Frequency diversity array multiple-input multiple-output radar target detection system

Abstract

The invention relates to the technical field of radars, and provides a frequency diversity array multi-input multi-output radar target detection system. The system comprises a detection statistic construction module, a detection optimization module, a detection threshold determination module and a target judgment module. Wherein the detection statistic construction module is used for constructing a detection statistic; according to the detection statistics, constructing a semi-definite programming optimization model; the detection optimization module is used for constructing a semi-definite programming optimization model according to the detection statistics; the detection optimization module is also used for solving the optimization model to obtain an optimal value of the detection statistics; and the target judgment module is used for comparing the detection statistic optimal value with a detection threshold so as to detect whether a target exists or not, so that the target detection probability and the anti-interference capability of the frequency diversity array multi-input multi-output radar in a complex environment are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of radar, in particular to a frequency diversity array multiple-input multiple-output radar target detection system. BACKGROUND

[0002] The frequency diversity array multiple-input multiple-output radar is proposed due to its additional degree of freedom in the range domain and has attracted a lot of research attention. Compared with the traditional phased array, frequency diversity array radar or multiple-input multiple-output radar, the frequency diversity array multiple-input multiple-output radar significantly improves the target resolution capability and anti-jamming performance. At present, the research on this radar system has covered multiple fields: deception jamming suppression, range ambiguity clutter suppression, synthetic aperture radar imaging and parameter estimation. Only a few studies involve the detection problem of the frequency diversity array multiple-input multiple-output radar.

[0003] The frequency diversity array multiple-input multiple-output radar combines the distance dimension regulation capability of the frequency diversity array and the spatial processing advantage of the multiple-input multiple-output technology, realizes the beam range correlation through the carrier frequency increment, and forms the four-dimensional joint processing capability of "time-range-angle-Doppler" with the virtual array expansion, which exhibits unique technical advantages in complex electromagnetic environments.

[0004] However, the existing frequency diversity array multiple-input multiple-output radar target detection technology faces two key challenges: first, the unknownness of the target increment distance will cause the parameter mismatch problem, and the parameter mismatch problem will cause the significant decline of the system detection performance; second, the stability of the conventional detection method in complex electromagnetic environments is difficult to guarantee. These two problems seriously restrict the application effect of the frequency diversity array multiple-input multiple-output radar in actual environments.

[0005] Therefore, it is urgent to overcome the defects of the existing technology in the technical field. SUMMARY

[0006] The technical problem solved by the present application is to provide a frequency diversity array multiple-input multiple-output radar target detection system.

[0007] The present application adopts the following technical solutions: In a first aspect, the present application provides a frequency diversity array multiple-input multiple-output radar target detection system, characterized in that it comprises a detection statistic construction module, a detection optimization module, a detection threshold determination module and a target determination module; wherein: The detection statistic construction module is used to construct the detection statistic ; wherein, is a sample covariance matrix, represents the mean vector of the training sample matrix, represents the conjugate transpose, to jointly transmit and receive a steering vector, is an azimuth angle of a far-field point source, is a target incremental time delay; wherein a mean vector of the training sample matrix is obtained in advance; The detection optimization module is configured to construct an optimization model of a semi-definite programming according to the detection statistic . The detection optimization module is further configured to solve the optimization model to obtain an optimal value of the detection statistic. The target determination module is configured to compare the optimal value of the detection statistic with a detection threshold to detect whether a target exists.

[0008] Further, the sampling covariance matrix , is a training sample matrix, denotes a number of training samples; wherein the training sample matrix is obtained in advance.

[0009] Further, the optimization model is . wherein, denotes a maximum value of , denotes a real number set, and denotes and are both semi-definite matrices, and and are Hermitian matrices, is a number of transmitting array elements, and denote a construction matrix, , , , , . denotes a construction diagonal matrix, denotes a Hadamard product, , denotes a triangular polynomial coefficient vector of a detection statistic numerator, denotes a triangular polynomial coefficient vector of a detection statistic denominator, , , denotes a radar baseband bandwidth.

