Optimized frequency diversity array multiple-input multiple-output radar target detection method

Abstract

The present application relates to the field of radar technology, and provides a kind of frequency diversity array multiple-input multiple-output radar target detection method based on optimization.The method comprises: constructing detection statistics;According to the detection statistics, a semi-definite programming optimization model is constructed;The optimal value of the detection statistics is obtained by solving the optimization model;The optimal value of the detection statistics is compared with the detection threshold to detect whether the target exists.The present application constructs detection statistics and establishes semi-definite programming optimization model, and the optimal value of the statistics is obtained by solving, and then compared with the detection threshold to determine whether the target exists;The method improves the target detection probability and anti-interference ability under strong noise suppression interference environment by optimizing statistics design and semi-definite programming solution.

Description

Technical Field

[0001] This invention relates to the field of radar technology, and in particular to a target detection method for multi-input multi-output radar based on an optimized frequency diversity array. Background Technology

[0002] Frequency diversity array (FDI) multiple-input multiple-output (MIMO) radars have been proposed and attracted considerable research attention due to their additional degrees of freedom in the range domain. Compared to traditional phased array, frequency-controlled array, or MIMO radars, FDI significantly improves target resolution and anti-jamming performance. Current research on this radar system covers multiple areas: deceptive jamming suppression, range ambiguity clutter suppression, synthetic aperture radar imaging, and parameter estimation. Only a few studies have addressed the detection problem of FDI.

[0003] Target detection in frequency diversity array multi-input multi-output (MIMO) radars under noise suppression jamming conditions faces two main challenges. First, under noise suppression jamming, radar signals are susceptible to complex Gaussian interference, whose covariance matrix needs to be estimated using training samples. 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. Second, the uncertainty of target position parameters makes it difficult to accurately obtain incremental distances, significantly reducing the performance of traditional detectors. Furthermore, estimating target position parameters involves high computational complexity and a trade-off between real-time performance and accuracy. These technical bottlenecks severely restrict the detection performance of frequency diversity array MIMO radars in complex environments.

[0004] Therefore, overcoming the shortcomings of the existing technology is an urgent problem to be solved in this technical field. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a target detection method for multi-input multi-output radar based on optimized frequency diversity array.

[0006] The present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a target detection method for multi-input multi-output radar based on an optimized frequency diversity array, comprising:

[0008] Construct detection statistics ;in, For the sampling covariance matrix, The data matrix of the unit to be detected. This indicates the conjugate transpose. For joint transmit / receive steering vector, The azimuth angle of the far-field point source. Incremental delay for the target;

[0009] According to the detection statistics Construct a semidefinite programming optimization model;

[0010] The optimal value of the detection statistic is obtained by solving the optimization model.

[0011] The optimal value of the detection statistic is compared with the detection threshold to detect whether the target exists.

[0012] Preferably, the sampling covariance matrix , For the training sample matrix, This indicates the number of training samples.

[0013] Preferably, the optimization model is as follows: ;

[0014] in, express 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. , 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.

[0015] Preferably, the triangular polynomial coefficient vector of the numerator of the detection statistic ;

[0016] in, , Representation matrix of OK Column elements, .

[0017] Preferably, the trigonometric polynomial coefficient vector of the denominator of the detection statistic ;

[0018] 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. , , ; , The number of elements in the launch array.

[0019] Preferably, the trigonometric polynomial coefficient vector of the denominator of the detection statistic ;

[0020] in, , Representing vectors of One element, , , , 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.

[0021] Preferably, solving the optimization model to obtain the optimal value of the detection statistic specifically includes:

[0022] The optimization model is solved using the convex optimization method. Assume and The optimal value of the detection statistic is assumed, and the test is run several times to obtain the vector of optimal detection statistic values. and ;

[0023] in, , , for Assume and Number of runs under the assumption; for Assuming the following operation is performed The calculated value of the test statistic for each test. for Assuming the following operation is performed The calculated value of the test statistic for each test. .

