A target detection method, system, device and medium of a MIMO radar

Abstract

The present application relates to the technical field of radar detection, and particularly relates to a target detection method, system, device and medium of MIMO radar. The method comprises the following steps: acquiring a transmitting waveform set composed of transmitting waveforms of multiple transmitting antennas, a mismatch filter vector set composed of receiving vectors of multiple mismatch filters and a main lobe loss in the MIMO radar; under the constraints of the amplitude of the transmitting waveform, the receiving vector of the mismatch filter and the main lobe loss, establishing a total optimization problem with the minimum ISL value of radar signal GCAF as the target; adopting an MBI algorithm to decompose the total optimization problem into a first sub-optimization problem and a second sub-optimization problem; adopting an SCA algorithm to convert the first sub-optimization problem and the second sub-optimization problem into a first convex optimization problem and a second convex optimization problem; and solving the first convex optimization problem and the second convex optimization problem to obtain the target amplitude of the transmitting waveform and the target receiving vector of the mismatch filter in the radar, so as to improve the radar detection precision.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of radar signal processing, in particular to a target detection method, system, device and medium of MIMO radar. BACKGROUND

[0002] Waveform design has important application value in many fields such as radar and wireless communication. Traditional phased array radar can only transmit the same waveform through multiple antennas, while multiple input multiple output (MIMO) radar as a new system radar can transmit different waveforms independently through different antennas. If the transmitted waveforms meet the orthogonality, the system can obtain a larger virtual aperture, thereby improving the detection performance, parameter identification performance and resolution of the system, and therefore the orthogonal waveform design of MIMO radar has been widely concerned in recent years.

[0003] The main purpose of MIMO radar orthogonal waveform design is to suppress the autocorrelation sidelobe level and cross-correlation level of the waveform. The lower the autocorrelation sidelobe level, that is, the less relevant the waveform is to the waveform after time shift, the better the target resolution capability and synchronization performance of the system will be; the lower the cross-correlation level means the smaller the correlation between waveforms, which can improve the parameter estimation performance and suppress the mutual interference of the system. At the same time, good autocorrelation / cross-correlation can also ensure that the radar receiving filter can more easily separate the transmitting degrees of freedom from the echo signal.

[0004] Among them, the commonly used optimization criteria for measuring autocorrelation / cross-correlation are integrated sidelobe level (ISL), peak sidelobe level (PSL), weighted peak sidelobe level (WPSL) and weighted integrated sidelobe level (WISL). Based on the above criteria, the MIMO radar orthogonal waveform design problem is modeled, and waveforms with good autocorrelation and cross-correlation characteristics can be solved.

[0005] However, when modeling the above problem, the radar transmitting sequence set design is usually considered, and the radar receiving filter design is not considered, resulting in insufficient degrees of freedom of the radar and low detection accuracy. SUMMARY

[0006] The purpose of the present application is to provide a target detection method, system, device and medium of MIMO radar, which can improve the accuracy of radar target detection.

[0007] To address the aforementioned technical problems, embodiments of the present invention provide a target detection method for MIMO radar, comprising the following steps:

[0008] Acquire the transmitted waveform set consisting of the transmitted waveforms from multiple transmit antennas, the mismatched filter vector set consisting of the received vectors from multiple mismatched filters, and the main lobe loss in the MIMO radar;

[0009] Under the constraints of the amplitude of the transmitted waveform, the receiver vector of the mismatch filter, and the main lobe loss, a total optimization problem is established with the goal of minimizing the ISL value of the generalized mutual ambiguity function GCAF of the MIMO radar signal.

[0010] The MBI algorithm is used to solve the overall optimization problem. In each iteration of the MBI algorithm, by fixing the variables in the overall optimization problem except for the amplitude of the transmitted waveform, or by fixing the variables in the overall optimization problem except for the received vector of the mismatch filter, the overall optimization problem is decomposed into a first sub-optimization problem that optimizes the received vector of the mismatch filter with the objective of minimizing the ISL value of the MIMO radar signal GCAF, and a second sub-optimization problem that optimizes the amplitude of the transmitted waveform with the objective of minimizing the ISL value of the MIMO radar signal GCAF.

[0011] The SCA algorithm is used to solve the first sub-optimization problem and the second sub-optimization problem respectively. In each iteration of the SCA algorithm, the non-convex objective function and non-convex constraints in the first and second sub-optimization problems are replaced by a preset convex approximation function, thereby transforming the first and second sub-optimization problems into the first convex optimization problem and the second convex optimization problem respectively.