[0010] Further, the triangular polynomial coefficient vector of the detection statistic numerator . wherein, , Representation matrix of OK The elements of the column.

[0011] Furthermore, the triangular polynomial coefficient vector of the denominator of the detection statistic ; in, , This indicates the conjugate operation. Representing vectors of One element, , This represents the spatiotemporal modulation process of the transmitted signal in the angle-range domain. , , Indicates the receiving guide vector. For the number of receiving array elements, The number of elements in the transmitting array. For the spacing between array elements, Indicates the reference carrier wavelength.

[0012] Furthermore, it also includes a detection threshold determination module, which is used to: solve the optimization model using a convex optimization method and run it several times to obtain... Optimal value vector of detection statistics under the assumption and Optimal value vector of detection statistics under the assumption ; in, , , for Assume and Number of runs under the assumption; for Assuming the following operation is performed The optimal value of the test statistic for the second test. for Assuming the following operation is performed The optimal value of the test statistic for the second test. .

[0013] Furthermore, the detection threshold is ;in, Indicates in Assuming the following Arranged in descending order, This indicates that the data is rounded up; PFA represents the preset false alarm probability. This indicates that the data is sorted in descending order.

[0014] Furthermore, the target determination module is specifically used to: if the detection statistic has the optimal value Greater than or equal to the detection threshold If the detection statistic is optimal, then the target is determined to exist; Less than the detection threshold If the target does not exist, then it is determined that the target does not exist.

[0015] Furthermore, the characteristic is that the expression of the data matrix of the unit to be detected is: ;in, Represents the complex amplitude of the target signal. express Gaussian interference noise vector, For the target incremental delay, , Indicates the radar baseband bandwidth. For joint transmit / receive steering vector; The expression for the mean vector of the training sample matrix is: ;in, The number of training samples, .

[0016] Furthermore, the joint transmit / receive steering vector ; in, Indicates the Kronecker product. It represents the Hadamah accumulation. Indicates the receiving guide vector. The superscript indicates the guide vector representing the distance offset of the target within the cell. Indicates transpose. represents an imaginary number, Indicates the reference carrier wavelength. For the frequency to increase, This represents the spatiotemporal modulation process of the transmitted signal in the angle-range domain. , From elements Composition, and , Indicates the first The matched filter pair for the _th The output response of each transmitted waveform This represents the transmitted signal waveform of a frequency diversity array multiple-input multiple-output radar. , This indicates the conjugate operation. Indicates the pulse width. The number of elements in the transmitting array. For the number of receiving array elements, The distance between array elements.

[0017] In a second aspect, the present application also provides a frequency diversity array multiple-input multiple-output radar target detection device for implementing the frequency diversity array multiple-input multiple-output radar target detection system of the first aspect, and the device comprises: at least one processor; and a memory connected with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor to execute the frequency diversity array multiple-input multiple-output radar target detection system of the first aspect.

[0018] In a third aspect, the present application also provides a non-volatile computer storage medium, which stores computer executable instructions, and the computer executable instructions are executed by one or more processors to complete the method of the first aspect.

[0019] In a fourth aspect, a computer program product containing instructions is provided, which, when executed on a computer or processor, causes the computer or processor to execute the method of the first aspect and any one of the aspects thereof.

[0020] The present application constructs a detection statistic, uses the detection statistic to construct an optimization model of a semi-definite programming, solves the model to obtain an optimal value of the detection statistic, and uses the optimal value of the detection statistic to detect whether there is a target in a corresponding azimuth angle, so as to improve the target detection probability and the anti-interference ability of the frequency diversity array multiple-input multiple-output radar in a complex environment; the present application takes into account the robust detection of unknown target positions, maintains a high detection probability in a strong interference environment, improves the detection probability in a complex electromagnetic environment, provides a unified technical route for subsequent researches on complex scenes such as anti-clutter and anti-non-Gaussian interference, and has theoretical universality and engineering realizability. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application, and other drawings can also be obtained according to these drawings without creative labor for those skilled in the art.