[0024] Preferably, the detection threshold is ;in, , This indicates that the data is rounded to the nearest integer. PFA represents the preset false alarm probability. This indicates that the data is sorted in descending order.

[0025] Preferably, comparing the optimal value of the detection statistic with the detection threshold to detect the presence of the target specifically includes:

[0026] If the detection statistic is optimal Greater than or equal to the detection threshold If so, then the target is determined to exist;

[0027] If the detection statistic is optimal Less than the detection threshold If the target does not exist, then it is determined that the target does not exist.

[0028] Preferably, the data matrix of the unit to be detected and training sample matrix It is pre-built, specifically including:

[0029] Construct the data matrix of the unit to be detected ;in, Represents the complex amplitude of the target signal. express Gaussian interference noise vector, For the target incremental delay, , Indicates the radar baseband bandwidth. This is the joint transmit / receive steering vector.

[0030] Preferably, the joint transmit / receive steering vector ;

[0031] 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.

[0032] Secondly, the present invention also provides an optimized frequency diversity array multi-input multi-output radar target detection device for implementing the optimized frequency diversity array multi-input multi-output radar target detection method described in the first aspect, the device comprising:

[0033] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor for performing the optimized frequency diversity array multi-input multi-output radar target detection method described in the first aspect.

[0034] Thirdly, the present invention also provides a non-volatile computer storage medium storing computer-executable instructions that are executed by one or more processors to perform the method described in the first aspect.

[0035] Fourthly, a chip is provided, comprising: a processor and an interface for calling and running a computer program stored in memory, performing the method as described in the first aspect.

[0036] Fifthly, a computer program product comprising instructions is provided, which, when executed on a computer or processor, cause the computer or processor to perform the methods of the first aspect and any one thereof.

[0037] This invention constructs a detection statistic and uses it to build a semidefinite programming optimization model. The optimal value of the detection statistic is obtained by solving the model and comparing it with the detection threshold to determine whether the target exists. This significantly improves the target detection probability and anti-interference capability under strong noise suppression interference environment. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments of the present invention will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0039] Figure 1 This is a flowchart illustrating an optimized frequency diversity array multi-input multi-output radar target detection method provided in an embodiment of the present invention;

[0040] Figure 2 This is a flowchart illustrating an optimized frequency diversity array multi-input multi-output radar target detection method provided in an embodiment of the present invention;

[0041] Figure 3 This is a flowchart illustrating an optimized frequency diversity array multi-input multi-output radar target detection method provided in an embodiment of the present invention;

[0042] Figure 4 This is a schematic diagram of an optimized frequency diversity array multi-input multi-output radar target detection method provided in an embodiment of the present invention;

[0043] Figure 5 This is a schematic diagram of the architecture of an optimized frequency diversity array multi-input multi-output radar target detection system provided in an embodiment of the present invention;

[0044] Figure 6 This is a schematic diagram illustrating the detection probability of an optimized frequency diversity array multi-input multi-output radar target detection method provided by an embodiment of the present invention compared with existing technologies;

[0045] Figure 7 This is a schematic diagram of the architecture of an optimized frequency diversity array multi-input multi-output radar target detection device provided in an embodiment of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the invention 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 merely illustrative and not intended to limit the invention.

[0047] Unless the context otherwise requires, throughout the specification and claims, the term "comprising" is interpreted as openly inclusive, meaning "including, but not limited to." In the description of the specification, terms such as "one embodiment," "some embodiments," "exemplary embodiment," "example," "specific example," or "some examples" are intended to indicate that a particular feature, structure, material, or characteristic associated with that embodiment or example is included in at least one embodiment or example of this disclosure. The illustrative representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics mentioned may be included in any suitable manner in any one or more embodiments or examples; that is, although they may be incorporated into embodiments or examples using the above terms for reasons such as order and position, it does not limit them to be incorporated in combination by a single embodiment or example.

[0048] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of embodiments of this disclosure, unless otherwise stated, "a plurality of" means two or more. Furthermore, for example, the description may use the prefix "A" or "B" to describe the same type of nouns as two independent entities. In this case, the corresponding features defined with "A" and "B" are used only to distinguish between similar entities and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features.