[0012] Solve the first and second convex optimization problems respectively to obtain the target amplitude of the transmitted waveform and the target receiving vector of the mismatched filter in the MIMO radar, so that the transmitting antenna and the mismatched filter of the MIMO radar can jointly perform target detection with the target amplitude and the target receiving vector respectively.

[0013] Optionally, the GCAF of the MIMO radar signal is:

[0014] ;

[0015] In the formula, Indicates the first The set of transmitted waveforms from each transmitting antenna. Indicates the first The mismatch filter vector set for each receiving antenna. Indicates the length of the transmitted waveform set. Represents the Doppler frequency shift vector set, Represents the set of delays. N -1=K

[0016] Simplify the expression as:

[0017]

[0018] In the formula, represents the conjugate transpose operation, represents a diagonal matrix composed of vectors , and respectively represent the Doppler shift vector and the matrix of time delay;

[0019]

[0020]

[0021] The total optimization problem is represented by the following formula:

[0022]

[0023] In the formula, C1, C2 and C3 respectively represent the main lobe loss constraint, the amplitude constraint of the transmit waveform, and the receive vector constraint of the mismatched filter, represents the preset main lobe gain coefficient, represents the main lobe set, represents the fluctuation degree of the transmit waveform amplitude, represents all the side lobe sets of the local GCAF.

[0024] Optionally, the first sub-optimization problem and the second sub-optimization problem are respectively represented by the following formula:

[0025]

[0026] .

[0027] Optionally, the solving of the first sub-optimization problem and the second sub-optimization problem respectively includes:

[0028] Obtaining the first-order Taylor expansion of three concave functions in the first sub-optimization problem and the second sub-optimization problem , and ;

[0029] Substituting the first-order Taylor expansion of the three concave functions into the first sub-optimization problem and the second sub-optimization problem respectively, so as to convert the first sub-optimization problem and the second sub-optimization problem into the first convex optimization problem and the second convex optimization problem respectively.

[0030] ​​​​​​Optionally, the first and second convex optimization problems are represented by the following equations, respectively:

[0031] ;

[0032] .

[0033] Optionally, the first convex optimization problem is solved by the following steps:

[0034] For the first convex optimization problem, auxiliary variables , and are introduced, and the first convex optimization problem is equivalent to:

[0035] ;

[0036] The augmented Lagrangian function of the equivalent first convex optimization problem is:

[0037] ;

[0038] In the equation, λ and μ represent the Lagrange multiplier vector and the penalty factor, respectively;

[0039] Based on each term in , the optimization problem about , the optimization problem about , the optimization problem about , and the optimization problem about are obtained:

[0040] ;

[0041] In the equation, , , ;

[0042] ;

[0043] In the equation, , ;

[0044] ;

[0045] In the equation, ;

[0046] ;

[0047] In the equation, ;​​

[0048] Based on the ADPM algorithm, the optimization problem about , the optimization problem about , the optimization problem about and the optimization problem about are solved, so that the penalty factor in the ADPM algorithm is dynamically updated in each iteration, and the penalty term gradually approaches zero.

[0049] Embodiments of the present application also provide a target detection system of a MIMO radar, comprising:

[0050] A parameter acquisition module is configured to acquire a set of transmission waveforms composed of transmission waveforms of a plurality of transmission antennas, a set of mismatch filter vectors composed of reception vectors of a plurality of mismatch filters, and a main lobe loss in the MIMO radar.

[0051] A problem establishing module is configured to establish, under the constraints of the amplitudes of the transmission waveforms, the reception vectors of the mismatch filters and the main lobe loss, a total optimization problem with the minimum ISL value of the MIMO radar signal GCAF as the target.

[0052] A problem decomposition module is configured to solve the total optimization problem by using the MBI algorithm, and in each iteration of the MBI algorithm, by fixing the variables in the total optimization problem except the amplitudes of the transmission waveforms or fixing the variables in the total optimization problem except the reception vectors of the mismatch filters, the total optimization problem is decomposed into a first sub-optimization problem with the minimum ISL value of the MIMO radar signal GCAF as the target and the reception vectors of the mismatch filters being optimized, and a second sub-optimization problem with the minimum ISL value of the MIMO radar signal GCAF as the target and the amplitudes of the transmission waveforms being optimized.