[0022] Figure 1 is a whole flowchart of a frequency diversity array multiple-input multiple-output radar target detection system module provided by the embodiments of the present application; Figure 2 is a flowchart of comparing a detection statistic with a detection threshold provided by the embodiments of the present application; Figure 3 is a flowchart of a frequency diversity array multiple-input multiple-output radar target detection method provided by the embodiments of the present application; Figure 4 is a schematic diagram of a frequency diversity array multiple-input multiple-output radar target detection method provided by an embodiment of the present application; Figure 5 is a schematic diagram of a frequency diversity array multiple-input multiple-output radar target detection system provided by an embodiment of the present application; Figure 6 is a schematic diagram of a frequency diversity array multiple-input multiple-output radar target detection method provided by an embodiment of the present application and a detection probability of prior art; Figure 7 is a schematic diagram of a frequency diversity array multiple-input multiple-output radar target detection device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0024] Unless otherwise required by context, the term "comprises" in the specification and claims is to be construed as open-ended, i.e., as "comprises but not limited to". In the description of the specification, the terms "one embodiment", "some embodiments", "exemplary embodiment", "example", "specific example" or "some examples" are intended to mean that the particular feature, structure, material or characteristic being described in connection with such embodiment or example includes at least one embodiment or example of the present disclosure. The illustrative representations of the above terms do not necessarily refer to the same embodiment or example. In addition, the particular features, structures, materials or characteristics described in connection with a certain embodiment or example can be included in any suitable way in any one or more embodiments or examples, i.e., although they can be carried by the embodiment or example of the above terms due to the order of appearance and location, they are not limited to being carried by the embodiment or example in a combined manner.

[0025] In the description of the present application, the terms "first", "second" are only used for descriptive purposes and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features limited by "first", "second" can be explicitly or implicitly included in one or more features. In the description of the embodiments of the present disclosure, unless otherwise stated, the meaning of "multiple" is two or more. In addition, for example, in the description, the same type of nouns can also be described as two independent individuals by adding "A", "B" at the end, in which case the features limited by "A", "B" are only used for the purpose of distinguishing the same type of individual description, and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated.

[0026] In the description of the present application, the expression "A and / or B" (wherein A and B represent specific features) includes the following three combinations: A alone, B alone, and the combination of A and B.

[0027] In the present application, "about", "approximately" or "approximately" includes the stated value and the average value within the acceptable deviation range of the specific value, wherein the acceptable deviation range is determined by the person skilled in the art considering the measurement being discussed and the error related to the measurement of the specific quantity (i.e. the limitation of the measurement system).

[0028] In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as there is no conflict.

[0029] Embodiment 1: Embodiment 1 of the present application provides a frequency diversity array multiple-input multiple-output radar target detection system, as shown in Figure 1 , comprising: The detection statistic construction module is configured to construct a detection statistic ; wherein, is a sample covariance matrix, represents the mean vector of the training sample matrix, represents the conjugate transpose, is a joint transmit-receive steering vector, is the azimuth angle of the far-field point source, is the target incremental delay; wherein the mean vector of the training sample matrix is constructed in advance.

[0030] In one embodiment, the frequency diversity array multiple-input multiple-output radar target detection system of the present application can further comprise a data matrix construction module, which constructs a correlation matrix according to information, determines a target space-time steering vector, a sample covariance matrix and a to-be-detected unit data matrix.

[0031] The detection statistic construction module is configured to construct a detection statistic with adjustable parameters by using the to-be-detected unit data matrix and the Wald criterion.

[0032] The detection optimization module is configured to construct a semi-definite programming optimization model according to the detection statistic .

[0033] The detection optimization module is further configured to solve the optimization model to obtain the optimal value of the detection statistic.

