving module under the assumption of H 1; in the maximum likelihood estimation solving module under the assumption of H 0, M, Q and/>, under the assumption of H 0, are obtained according to the formulas (15), (17) and (23), respectivelyMaximum likelihood estimation/>、/>And; In the maximum likelihood estimation solving module under the H1 assumption, the H 1 assumption M, D and/>, respectively, are obtained according to the formula (19), the formula (21) and the formula (24)Maximum likelihood estimation/>、/>And/>。
Notably, in the step B1, the subspace distance extension target signal model constructed by the method can effectively solve the problem that the rank-one signal model is difficult to process target steering vector mismatch, and the robustness of the broadband radar to the situation of target steering vector mismatch on the ground is improved; meanwhile, the fact that external interference possibly exists in an actual ground environment can be considered to have adverse effects on the self-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 that mismatch effects possibly existing in the interference signals are reduced. Aiming at the interference environment with subspace structuring, the target intelligent detection method for interference orthogonal suppression under heterogeneous clutter can effectively suppress interference signals with different intensities, and has good intelligent anti-interference performance; meanwhile, the detector of the method can be suitable for partial uniform and other non-uniform ground clutter environments, and the intelligent adaptability of the detector to the non-uniform ground clutter environments is improved by fully excavating the local uniformity of the ground clutter and utilizing the maximum likelihood estimation of the clutter power factor under the non-target hypothesis.
B2, sending the results obtained by the maximum likelihood estimation solving module under the assumption of H 0 and the maximum likelihood estimation solving module under the assumption of H 1 to a target intelligent detector constructing module for interference orthogonal suppression under non-uniform clutter, and constructing the detection statistic of the target intelligent detection method for interference orthogonal suppression under non-uniform clutter according to the method (25)And will/>And sending the detection result to a detection judgment module.
Notably, in the step B2, compared with detectors such as S-GLRT-PHE and S-Rao-PHE of a distance extension target, the method has better detection performance in a matching environment and stronger detection robustness on a guide vector mismatch signal. In addition, the constructed target intelligent detection method for interference orthogonal suppression under heterogeneous clutter has an expression in a closed form, compared with the existing distance extension target self-adaptive detection method, the performance balance of 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 to be P fa, and the detection threshold T is calculated according to 100/P fa actually measured ground clutter data accumulated in the earlier stage according to the Monte Carlo method. Further, the statistics are detectedCompare with detection threshold T, if/>Judging that the current K distance units to be detected have distance expansion targets, wherein the main data are not used as auxiliary data of other subsequent distance units to be detected; otherwise/>And 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 (7)
1. The intelligent target detection method for interference quadrature suppression under non-uniform clutter is characterized by comprising the following steps of:
Step 1, main data Z are obtained from K distance units to be detected, and R auxiliary data are obtained from R reference distance units adjacent to the distance units to be detected; in the clutter covariance matrix M, the target coordinate matrix P, the interference coordinate matrix Q and the clutter power factor Under the condition of unknown, utilizing the oblique symmetry characteristic of the clutter covariance matrix to perform unitary transformation on the main data Z, the clutter covariance matrix M, the target coordinate matrix P and the interference coordinate matrix Q;
step2, transforming the main data by using the non-target assumption downskew symmetry And complex Gaussian joint probability density function pair clutter covariance matrix/>, of auxiliary data YSolving the maximum likelihood estimation of the clutter covariance matrix M and the interference coordinate matrix Q under the condition of no target assumption; transforming main data/>, using targeted supposition downskew symmetryAnd complex Gaussian joint probability density function pair clutter covariance matrix/>, of auxiliary data YSolving bias guide of the target signal and interference joint coordinate matrix D, and solving maximum likelihood estimation of the clutter covariance matrix M and the target signal and interference joint coordinate matrix D under the condition of target assumption; combining clutter power factors/>Constructing a distance extended target GLRT detection statistic by maximum likelihood estimation under the assumption of a target and the assumption of no target;
Step 3, setting a detection threshold T according to the preset false alarm probability; will detect statistics Compare with detection threshold T, ifJudging that the current K distance units to be detected have distance expansion targets; otherwise/>And judging that the current K distance units to be detected have no distance expansion targets.
