CN108627819A - Range extension target detection method and system based on radar observation - Google Patents
Range extension target detection method and system based on radar observation Download PDFInfo
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- 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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
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Abstract
本发明实施例提供一种基于雷达观测的距离扩展目标检测方法和系统，检测方法包括：基于观测协方差矩阵的显著特征值，获取信号子空间的正交基矩阵，其中，显著特征值根据雷达的所有距离单元的接收数据获得；基于信号子空间的正交基矩阵和目标子空间的正交基矩阵，建立二元假设检验模型；根据二元假设检验模型，建立基于体积相关函数的检验器，基于检验器的检验统计量判断是否存在距离扩展目标。本发明从几何视角出发，建立基于体积相关函数的检验器，无需辅助数据，无需对杂波协方差矩阵进行估计，对检测环境先验信息的依赖性更低，运算量和计算复杂度显著降低。本发明适用于均匀、非均匀环境。本发明也适用于点目标检测场景，具有更强的普适性。
Embodiments of the present invention provide a method and system for detecting a range-extended target based on radar observations. The detection method includes: obtaining an orthogonal basis matrix of the signal subspace based on the salient eigenvalues of the observation covariance matrix, wherein the salient eigenvalues are based on the radar The received data of all the distance units are obtained; based on the orthogonal basis matrix of the signal subspace and the orthogonal basis matrix of the target subspace, a binary hypothesis testing model is established; according to the binary hypothesis testing model, a tester based on a volume correlation function is established , based on the test statistic of the tester to determine whether there is a distance extension target. Starting from a geometric perspective, the present invention establishes a tester based on a volume correlation function, which does not require auxiliary data, does not need to estimate the clutter covariance matrix, has lower dependence on the prior information of the detection environment, and significantly reduces the amount of calculation and computational complexity . The invention is suitable for uniform and non-uniform environment. The present invention is also applicable to point target detection scenarios, and has stronger universality.
Description
Technical Field
The embodiment of the invention relates to the technical field of radar signal processing, in particular to a method and a system for detecting a distance extension target based on radar observation.
Background
As a detection means, radar needs to have multi-dimensional high resolution to obtain more detailed information of a detected object. As radar range resolution increases, the scattering points of the target are distributed across multiple range resolution cells. Such targets, which are larger in size than the radar range resolution unit, are commonly referred to as range extension targets.
In the existing research methods for expanding the target detection problem, the methods are mostly based on the statistical hypothesis testing theory. These methods start from the statistical description of the Likelihood function, and require that the echo statistical distribution parameters are known or need to be estimated, such as the Generalized Likelihood Ratio Test (GLRT), Rao Test, Wald Test, etc. which are widely used, and these Test methods all need to estimate the target echo complex amplitude and clutter covariance matrix. To complete the parameter estimation of the detector, these detection methods usually need to use enough auxiliary data with the same statistical characteristics as the unit to be detected and without the target echo, and the parameter estimation process is complicated. However, in increasingly complex electromagnetic environments, particularly hostile environments, prior information about clutter statistical distribution which can be acquired is extremely limited, and at this time, it is often difficult to obtain auxiliary data meeting requirements, which causes large deviation in parameter estimation, and the existing extended target detection methods all have different degrees or even large-scale detection performance degradation. One prior art proposes the concept of a "volume correlation function" as a measure of the distance between subspaces and applies it to point target detection, resulting in better performance. However, the detection performance of the method on the extended target is not verified; the detection process of the method needs to be finished iteratively, the volume correlation function needs to be calculated every iteration, and the calculation complexity is high; meanwhile, the method only considers the uniform environment, and the detection in the non-uniform environment is not analyzed.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides a method and a system for detecting a distance extension target based on radar observation.
The embodiment of the invention provides a distance extension target detection method based on radar observation, which comprises the following steps: acquiring an orthogonal basis matrix of a signal subspace based on a significant eigenvalue of an observation covariance matrix, wherein the significant eigenvalue is obtained according to received data of all range units of the radar; establishing a binary hypothesis test model based on the orthogonal basis matrix of the signal subspace and the orthogonal basis matrix of the target subspace; and establishing a checker based on a volume correlation function according to the binary hypothesis test model, acquiring test statistic of the checker, and judging whether a distance expansion target exists or not based on the test statistic.