[0034] In one embodiment, the detection optimization module is configured to transform the detection statistics into a convex optimization form, construct an optimization problem related to a semi-definite programming, and determine an optimal value of the detection statistics.

[0035] In one embodiment, the detection optimization module can include a detection threshold determination module, or the frequency diversity array multiple-input multiple-output radar target detection system according to the embodiments of the present application can also include a detection threshold determination module. The detection threshold determination module is configured to determine a detection threshold value by using system parameters, a preset false alarm probability of the system, and the optimal value of the detection statistics, which will be described below.

[0036] The target determination module is configured to compare the optimal value of the detection statistics with the detection threshold to determine whether a target exists.

[0037] In one embodiment, the target determination module is configured to compare the optimal value of the detection statistics with the detection threshold value to determine whether a target exists. If the detection statistics is less than the detection threshold, it is determined that no target exists. If the detection statistics is greater than or equal to the detection threshold, it is determined that a target exists. The value of the detection threshold is determined by system parameters, a preset false alarm probability of the system, and the optimization model related to the semi-definite programming.

[0038] In the prior art, due to the unknown and uncertain target increment distance, a parameter mismatch problem occurs, i.e., the increment distance is difficult to accurately obtain, which significantly reduces the performance of the conventional detector in the prior art. In addition, in a noise suppression interference environment, a radar signal is easily affected by complex Gaussian interference, and the covariance matrix of the radar signal needs to be estimated depending on training samples. The statistical characteristics of the interference signal are often unknown and time-varying, which makes the performance of the conventional detection algorithm based on a fixed interference model significantly degraded, and the stability of the conventional detection method is difficult to guarantee in a complex electromagnetic environment. In addition, the estimation of the target position parameter in the detection has high computational complexity, and real-time performance and accuracy are difficult to balance.

[0039] The present application constructs a detection statistics, constructs an optimization model of a semi-definite programming by using the detection statistics, solves the model to obtain an optimal value of the detection statistics, compares the optimal value of the detection statistics with a detection threshold to determine whether a target exists, and realizes target detection of a frequency diversity array multiple-input multiple-output radar in a complex environment. The present application balances the robust detection of an unknown target position, maintains a high detection probability in a strong interference environment, improves the detection probability in a complex electromagnetic environment, provides a unified technical route for subsequent research on complex scenes such as anti-clutter and anti-non-Gaussian interference, and has theoretical universality and engineering realizability.

[0040] Furthermore, by adjusting parameters, the detector in this embodiment of the invention can optimize the waveform design and spatial-distance degree of freedom allocation of the frequency diversity array multi-input multi-output radar, thereby adapting to different detection scenario requirements and ensuring detection stability.

[0041] It should be noted that the detector in this embodiment of the invention is suitable for large-sample, stationary interference scenarios; it is also suitable for problems where the interference covariance matrix and target parameters are unknown, and can significantly improve robustness.

[0042] Wherein, the sampling covariance matrix , For the training sample matrix, , The number of training samples and the data matrix of the units to be detected represent the total number of training samples. and training sample matrix Both have a dimension of 1. ,in, This refers to the number of transmit elements in a frequency diversity array multiple-input multiple-output radar. For the number of receiving array elements, This represents the number of training samples; where the data matrix of the units to be detected is... and training sample matrix All of them were pre-built.

[0043] In one optional implementation, the optimization model is: ;in, Indicates the detection statistic The maximum value, Represents the set of real numbers. and express and All are positive semi-definite matrices, and and For Hermitian matrices, The number of elements in the transmitting array. and Describes the constructed matrix. , , , , ; This indicates the construction of a diagonal matrix. It represents the Hadamah accumulation. , This represents the vector of triangular polynomial coefficients in the numerator of the detection statistic. This represents the vector of trigonometric polynomial coefficients in the denominator of the detection statistic. , , This indicates the radar baseband bandwidth.

[0044] Wherein, the triangular polynomial coefficient vector of the numerator of the detection statistic ;in, , Representation matrix of OK Column elements, The number of elements in the launch array.