[0053] A problem transformation module is configured to solve the first sub-optimization problem and the second sub-optimization problem by using the SCA algorithm, and in each iteration of the SCA algorithm, by using a preset convex approximation function to replace the non-convex objective function and the non-convex constraint in the first sub-optimization problem and the second sub-optimization problem, the first sub-optimization problem and the second sub-optimization problem are respectively transformed into a first convex optimization problem and a second convex optimization problem.

[0054] A problem solving module is configured to solve the first convex optimization problem and the second convex optimization problem respectively, to obtain the target amplitudes of the transmission waveforms and the target reception vectors of the mismatch filters in the MIMO radar, so that the transmission antennas and the mismatch filters of the MIMO radar jointly perform target detection with the target amplitudes and the target reception vectors respectively.

[0055] The embodiment of the present application also provides a computer device, comprising: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory has stored instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the target detection method of the MIMO radar.

[0056] The embodiment of the present application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the target detection method of the MIMO radar.

[0057] The target detection method of the MIMO radar provided by the present application has at least the following beneficial effects:

[0058] By establishing a total optimization problem with the minimum ISL value of the MIMO radar signal GCAF as the target under the constraints of the amplitude of the MIMO radar transmitting waveform, the receiving vector of the mismatch filter and the main lobe loss, and solving the total optimization problem, the transmitting antenna and the mismatch filter of the MIMO radar perform target detection with the solved target amplitude and target receiving vector respectively. The total optimization problem not only considers the design of the radar transmitting sequence set and the receiving filter at the same time, improves the degree of freedom of the radar, but also optimizes the waveform in the MIMO radar, realizes a lower ISL value and a controllable waveform peak-to-average ratio, and effectively improves the accuracy of the target detection of the MIMO radar. BRIEF DESCRIPTION OF DRAWINGS

[0059] One or more embodiments are illustrated by way of example in the figures that form a part of this patent document, these illustrative examples do not limit the embodiments in any way.

[0060] Figure 1 is a flow chart of a target detection method of a MIMO radar according to an embodiment of the present application;

[0061] Figure 2 is a curve graph of the ISL value of different methods varying with the iteration number according to an embodiment of the present application;

[0062] Figure 3 is an ISL three-dimensional graph of a range Doppler unit obtained by a MBI-SCA method according to an embodiment of the present application;

[0063] Figure 4 is an ISL three-dimensional graph of a range Doppler unit obtained by a MBI-ADPM method according to an embodiment of the present application;

[0064] Figure 5 is a zero Doppler two-dimensional graph of different methods according to an embodiment of the present application. DETAILED DESCRIPTION

[0065] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the embodiments of the present application will be described in detail below with reference to the drawings. However, it should be understood that, in the embodiments of the present application, many technical details are presented in order to make the present application better understood by the readers. However, the technical solutions claimed by the present application can be implemented even without these technical details and based on various changes and modifications of the following embodiments. The division of the following embodiments is for the convenience of description, and should not constitute any limitation on the specific implementation of the present application, and the embodiments can be combined with each other and cited to each other without contradiction.

[0066] One embodiment of the present application relates to a target detection method of a MIMO radar, and the implementation details of the target detection method of the MIMO radar of the present embodiment will be described in detail below. The following details are provided for the convenience of understanding, and are not essential for implementing the present solution.

[0067] The specific process of the target detection method of the MIMO radar of the present embodiment can be as shown in Figure 1 , which includes:

[0068] Step 101: Obtain a set of transmission waveforms composed of transmission waveforms of a plurality of transmitting antennas, a set of mismatch filter vectors composed of receiving vectors of a plurality of mismatch filters, and a main lobe loss in the MIMO radar.

[0069] Step 102: Under the constraints of the amplitudes of the transmission waveforms, the receiving vectors of the mismatch filters, and the main lobe loss, establish a total optimization problem with the minimum ISL value of the generalized cross ambiguity function (GCAF) of the MIMO radar signal as the target.

[0070] Specifically, it is assumed that the number of transmitters and receivers (i.e., transmitting antennas and receiving antennas) of the MIMO radar system (which can be a distributed MIMO radar or a centralized MIMO radar) is M. denotes the set of transmission waveforms of the i-th transmitting antenna, denotes the set of transmission waveforms of the i-th transmitting antenna, denotes the set of mismatch filter vectors of the i-th receiving antenna, wherein denotes the length of the set of transmission waveforms.