2. The method for intelligently detecting targets by interference quadrature suppression under heterogeneous clutter as claimed in claim 1, wherein said step 1 transforms the primary data Z unitary into a diagonally symmetric transformed primary data：
Wherein,
In the formula, the main data are expressed asVitamin complex matrix/>Under the assumption of targets, the t distance unit to be detected is/>The dimensional received complex signal is denoted/>Wherein/>Dimension target complex signal vector/>And/>Wiener complex vector/>Are all assumed to be deterministic and are denoted/>, respectivelyAnd/>，/>And/>Respectively, known column full rank/>Vitamin target signal subspace complex matrix sum/>The complex matrix of the subspace of the interference signal is maintained,Vector of dimension p t and/>The dimension complex vector q t represents the unknown complex coordinate vectors of the target signal and the interference signal, respectively; t is in the distance unit to be detected/>Wiry clutter vector/>Is a zero-mean complex circular Gaussian vector, expressed as/>，And clutter vectors between different distance units are independently and uniformly distributed, wherein/>Clutter covariance matrix of dimension/>Is an unknown Hermitian positive definite complex matrix, and gamma is an unknown clutter power factor between main data and auxiliary data; d N is a diagonally symmetric matrix.
3. The method for intelligently detecting targets by interference quadrature suppression under heterogeneous clutter according to claim 2, wherein in the step2, main data is transformed by utilizing target-free supposition downskew symmetry under the condition of no target suppositionAnd complex Gaussian joint probability density function pair clutter covariance matrix/>, of auxiliary data YAnd solving the bias guide of the interference coordinate matrix Q, and solving the maximum likelihood estimation of the clutter covariance matrix M and the interference coordinate matrix Q under the condition of no target assumption:
wherein,
Wherein the auxiliary data is expressed asVitamin complex matrix/>Let/>Contains only pure clutter components, wherein the t-th reference distance cell/>Complex vector/>Satisfy/>Which are also independently and identically distributed among the different distance units; q p is an unknown coordinate matrix of an interference subspaceIs a unitary transformation of (a).
4. The method for intelligent detection of an object with non-uniform clutter interference quadrature suppression according to claim 3, wherein the step 2 further comprises the steps of, in the absence of an object hypothesis,Maximum likelihood estimation/>Is the only positive solution to satisfy the equation of the eigenvalue:
wherein, Is an unknown, s=min (N, K),/>Is/>Is the kth non-zero feature root of (c),Wherein/>Representative/>And (5) a dimensional identity matrix.
5. The method for intelligent detection of target for non-uniform clutter interference quadrature suppression according to claim 2, wherein in said step2, under a target hypothesis, main data is transformed using target hypothesis downward-skew symmetryAnd complex Gaussian joint probability density function pair clutter covariance matrix/>, of auxiliary data YSolving bias guide of the target signal and interference joint coordinate matrix D, and solving maximum likelihood estimation of the clutter covariance matrix M and the target signal and interference joint coordinate matrix D under the condition of target assumption:
wherein,
In the method, in the process of the invention,Violet full rank augmentation matrix/>Subspace/>And/>Is linearly independent and meets; D P is a unitary transformation of D,/>Wherein the target subspace is unknown in the coordinate matrix。
6. The method for intelligent detection of an object with non-uniform clutter interference quadrature suppression according to claim 5, wherein in the step 2, under the assumption of the object,Maximum likelihood estimation/>Is the only positive solution that satisfies the eigenvalue equation:
wherein, Is/>Is the kth non-zero feature root,/>。
7. The method for intelligent detection of target for non-uniform clutter interference quadrature suppression according to claim 6, wherein the step 2 combines clutter power factorsMaximum likelihood estimation under the assumption of a target and under the assumption of no target, and constructing a distance extended target GLRT detection statistic:
wherein, Representing the determinant of the square matrix.
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