[0045] The trigonometric polynomial coefficient vector of the denominator of the detection statistic ;in, , This indicates the conjugate operation. Representing vectors of One element, , This represents the spatiotemporal modulation process of the transmitted signal in the angle-range domain. , , Indicates the receiving guide vector. For the number of receiving array elements, The number of elements in the transmitting array. For the spacing between array elements, Indicates the reference carrier wavelength.

[0046] In practical applications, the detection optimization module solves the optimization model to obtain the optimal value of the detection statistics, specifically: The detection threshold determination module uses a convex optimization method to solve the optimization model and runs it several times to obtain... Optimal value vector of detection statistics under the assumption and Optimal value vector of detection statistics under the assumption .

[0047] in, , , for Assume and Number of runs under the assumption; for Assuming the following operation is performed The optimal value of the test statistic for the second test. for Assuming the following operation is performed The optimal value of the test statistic for the second test. .

[0048] In some embodiments, detection threshold ;in, Indicates in Assuming the following in descending order, denotes rounding up the data, and PFA denotes a preset false alarm probability, denotes descending order sorting of the data.

[0049] In actual use, the target determination module is specifically configured to: compare the detection statistic optimal value with a detection threshold to detect whether a target exists, as shown in the following formula: Figure 2 specifically includes the following steps. In step 301, if the detection statistic optimal value is greater than or equal to a detection threshold , it is determined that a target exists.

[0050] In step 302, if the detection statistic optimal value is less than the detection threshold , it is determined that a target does not exist.

[0051] In an actual application scenario, the to-be-detected cell data matrix , the training sample matrix , and the training sample matrix are obtained in advance, and specifically, an expression of the to-be-detected cell data matrix is as follows: ; wherein, denotes a complex amplitude of a target signal, denotes an dimensional Gaussian interference and noise vector, is a target incremental time delay, , denotes a radar baseband bandwidth, is a joint transmit-receive steering vector. The joint transmit-receive steering vector ; wherein, denotes a Kronecker product, denotes a Hadamard product, denotes a receive steering vector, denotes a steering vector of a target distance offset in a distance unit, and a superscript denotes transposition, denotes an imaginary number, denotes a reference carrier wavelength, is a frequency increment, denotes a space-time modulation process of a transmit signal in an angle-distance domain, , is composed of elements , and , denotes an output response of an th matched filter to an th transmit waveform, This represents the transmitted signal waveform of a frequency diversity array multiple-input multiple-output radar. , This indicates the conjugate operation. Indicates the pulse width. The number of elements in the transmitting array. For the number of receiving array elements, The distance between array elements.

[0052] The frequency diversity array multi-input multi-output radar target detection method provided in this embodiment does not require independent filtering and constant false alarm rate (CFAR) processing steps, and has adaptive detection capability for targets with unknown positions within the range cell. It maintains good detection performance even under strong noise suppression interference environments. This method significantly improves the target detection capability of radar systems in complex electromagnetic environments and can also ensure robust detection of unknown target positions; it is suitable for large-sample, stable interference scenarios, ensuring that the detector achieves target detection with a high probability.

[0053] Example 2: Based on the method described in Embodiment 1, this invention combines specific application scenarios and uses technical descriptions in relevant scenarios to illustrate the implementation process of the features of this invention in those scenarios.

[0054] Due to noise suppression interference, the transmitted and received signals of frequency diversity array multi-input multi-output radar are easily affected by interference. Its covariance matrix needs to be estimated based on training samples, and the statistical characteristics of the interference signal are often unknown and time-varying, significantly degrading the performance of traditional detection algorithms based on fixed interference models. Furthermore, the uncertainty of target position parameters makes it difficult to accurately obtain incremental distance, further reducing the performance of traditional detectors. To address the adaptive detection problem of frequency diversity array multi-input multi-output radar under noise suppression interference conditions, this invention provides a target detection method for frequency diversity array multi-input multi-output radar, illustrated by the following application scenario: Assume the number of transmitting array elements in a frequency diversity array multiple-input multiple-output radar system is . The number of receiving array elements is The spacing between array elements is A far-field point source is located at the azimuth angle. In this case, when the data to be detected contains both targets and noise, the data to be detected can be used. A dimensional vector is represented as:

[0055] in, Represents the complex amplitude of the target signal. express Gaussian interference noise vector, For the target incremental delay, , Indicates the radar baseband bandwidth. For joint transmit / receive steering vector, and , Indicates the Kronecker product. It represents the Hadamah accumulation. Indicates the receiving guide vector. The superscript indicates the guide vector representing the distance offset of the target within the cell. Indicates transpose. represents an imaginary number, Indicates the reference carrier wavelength. For the frequency to increase, This represents the spatiotemporal modulation process of the transmitted signal in the angle-range domain. , From elements Composition, and , Indicates the first The matched filter pair for the _th The output response of each transmitted waveform This represents the transmitted signal waveform of a frequency diversity array multiple-input multiple-output radar. , Indicates the pulse width.

[0056] The frequency diversity array multi-input multi-output radar target detection method provided in this embodiment, such as Figure 3 and Figure 4 As shown, it includes: In step 401, the target space-time steering vector, the sampling covariance matrix, and the data matrix of the unit to be detected are determined.

[0057] In step 402, the detection statistics of the Wald criterion are designed and processed in an integrated manner.

[0058] In step 403, the detection problem is transformed into a convex optimization problem, and an optimization model based on semidefinite programming is constructed.

[0059] In step 404, the optimization problem is solved to obtain the optimal value of the detection statistic.

[0060] In step 405, the detection threshold is determined based on system parameters, the system's preset false alarm probability, and the optimization model based on semidefinite programming.

[0061] In step 406, the optimal value of the detection statistic is compared with the detection threshold to determine whether the target exists. If the detection statistic is less than the detection threshold, the target is determined not to exist; if the detection statistic is greater than or equal to the detection threshold, the target is determined to exist.

[0062] The constructed data matrix of the unit to be detected and the training sample matrix are respectively represented as follows: and ( ), both of which have dimension wherein, is the number of transmit elements of the frequency-diversity array multiple-input multiple-output radar, is the number of receive elements, denotes the number of training samples.

[0063] The sample covariance matrix constructed by the training samples is denoted as is wherein the superscript denotes the conjugate transpose.

[0064] The detection statistic with adjustable parameters based on the Wald criterion is ; in the formula, denotes the absolute value operation.

[0065] The optimization model of the semi-definite programming is .

[0066] in the formula, denotes the maximum value of the detection statistic denotes the real number set, denotes the real number set, and denote and are both semi-definite matrices, and and are Hermitian matrices, and respectively denote the triangular polynomial coefficient vectors of the numerator and the denominator of the detection statistic, and the expressions thereof are , , and respectively denote , , denotes the element in the row column of the matrix , denotes the conjugate operation, denotes the elements of the vector , , denotes the space-time modulation process of the transmit signal in the angle-range domain, , , ; and denote the construction matrices, and the expressions thereof are , ,in , , ; This indicates the construction of a diagonal matrix. , , .

[0067] Step 405 specifically involves obtaining the detection statistic using a convex optimization tool. maximum value , respectively obtained Assume and The optimal value under the assumption, and That is, the Wald detection statistic based on semidefinite programming optimization; When in Assume and Run them separately under the following assumptions At this time, the dimensions are all obtained. Statistical vector and superscript Indicates running the first Second-rate, ;exist Under the following assumptions, calculate the detection threshold: , , indicating in Assuming the following Arranged in descending order; This indicates that the data is rounded up; PFA represents the preset false alarm probability. This indicates that the data is sorted in descending order.