[0049] In the description of this invention, the expression “A and / or B” (where A and B are used to formally represent specific features) will be used. The corresponding expression includes the following three combinations: only A, only B, and a combination of A and B.

[0050] As used in this invention, “about,” “approximately,” or “approximately” includes the stated value and the average value within an acceptable range of deviation from a particular value, wherein the acceptable range of deviation is determined by a person skilled in the art taking into account the measurement under discussion and the error associated with the measurement of the particular quantity (i.e., the limitations of the measurement system).

[0051] Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0052] Example 1:

[0053] Embodiment 1 of the present invention provides a target detection method for multi-input multi-output radar based on optimized frequency diversity array, such as... Figure 1As shown, it includes:

[0054] In step 201, the detection statistic is constructed. ;in, For the sampling covariance matrix, The data matrix of the unit to be detected. This indicates the conjugate transpose. For joint transmit / receive steering vector, The azimuth angle of the far-field point source. Incremental delay for the target;

[0055] In step 202, based on the detection statistics Construct a semidefinite programming optimization model;

[0056] In step 203, the optimization model is solved to obtain the optimal value of the detection statistic;

[0057] In step 204, the optimal value of the detection statistic is compared with the detection threshold to detect whether the target exists.

[0058] The detection threshold is determined by system parameters, the system's preset false alarm probability, and an optimization model based on semidefinite programming.

[0059] This embodiment constructs a detection statistic and uses it to build a semidefinite programming optimization model. The optimal value of the detection statistic is obtained by solving the model, and then compared with the detection threshold to determine whether the target exists. This significantly improves the target detection probability and anti-interference capability under strong noise suppression interference environment.

[0060] 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 indicates the number of training samples.

[0061] 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. , The vector of triangular polynomial coefficients in the numerator of the detection statistic is denoted as . This represents the vector of trigonometric polynomial coefficients in the denominator of the detection statistic. , , This indicates the radar baseband bandwidth.

[0062] 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.

[0063] The trigonometric polynomial coefficient vector of the denominator of the detection statistic ;in, , Representing vectors of One element, , , , 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.

[0064] In practical application scenarios, solving the optimization model to obtain the optimal value of the detection statistic specifically includes:

[0065] The optimization model is solved using the convex optimization method. Assume and The optimal value of the detection statistic is assumed, and the test is run several times to obtain the vector of optimal detection statistic values. and the optimal value vector .

[0066] 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. .

[0067] In some embodiments, the detection threshold is ;in, , This indicates that the data is rounded to the nearest integer. PFA represents the preset false alarm probability. This indicates that the data is sorted in descending order.

[0068] In practical applications, the optimal value of the detection statistic is compared with the detection threshold to detect the presence of the target. Figure 2 As shown, it specifically includes:

[0069] In step 301, if the detection statistic is optimal... Greater than or equal to the detection threshold If so, then the target is determined to exist;

[0070] In step 302, if the detection statistic is optimal... Less than the detection threshold If the target does not exist, then it is determined that the target does not exist.

[0071] In a practical application scenario, the data matrix of the unit to be detected and training sample matrix It is pre-constructed, specifically including: constructing the data matrix of the unit to be detected. ;in, Represents the complex amplitude of the target signal. express Gaussian interference noise vector, For the target incremental delay, , Indicates the radar baseband bandwidth. This is 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. , Indicates the pulse width. The number of elements in the transmitting array. For the number of receiving array elements, The distance between array elements.

[0072] The optimized frequency diversity array multi-input multi-output radar target detection method provided in this embodiment eliminates the need for independent filtering and constant false alarm rate (CFAR) processing steps, and possesses 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, while also ensuring robust detection of unknown target positions, guaranteeing that the detector detects targets with a high probability even under low signal-to-noise ratio (SNR) conditions.

[0073] Example 2:

[0074] 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.