[0071] At this time, the discrete GCAF of the MIMO radar signal is defined as:

[0072] ;

[0073] In the formula, denotes the set of Doppler shift vectors, ​denotes a set of time delays, N -1= K .

[0074] For simplicity of representation, it is expressed in matrix form as:

[0075] ;

[0076] wherein, denotes a conjugate transpose operation, denotes a diagonal matrix composed of vectors , and denote a Doppler shift vector and a matrix of time delays, respectively.

[0077] That is,

[0078] ;

[0079] .

[0080] The embodiment considers the joint design of the set of transmitted waveforms and the mismatched filter bank, i.e., the non-matched filtering system. Compared with the matched filtering system, the mismatched filtering can obtain a lower sidelobe level at the cost of slight main lobe loss. Therefore, the main lobe loss caused by the mismatched filter needs to be considered in the waveform design process, and the main lobe loss can be controlled by the following formula:

[0081] ;

[0082] wherein, is a main lobe loss coefficient, and the value range is , denotes a set of main lobes.

[0083] Secondly, considering the engineering implementation, the amplitude of the transmitted waveform needs to be limited within a certain range to control the peak-to-average ratio of the waveform, and the amplitude dynamic range constraint of the transmitted sequence can be expressed as:

[0084] ;

[0085] wherein, denotes the fluctuation degree of the waveform amplitude.

[0086] In addition, considering the limited energy of the receiving filter, a total energy constraint is imposed on it, i.e.,

[0087] .

[0088] Finally, due to the fact that the Doppler shift of the target is much lower than the signal bandwidth, and the isovolume property of the Generalized Cross Ambiguity Function (GCAF), the level of the whole range-Doppler cell cannot be reduced simultaneously, but only the local range-Doppler region of interest can be optimized , which is defined as , ,

[0089] Based on the above, in the context of fast-time waveform design, the embodiment adopts the ISL minimization criterion to jointly design the transmit sequence set and the mismatched filter bank, and the total optimization problem can be modeled as

[0090]

[0091] wherein denotes the set of all side lobes of the local GCAF, and .

[0092] In step 103, the MBI algorithm is adopted to solve the total optimization problem, and in each iteration of the MBI algorithm, by fixing the variables in the total optimization problem except the amplitudes of the transmit waveform, or fixing the variables in the total optimization problem except the receive vector of the mismatched filter, the total optimization problem is decomposed into a first sub-optimization problem of optimizing the receive vector of the mismatched filter with the minimum ISL value of the MIMO radar signal GCAF as the target, and a second sub-optimization problem of optimizing the amplitudes of the transmit waveform with the minimum ISL value of the MIMO radar signal GCAF as the target.

[0093] Specifically, the objective function of the total optimization problem is a non-convex fourth-order polynomial function, and both C1 and C2 in the objective function are non-convex constraints, so the total optimization problem is a typical HOP non-convex optimization problem. It is difficult to directly solve the HOP non-convex optimization problem. However, if or , the objective function is a quadratic convex function with respect to and , and based on this, the embodiment will solve it based on the MBI algorithm framework. The MBI algorithm can well solve the HOP optimization problem, and is especially suitable for scenarios where the problem variables can be naturally divided into multiple blocks. This method fixes other variable blocks in each iteration, only optimizes a certain variable block, and selects the block that improves the objective function the most to update, thereby gradually improving the quality of the solution.

[0094] Based on this idea, in , except for or​​ other variables, represents the MBI iteration number, and solves the corresponding optimization problem. The optimization problems related to the first and second sub-problems can be respectively represented as:

[0095]

[0096]

[0097] For convenience, the objective function is represented as After obtaining the solutions of the above two equations and , the corresponding objective function values are calculated, and the or corresponding to the minimum objective function value is updated , , , , or , and the process is repeated until the stopping condition is met.

[0098] The process of solving the total optimization problem by the MBI algorithm is shown in Table 1:

[0099] Table 1

[0100]

[0101] In step 104, the SCA algorithm is used to solve the first and second sub-problems. In each iteration of the SCA algorithm, a predetermined convex approximation function is used to replace the non-convex objective function and non-convex constraints in the first and second sub-problems, and the first and second sub-problems are respectively converted into the first and second convex optimization problems.