[0068] The step of comparing the optimal value of the detection statistic with the detection threshold to determine whether the target exists specifically involves: setting the optimal value of the detection statistic... With the detection threshold A comparison is made to determine whether the target exists. Specifically: if the detection statistic is optimal... Greater than or equal to the detection threshold If the detection statistic is optimal, then the target is determined to exist; Less than the detection threshold If the target does not exist, then it is determined that the target does not exist.

[0069] The method and system provided in this embodiment not only improve the theoretical framework of frequency diversity array multi-input multi-output radar detection, but also provide key technical support for practical system design, serving as fundamental work for radar target detection in complex environments. By solving the point target detection problem in Gaussian noise, it lays a methodological foundation for subsequent research on complex scenarios such as clutter and interference resistance, possessing significant theoretical and practical application value. The method described in this embodiment, by adjusting parameters, can optimize the waveform design and array configuration of frequency diversity array multi-input multi-output radar, applicable to large-sample, stationary interference scenarios, and particularly suitable for problems where the interference covariance matrix and target parameters are unknown, significantly improving robustness.

[0070] Figure 6 This is the method of this embodiment (actually, the detector that applies the method described in this embodiment). Figure 6 The curve with circles in it) and the existing 1S-GLRT-based detector ( Figure 6 (The curves marked with asterisks) are schematic diagrams illustrating the detection probability at different signal-to-interference-plus-noise ratios. It can be seen that, under suitable parameters, the method in this embodiment has a higher detection probability than the existing 1S-GLRT detector.

[0071] Example 3: like Figure 7 The diagram shown is a schematic representation of the architecture of a frequency diversity array multi-input multi-output radar target detection device according to an embodiment of the present invention. The frequency diversity array multi-input multi-output radar target detection device of this embodiment includes one or more processors 21 and a memory 22. Figure 7 Take a processor 21 as an example.

[0072] Processor 21 and memory 22 can be connected via a bus or other means. Figure 7 Taking the example of a connection between China and Israel via a bus.

[0073] The memory 22, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs and non-volatile computer-executable programs, such as the frequency diversity array multi-input multi-output radar target detection method and / or the frequency diversity array multi-input multi-output radar target detection system in Embodiment 1. The processor 21 executes the frequency diversity array multi-input multi-output radar target detection method and / or implements the frequency diversity array multi-input multi-output radar target detection system in Embodiment 1 by running the non-volatile software programs and instructions stored in the memory 22.

[0074] The memory 22 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state memory device. In some embodiments, the memory 22 can optionally include a memory that is remotely located relative to the processor 21, and these remotely located memories can be connected to the processor 21 through a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0075] The program instructions / modules are stored in the memory 22, and when executed by the one or more processors 21, perform the frequency diversity array multiple-input multiple-output radar target detection method in the above-mentioned embodiment 1, and / or implement the frequency diversity array multiple-input multiple-output radar target detection system in embodiment 1.

[0076] It is worth noting that the information interaction, execution process, and the like between the modules and units in the above-mentioned apparatus and system are based on the same concept as the processing method embodiments of the present application, and the specific content can be referred to the description in the method embodiments of the present application, which will not be described here.

[0077] Those of ordinary skill in the art can understand that all or part of the steps in the various embodiments can be completed by a program instructing relevant hardware, and the program can be stored in a computer readable storage medium, which can include a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and the like.

[0078] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A frequency diversity array multi-input multi-output radar target detection system, characterized in that, It includes a detection statistics construction module, a detection optimization module, a detection threshold determination module, and a target determination module; among which: The detection statistics construction module is used to construct detection statistics. ;in, For the sampling covariance matrix, This represents the mean vector of the training sample matrix. This indicates the conjugate transpose. For joint transmit / receive steering vector, The azimuth angle of the far-field point source. The target incremental delay; wherein, the mean vector of the training sample matrix. It is pre-built; The detection optimization module is used to optimize the detection based on the detection statistics. Construct a semidefinite programming optimization model; The detection optimization module is also used to solve the optimization model to obtain the optimal value of the detection statistics; The target determination module is used to compare the optimal value of the detection statistics with the detection threshold to detect whether the target exists.