The embodiment of the invention provides a radar observation-based distance extension target detection system, which comprises: the acquisition matrix module is used for acquiring an orthogonal basis matrix of a signal subspace based on a significant eigenvalue of an observation covariance matrix, wherein the significant eigenvalue is acquired according to received data of all range units of the radar; the acquisition model module is used for establishing a binary hypothesis test model based on the orthogonal basis matrix of the signal subspace and the orthogonal basis matrix of the target subspace; and the detection module is used for establishing a checker based on a volume correlation function according to the binary hypothesis test model, acquiring the test statistic of the checker and judging whether a distance expansion target exists or not based on the test statistic.
The embodiment of the invention provides a distance extension target detection device based on radar observation, which comprises: at least one processor; and at least one memory communicatively coupled to the processor, wherein: the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the detection method.
Embodiments of the present invention provide a non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the above-described detection method.
According to the method and the system for detecting the range extension target based on radar observation, provided by the embodiment of the invention, the subspace is used as a basic unit and a processing object of signal representation, the detection is completed by utilizing the inherent geometric relation between the target subspace and the clutter subspace, and the checker based on the volume correlation function is established from the geometric perspective. Compared with the target detection method based on the volume correlation function provided by the prior art, the method provided by the embodiment of the invention does not need an iteration process, and is suitable for uniform and non-uniform environments. In addition, when the corresponding parameters in the algorithm select appropriate values, the distance extension target detection method provided by the embodiment of the invention is also suitable for a point target detection scene, and has stronger universality.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a flowchart of an embodiment of a method for detecting a range-extending target based on radar observation according to the present invention;
FIG. 2 is a graph of performance of a simulation experiment performed in an embodiment of the present invention;
FIG. 3 is a block diagram of an embodiment of a radar-based observation distance extended target detection system of the present invention;
fig. 4 is a schematic frame diagram of a radar observation-based range-extended target detection apparatus in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of an embodiment of a method for detecting a distance-extended target based on radar observation according to the present invention, as shown in fig. 1, including: s101, acquiring an orthogonal basis matrix of a signal subspace based on a significant eigenvalue of an observation covariance matrix, wherein the significant eigenvalue is acquired according to received data of all range units of the radar; s102, establishing a binary hypothesis test model based on the orthogonal basis matrix of the signal subspace and the orthogonal basis matrix of the target subspace; s103, according to the binary hypothesis test model, a tester based on a volume correlation function is established, test statistics of the tester are obtained, and whether a distance expansion target exists or not is judged based on the test statistics.
Specifically, step S101 is to acquire a base matrix of the signal subspace based on the eigenvalue, and then orthogonalize the base matrix of the signal subspace to acquire an orthogonal base matrix of the signal subspace. Here, the significant eigenvalues of the observed covariance matrix are obtained from the received data estimates of all range cells.
Further, step S102 is to establish a binary hypothesis testing model based on the orthogonal basis matrix of the signal subspace and the orthogonal basis matrix of the target subspace. Which implies that the orthogonal basis matrices of the target subspace are obtained by orthogonalizing the known basis matrices of the target subspace.
Further, step S103 is to determine whether or not a distance extension target exists based on the test statistic of the tester of the volume correlation function of the binary hypothesis test model.
According to the radar observation-based distance extension target detection method provided by the embodiment of the invention, the subspace is used as a basic unit and a processing object of signal representation, the detection is completed by utilizing the inherent geometric relation between the target subspace and the clutter subspace, and the checker based on the volume correlation function is established from the geometric perspective. Compared with the target detection method based on the volume correlation function provided by the prior art, the method provided by the embodiment of the invention does not need an iteration process, and is suitable for uniform and non-uniform environments. In addition, when the corresponding parameters in the algorithm select appropriate values, the distance extension target detection method provided by the embodiment of the invention is also suitable for a point target detection scene, and has stronger universality.
Based on the above embodiment, the obtaining an orthogonal basis matrix of a signal subspace based on a significant eigenvalue of an observation covariance matrix, where the significant eigenvalue is obtained according to received data of all range units of the radar specifically includes: estimating and obtaining an observation covariance matrix based on the received data of all range units of the radar; performing characteristic decomposition on the observation covariance matrix to obtain a plurality of characteristic values and a plurality of characteristic vectors which are in one-to-one correspondence with the plurality of characteristic values; and taking a matrix formed by a preset number of eigenvectors in the plurality of eigenvectors as an orthogonal basis matrix of the signal subspace.