[0102] Specifically, since the first two constraints in the first sub-problem and the first constraint in the second sub-problem are non-convex and cannot be directly solved, the SCA algorithm is used to solve the first and second sub-problems. SCA is an iterative algorithm for solving non-convex optimization problems. The basic idea of this algorithm is to replace the non-convex objective function or constraint function with an appropriate convex approximation function in each iteration, and then iteratively solve a series of easily solvable convex sub-problems to gradually approach the global or local optimal solution of the original problem.

[0103] In a specific implementation, the first and second sub-problems are obtained , and ​​​​the first-order Taylor expansion of the three concave functions; the first-order Taylor expansion of the three concave functions is substituted into the first sub-optimization problem and the second sub-optimization problem respectively, so as to convert the first sub-optimization problem and the second sub-optimization problem into the first convex optimization problem and the second convex optimization problem respectively.

[0104] That is, the constraints in the first sub-optimization problem , and the constraints in the second sub-optimization problem are non-convex, and for this purpose, the three constraints can be approximated by using the corresponding convex upper bound functions (first-order Taylor expansion) to convert the non-convex problem into a convex problem.

[0105] Specifically, let x k and x k+1 represent the solution of the k th iteration and the (k+1) th iteration respectively, then the first-order Taylor expansion of the three concave functions is as follows: i , and .

[0106]

[0107]

[0108]

[0109] Substitute the above formulas into the first sub-optimization problem and the second sub-optimization problem, and the convexized sub-problems (i.e. the first convex optimization problem and the second convex optimization problem) are as follows:

[0110]

[0111]

[0112] In step 105, the first convex optimization problem and the second convex optimization problem are solved respectively to obtain the target amplitude of the transmit waveform and the target receiving vector of the mismatched filter in the MIMO radar, so that the transmit antenna and the mismatched filter of the MIMO radar perform target detection jointly with the target amplitude and the target receiving vector respectively.

[0113] Specifically, the two convex problems (i.e. the first convex optimization problem and the second convex optimization problem) can be directly solved by using the interior point method. Once x k+1 and x k+2 are obtained, replace x k with x k+1 and x k+2, and repeat the process until the stopping condition is met.

[0114] Wherein, the process of solving the first sub-optimization problem and the second sub-optimization problem by using the SCA algorithm is shown in Table 2:​​​​​​​​​​​​

[0115] Table 2

[0116]

[0117] In this embodiment, by establishing a total optimization problem with the minimum ISL value of the MIMO radar signal GCAF as the target under the constraints of the amplitude of the MIMO radar transmitting waveform, the receiving vector of the mismatched filter and the main lobe loss, and solving the total optimization problem, the transmitting antenna and the mismatched filter of the MIMO radar are respectively used for target detection with the target amplitude and the target receiving vector obtained by solving. The total optimization problem not only considers the design of the radar transmitting sequence set and the receiving filter at the same time, improves the degree of freedom of the radar, but also optimizes the waveform in the MIMO radar, realizes a lower ISL value and a controllable waveform peak-to-average ratio, and effectively improves the accuracy of target detection of the MIMO radar.

[0118] In one embodiment, since the interior point algorithm is used to solve the first convex optimization problem and the second convex optimization problem in step 104 of the above embodiment, the operation complexity is about , which is relatively large when N is large. At this time, this embodiment will propose a fast algorithm based on ADPM to solve the first convex optimization problem and the second convex optimization problem.

[0119] In specific implementation, compared with the traditional ADMM algorithm, the penalty factor of the ADPM algorithm can be dynamically updated in each iteration, so that the penalty term gradually approaches zero, ensuring the convergence of the algorithm, and enabling the algorithm to find a better feasible solution faster.

[0120] Therefore, based on the ADPM algorithm framework, the first convex optimization problem is equivalent to:

[0121] .

[0122] The augmented Lagrangian function of the equivalent first convex optimization problem is:

[0123] ;

[0124] In the formula, λ is the Lagrange multiplier vector, and ρ is the penalty factor. and ρ respectively represent the Lagrange multiplier vector and the penalty factor.

[0125] The optimization result of the k-th iteration is represented by , and the optimization step is as follows: Step 1, update :

[0126] Neglecting

[0127] , the optimization result of the k-th iteration is represented by ​The optimization problem about , which is irrelevant to , can be simplified as:

[0128] ;

[0129] where , , .

[0130] The KKT conditions of this equation are:

[0131] ;

[0132] The solution of the optimization problem about can be obtained as:

[0133] ;

[0134] where .