[0075] 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 an optimized target detection method for frequency diversity array multi-input multi-output radar, illustrated by the following application scenario:

[0076] 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:

[0077]

[0078] 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.

[0079] The optimized 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:

[0080] In step 401, the data matrix of the unit to be detected and the training sample matrix are constructed.

[0081] In step 402, a sampling covariance matrix is ​​constructed using the training sample matrix, and a guide vector for target distance offset and a transmit / receive guide vector are constructed within the range cell based on the target distance increment and the target azimuth angle, respectively.

[0082] In step 403, a detection statistic with adjustable parameters is constructed.

[0083] In step 404, an optimization model for semidefinite programming is constructed using the detection statistics.

[0084] In step 405, the optimization problem is solved to obtain the optimal value of the detection statistic.

[0085] In step 406, the detection threshold is determined based on system parameters, the system's preset false alarm probability, and the optimization model based on semidefinite programming.

[0086] In step 407, the detection statistic (i.e., the optimal value of the detection statistic) is compared with the detection threshold to determine whether the target exists.

[0087] The constructed data matrix of the unit to be detected and the training sample matrix are respectively represented as follows: and ( 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 indicates the number of training samples.

[0088] The sampling covariance matrix constructed using the training samples is denoted as ,for superscript This indicates the conjugate transpose.

[0089] The detection statistic with adjustable parameters is In the formula, This indicates the absolute value operation.

[0090] The optimization model of the semidefinite programming is as follows: .

[0091] In the formula, 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, and Let these be the trigonometric polynomial coefficient vectors of the numerator and denominator of the detection statistic, respectively, and their expressions are as follows: , , and They are respectively represented as , , Representation matrix of OK Column elements, This indicates the conjugate operation. Representing vectors of One element, , , , ; and The two expressions are: , ,in , , ; This indicates the construction of a diagonal matrix. , , .

[0092] Step 405 specifically involves: obtaining the solution using a convex optimization tool. maximum value , respectively obtained The optimal value vector under the assumption and The optimal value vector under the assumption That is, the detection statistics of the Rao detector based on semidefinite programming optimization;

[0093] When in 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: , ,in, This indicates that the data is rounded to the nearest integer. PFA represents the preset false alarm probability. This indicates that the data is sorted in descending order.

[0094] The step of comparing 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.

[0095] Based on the method described in this embodiment, this embodiment also provides an optimized frequency diversity array multi-input multi-output radar target detection system, such as... Figure 5 As shown, the system includes the following modules: a data matrix construction module, used to construct a data matrix of the unit to be detected and a training sample matrix; an intermediate variable matrix construction module, used to construct a sampling covariance matrix based on the training samples, and to construct a steering vector for the target distance offset within the range unit and a transmit / receive steering vector based on the target distance increment and target azimuth angle, respectively; a detection statistics construction module, used to construct a detection statistics with adjustable parameters using the data matrix and the intermediate variable matrix; a detection optimization module, used to determine the optimal value of the detection statistics by obtaining an optimization problem equivalent to a semidefinite programming problem based on the detection statistics with adjustable parameters; a detection threshold determination module, used to determine the detection threshold value using the false alarm probability preset by the system and the optimal value of the detection statistics; and a target determination module, used to compare the optimal value of the detection statistics with the detection threshold value to determine whether the target exists. The method and system provided in this embodiment not only improve the theoretical system of frequency diversity array multi-input multi-output radar detection, but also provide key technical support for practical system design, and are fundamental work for radar target detection in complex environments. By solving the point target detection problem in Gaussian noise, this study lays a methodological foundation for subsequent research on complex scenarios such as anti-clutter and anti-interference, and has significant theoretical and practical 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 radars. It is suitable for small sample scenarios, especially for problems where the interference covariance matrix and target parameters (amplitude, range) are unknown, significantly improving robustness.

[0096] Figure 6 This is the method of this embodiment (actually, the detector that applies the method described in this embodiment). Figure 6 The blue curve in the image) is similar to the existing 1S-GLRT-based detector ( Figure 6 The black curve in the diagram illustrates the detection probability under different signal-to-interference-plus-noise ratios (SNRs). It can be seen that, under suitable parameters, the method in this embodiment has a higher detection probability than the existing 1S-GLRT detector.