Specifically, consider the following scenario: the radar has N receiving channels (the receiving channels may represent array elements, pulses or a combination of the two, depending on the specific application scenario), and the target to be detected spans K distance resolution units at most. For the kth range bin, when there is a range extension target, the received data may be represented as:
zk＝Pαk+Qβk+nk，k＝1，2，...，K，；
wherein z iskTo receive a data vector; the target and clutter echoes belong to two subspaces without cross-connection and haveIs a base matrix of a known target subspace, αkIn order to obtain the complex amplitude of the target echo,being a base matrix of a clutter subspace and q known, βkComplex amplitude of clutter echo; n iskIs white noise. Writing the received data as an N x K dimensional matrix z, wherein the kth column of z represents the received data for the kth range bin, then
Estimating an observation covariance matrix MzThe following were used:
wherein (·)HRepresenting a matrix conjugate transpose. As can be seen from the RMB (Reed-Mallett-Brennan) criterion, to ensure MzThe estimation accuracy should be 2N of K.
Further, for MzPerforming characteristic decomposition to obtain characteristic value lambda1≥λ2≥…≥λr≥λr+1＝…＝λNAnd corresponding feature vector u1，u2，...，ur，ur+1，...，uN. And taking a matrix formed by a preset number of eigenvectors as an orthogonal basis matrix of the signal subspace.
Based on the above embodiment, the taking a matrix formed by a preset number of eigenvectors in the plurality of eigenvectors as an orthogonal basis matrix of the signal subspace specifically includes: and taking a matrix formed by a preset number of eigenvectors in the plurality of eigenvectors in sequential arrangement as an orthogonal basis matrix of the signal subspace.
Specifically, a basis matrix composed of eigenvectors corresponding to the first r significant eigenvaluesAs an orthogonal basis matrix for the signal subspace. Wherein r represents a preset number.
Based on the above embodiment, the establishing a binary hypothesis testing model based on the orthogonal basis matrix of the signal subspace and the orthogonal basis matrix of the target subspace specifically includes: performing Gram-Schmidt orthogonalization on a basis matrix of a target subspace to obtain an orthogonal basis matrix of the target subspace; and establishing a binary hypothesis test model based on the orthogonal basis matrix of the signal subspace and the orthogonal basis matrix of the target subspace, wherein the binary hypothesis test model comprises a distance expansion target existence hypothesis and a non-distance expansion target existence hypothesis.
It is checked whether the observed data contains the target echo, i.e. whether the signal subspace contains the target subspace. Is provided withspan(Us) Representing the signal subspace and the target subspace, respectively, the above target detection can be described as a binary hypothesis testing problem.
Based on the above embodiment, the binary hypothesis testing model is represented by the following formula:
wherein,indicating that no range extension target exists,indicating that a distance extended target exists; dim (∩span(Us) Is shown in (a)And span (U)s) The dimensions of the intersecting sub-spaces are,is a signal subspace, span (U)s) Is a target subspace, UsIs an orthogonal basis matrix for the target subspace,is an orthogonal basis matrix of the signal subspace.
It should be noted that hypothesis testing is a method for inferring a population from a sample according to certain hypothesis conditions in mathematical statistics. The specific method comprises the following steps: making some assumption about the population studied as required by the problem, denoted H0; selecting a suitable statistic chosen such that its distribution is known, assuming H0 holds; from the measured samples, the value of the statistic is calculated and tested against a predetermined level of significance, making a decision to reject or accept the hypothesis H0. The conventional hypothesis test methods include u-test, t-test, X2 test (Chi-square test), F-test, rank sum test, and the like.
Based on the above embodiment, the determining whether there is a distance expansion target based on the test statistic specifically includes: judging whether the test statistic is larger than a preset judgment threshold or not; if the test statistic is larger than the preset judgment threshold, a distance expansion target exists; and if the test statistic is less than or equal to the preset judgment threshold, the distance expansion target does not exist.
It should be noted that the test statistics are:
based on the above embodiment, the volume correlation function is represented by the following formula:
wherein corr (Us) Representing volume-related functions, for any matrixHas a d-dimensional volume ofγi(i ═ 1, 2.. d) are the singular values of matrix X, m is the number of rows in the matrix, and d is the number of columns in the matrix.
Fig. 2 is a detection performance diagram of a simulation experiment performed in the embodiment of the present invention, and please refer to fig. 2, which shows a detection performance diagram of an application of the distance-extended target detection method. In the simulation, a radar receiving channel N is set to be 20, the number K of to-be-detected distance units is set to be 40, the noise-to-noise ratio CNR is set to be 20dB, and the false alarm probability P is setFA＝10-3The detection threshold passes 100/PFAObtained by a sub-Monte Carlo experiment. In the experiment, the angle distribution area of the fixed target is 0-10 degrees, and the clutter angle distribution area of 0-20 degrees and 40-60 degrees are selected to respectively represent the partially overlapped and non-overlapped scenes of the target and the clutter angle area. It can be seen that, when the target and the clutter angle region are not coincident, the whole detection curve moves to the left, which indicates that the detection performance is all improved at this time. For example, when the target and clutter angular regions are not coincident, the signal to noise ratio requirement to achieve 90% detection probability is reduced by 4 dB.