2. The frequency diversity array multi-input multi-output radar target detection system according to claim 1, characterized in that, The sampling covariance matrix , For the training sample matrix, This represents the number of training samples; where the training sample matrix... It is pre-built.

3. The frequency diversity array multi-input multi-output radar target detection system according to claim 1, characterized in that, The optimization model is as follows: ; in, Indicates the detection statistic The maximum value, Represents the set of real numbers. and express and All are positive semi-definite matrices, and and For Hermitian matrices, The number of elements in the transmitting array. and Describes the constructed matrix. , , , , ; This indicates the construction of a diagonal matrix. It represents the Hadamah accumulation. , This represents the vector of triangular polynomial coefficients in the numerator of the detection statistic. This represents the vector of trigonometric polynomial coefficients in the denominator of the detection statistic. , , This indicates the radar baseband bandwidth.

4. The frequency diversity array multi-input multi-output radar target detection system according to claim 3, characterized in that, The triangular polynomial coefficient vector of the numerator of the detection statistic ; in, , Representation matrix of OK The elements of the column.

5. The frequency diversity array multi-input multi-output radar target detection system according to claim 3, characterized in that, The trigonometric polynomial coefficient vector of the denominator of the detection statistic ; in, , This indicates the conjugate operation. Representing vectors of One element, , This represents the spatiotemporal modulation process of the transmitted signal in the angle-range domain. , , Indicates the receiving guide vector. For the number of receiving array elements, The number of elements in the transmitting array. For the spacing between array elements, Indicates the reference carrier wavelength.

6. The frequency diversity array multi-input multi-output radar target detection system according to claim 1, characterized in that, It also includes a detection threshold determination module, which is used to: solve the optimization model using a convex optimization method and run it several times to obtain... Optimal value vector of detection statistics under the assumption and Optimal value vector of detection statistics under the assumption ; in, , , for Assume and Number of runs under the assumption; for Assuming the following operation is performed The optimal value of the test statistic for the second test. for Assuming the following operation is performed The optimal value of the test statistic for the second test. .

7. The frequency diversity array multi-input multi-output radar target detection system according to claim 6, characterized in that, The detection threshold is ;in, Indicates in Assuming the following Arranged in descending order, This indicates that the data is rounded up; PFA represents the preset false alarm probability. This indicates that the data is sorted in descending order.

8. The frequency diversity array multi-input multi-output radar target detection system according to claim 7, characterized in that, The target determination module is specifically used for: if the detection statistic is optimal... Greater than or equal to the detection threshold If the detection statistic is optimal, then the target is determined to exist; Less than the detection threshold If the target does not exist, then it is determined that the target does not exist.

9. The frequency diversity array multi-input multi-output radar target detection system according to claim 1, characterized in that, The expression for the data matrix of the unit to be detected is: ;in, Represents the complex amplitude of the target signal. express Gaussian interference noise vector, For the target incremental delay, , Indicates the radar baseband bandwidth. For joint transmit / receive steering vector; The expression for the mean vector of the training sample matrix is: ;in, The number of training samples, .

10. The frequency diversity array multi-input multi-output radar target detection system according to claim 9, characterized in that, The joint transmit / receive steering vector ; in, Indicates the Kronecker product. It represents the Hadamah accumulation. Indicates the receiving guide vector. The superscript indicates the guide vector representing the distance offset of the target within the cell. Indicates transpose. represents an imaginary number, Indicates the reference carrier wavelength. For the frequency to increase, This represents the spatiotemporal modulation process of the transmitted signal in the angle-range domain. , From elements Composition, and , Indicates the first The matched filter pair for the _th The output response of each transmitted waveform This represents the transmitted signal waveform of a frequency diversity array multiple-input multiple-output radar. , This indicates the conjugate operation. Indicates the pulse width. The number of elements in the transmitting array. For the number of receiving array elements, The distance between array elements.

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