[0097] Example 3:

[0098] like Figure 7The diagram shown is an architectural schematic of an apparatus for a multi-input multi-output (MIMO) radar target detection method based on an optimized frequency diversity array, according to an embodiment of the present invention. The apparatus for this optimized frequency diversity array MIMO radar target detection method includes one or more processors 21 and a memory 22. Figure 7 Take a processor 21 as an example.

[0099] 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.

[0100] 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 optimized frequency diversity array multi-input multi-output radar target detection method in Embodiment 1. The processor 21 executes the optimized frequency diversity array multi-input multi-output radar target detection method by running the non-volatile software programs and instructions stored in the memory 22.

[0101] Memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, memory 22 may optionally include memory remotely located relative to processor 21, which can be connected to processor 21 via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0102] The program instructions / modules are stored in the memory 22. When executed by one or more processors 21, they execute the optimized frequency diversity array multi-input multi-output radar target detection method described in Embodiment 1 above.

[0103] It is worth noting that the information interaction and execution process between the modules and units in the above-mentioned device and system are based on the same concept as the processing method embodiment of the present invention. For details, please refer to the description in the method embodiment of the present invention, and will not be repeated here.

[0104] Those skilled in the art will understand that all or part of the steps in the various methods of the embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc.

[0105] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An optimization-based frequency diversity array multiple-input multiple-output radar target detection method, characterized in that, include: Constructing detection statistics ; wherein, is a sampled covariance matrix, is a data matrix of the unit to be detected, denotes a conjugate transpose, is a joint transmit-receive steering vector, is an azimuth angle of a far-field point source, is a target incremental time delay; According to the detection statistics Construct a semidefinite programming optimization model; The optimal value of the detection statistic is obtained by solving the optimization model. The optimal value of the detection statistic is compared with the detection threshold to detect whether the target exists. The optimization model is as follows: ; in, express 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. , 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.

2. The target detection method based on optimized frequency diversity array multi-input multi-output radar according to claim 1, characterized in that, The sampling covariance matrix , For the training sample matrix, This indicates the number of training samples.

3. The target detection method based on optimized frequency diversity array multi-input multi-output radar according to claim 1, characterized in that, 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.

4. The target detection method based on optimized frequency diversity array multi-input multi-output radar according to claim 1, 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.

5. The target detection method based on optimized frequency diversity array multi-input multi-output radar according to claim 1, characterized in that, Solving the optimization model to obtain the optimal value of the detection statistic specifically includes: The optimization model is solved using the convex optimization method. Assume and The optimal value of the detection statistic is assumed, and the test is run several times to obtain the vector of optimal detection statistic values. and ; 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. .

6. The target detection method based on optimized frequency diversity array multi-input multi-output radar according to claim 5, characterized in that, The detection threshold is ;in, , This indicates that the data is rounded to the nearest integer. PFA represents the preset false alarm probability. This indicates that the data is sorted in descending order.

7. The target detection method based on optimized frequency diversity array multi-input multi-output radar according to claim 5, characterized in that, The step of comparing the optimal value of the detection statistic with the detection threshold to detect whether the target exists specifically includes: If the detection statistic is optimal Greater than or equal to the detection threshold If so, then the target is determined to exist; If the detection statistic is optimal Less than the detection threshold If the target does not exist, then it is determined that the target does not exist.

8. The target detection method based on optimized frequency diversity array multi-input multi-output radar according to claim 1, characterized in that, Data matrix of the unit to be detected and training sample matrix It is pre-built, specifically including: Construct the data matrix of the unit to be detected ;in, Represents the complex amplitude of the target signal. express Gaussian interference noise vector, 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, For the target incremental delay, , Indicates the radar baseband bandwidth. This is the joint transmit / receive steering vector.

9. The target detection method based on optimized frequency diversity array multi-input multi-output radar according to claim 8, 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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