Based on the above embodiments, fig. 3 is a block diagram of an embodiment of the system for detecting a distance-extended target based on radar observation according to the present invention, including: an obtaining matrix module 301, configured to obtain an orthogonal basis matrix of a signal subspace based on a significant eigenvalue of an observation covariance matrix, where the significant eigenvalue is obtained according to received data of all range units of the radar; an obtaining model module 302, configured to establish a binary hypothesis testing model based on the orthogonal basis matrix of the signal subspace and the orthogonal basis matrix of the target subspace; the detection module 303 is configured to establish a checker based on a volume correlation function according to the binary hypothesis test model, obtain a test statistic of the checker, and determine whether a distance extension target exists based on the test statistic.
The detection system of the embodiment of the invention can be used for executing the technical scheme of the embodiment of the method for detecting the range expansion target based on radar observation shown in fig. 1, and the implementation principle and the technical effect are similar, and are not repeated here.
Based on the above embodiments, fig. 4 is a schematic frame diagram of a distance extended target detection device based on radar observation in an embodiment of the present invention. Referring to fig. 4, an embodiment of the present invention provides a device for detecting a range-extended target based on radar observation, including: a processor (processor)410, a communication Interface 420, a memory (memory)430 and a bus 440, wherein the processor 410, the communication Interface 420 and the memory 430 are communicated with each other via the bus 440. The processor 410 may call logic instructions in the memory 430 to perform methods comprising: acquiring an orthogonal basis matrix of a signal subspace based on a significant eigenvalue of an observation covariance matrix, wherein the significant eigenvalue is obtained according to received data of all range units of the radar; establishing a binary hypothesis test model based on the orthogonal basis matrix of the signal subspace and the orthogonal basis matrix of the target subspace; and establishing a checker based on a volume correlation function according to the binary hypothesis test model, acquiring test statistic of the checker, and judging whether a distance expansion target exists or not based on the test statistic.
An embodiment of the present invention discloses a computer program product, which includes a computer program stored on a non-transitory computer readable storage medium, where the computer program includes program instructions, and when the program instructions are executed by a computer, the computer can execute the capacity expansion method provided by the above-mentioned method embodiments, for example, the method includes: acquiring an orthogonal basis matrix of a signal subspace based on a significant eigenvalue of an observation covariance matrix, wherein the significant eigenvalue is obtained according to received data of all range units of the radar; establishing a binary hypothesis test model based on the orthogonal basis matrix of the signal subspace and the orthogonal basis matrix of the target subspace; and establishing a checker based on a volume correlation function according to the binary hypothesis test model, acquiring test statistic of the checker, and judging whether a distance expansion target exists or not based on the test statistic.
Based on the foregoing embodiments, an embodiment of the present invention provides a non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions cause the computer to execute the capacity expansion method provided by each method embodiment, for example, the method includes: acquiring an orthogonal basis matrix of a signal subspace based on a significant eigenvalue of an observation covariance matrix, wherein the significant eigenvalue is obtained according to received data of all range units of the radar; establishing a binary hypothesis test model based on the orthogonal basis matrix of the signal subspace and the orthogonal basis matrix of the target subspace; and establishing a checker based on a volume correlation function according to the binary hypothesis test model, acquiring test statistic of the checker, and judging whether a distance expansion target exists or not based on the test statistic.
Those of ordinary skill in the art will understand that: the implementation of the above-described apparatus embodiments or method embodiments is merely illustrative, wherein the processor and the memory may or may not be physically separate components, i.e. may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a usb disk, a removable hard disk, a ROM/RAM, a magnetic disk, an optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute the method according to the embodiments or some parts of the embodiments.
According to the distance extension target detection method and system provided by the embodiment of the invention, the subspace is used as a basic unit and a processing object of signal representation, the detection is completed by utilizing the inherent geometric relation between the target subspace and the clutter subspace, and the checker based on the volume correlation function is established from the geometric perspective. Compared with the target detection method based on the volume correlation function provided by the prior art, the method provided by the embodiment of the invention does not need an iteration process, and is suitable for uniform and non-uniform environments. In addition, when the corresponding parameters in the algorithm select appropriate values, the distance extension target detection method provided by the embodiment of the invention is also suitable for a point target detection scene, and has stronger universality.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
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