[0135] Step 2, update :

[0136] Ignoring the terms in that are irrelevant to , the optimization problem about is:

[0137] ;

[0138] where , .

[0139] It can be found that the objective function and the constraint condition of this problem are separable for , that is, the variables in this problem can be optimized in parallel. For the th element in , we can get:

[0140] ;

[0141] Similar to the optimization problem about , the closed-form solution of this equation is:

[0142] ;

[0143] where .

[0144] Step 3, update :

[0145] Neglect with respect to the optimization problem is:

[0146] ;

[0147] where .

[0148] Similarly, the objective function and constraints of the problem are separable with respect to , for the th element in , we have:

[0149] ;

[0150] The closed-form solution is

[0151] .

[0152] Step 4, update :

[0153] Neglect with respect to the optimization problem is:

[0154] ;

[0155] where .

[0156] Further simplifying it, we have:

[0157] ;

[0158] where .

[0159] This is an unconstrained problem, and the solution is obtained by taking the first derivative of with respect to and setting it equal to 0:

[0160] .

[0161] Updating the solution requires an inverse operation, to reduce the computational load, we can use the properties of eigenvalue decomposition to avoid directly inverting at each iteration, thus reducing the computational complexity.

[0162] The specific principle is as follows:

[0163] If , the singular value decomposition of ;

[0164] where is the eigenvector matrix, is the eigenvalue diagonal matrix.

[0165] Thus:

[0166] .

[0167] Because and are kept constant during the iteration of ADPM algorithm, they can be obtained in advance before the iteration of ADPM starts, and the subsequent update only needs to solve inverse. Therefore, if the maximum number of iterations is , the computational complexity can be reduced from to .

[0168] Step 5, update and :

[0169] ADPM algorithm updates based on the original residual value . If does not decrease with the increase of the number of iterations, then is increased to force to approach 0, so as to find a feasible solution. Otherwise, remains unchanged. Therefore is expressed as:

[0170] ;

[0171] where , , .

[0172] The Lagrange multiplier vector can be updated as follows:

[0173] ;

[0174] where , is a large enough positive number.

[0175] Iterative update , , , until the stopping condition is reached.

[0176] A reasonable stopping condition is that both the original residual and the dual residual are small enough, that is:

[0177] ; ;

[0178] where, and are the feasibility tolerance of the primary and dual residual of the th iteration, which can be selected by absolute and relative criteria, i.e.,

[0179] ;

[0180] ;

[0181] where, is the absolute error, is the relative error.

[0182] The ADPM iteration terminates when both of the above termination criteria are satisfied.

[0183] Suitable can make the ADPM algorithm converge faster, and the proper initial penalty factor is given by:

[0184] ;

[0185] where, and are the minimum and maximum eigenvalue of , respectively. If the minimum eigenvalue of is 0, the minimum non-zero eigenvalue of is selected.

[0186] The process of solving the first convex optimization problem by the ADPM algorithm can be found in Table 3:

[0187] Table 3

[0188]

[0189] Similarly, introduce auxiliary variable , , and equivalently transform the second convex optimization problem as:

[0190] ;

[0191] The augmented Lagrangian function of the equivalently transformed second convex optimization problem is:

[0192] .

[0193] Since the structure of this formula is similar to that of the augmented Lagrangian function of the equivalently transformed first convex optimization problem, the same ADPM algorithm can be used to solve it, and the specific steps are not repeated here.

[0194] The calculation complexity of the embodiment mainly relates to the iteration number of the inner and outer layer algorithms and the sequence length . The calculation complexity of the inner loop SCA algorithm is , the calculation complexity of the ADPM algorithm is , assuming that the maximum iteration number of the outer layer MBI algorithm when converging is , then the calculation complexity of the overall MBI-SCA and MBI-ADPM algorithms is , It can be seen that the complexity of the latter is generally lower than that of the former.

[0195] In the embodiment, (1) a problem model of minimizing GCAF autocorrelation and cross-correlation ISL under the constraints of main lobe gain and dynamic range is established. The model can not only provide a lower ISL value, but also control the peak-to-average ratio of the waveform, so that the designed waveform can be better applied to the actual MIMO system.

[0196] (2) For the non-convex problem of HOP, the MBI_SCA method is proposed. By using the MBI algorithm, the non-convex problem is decomposed into multiple parallel sub-problems, and the SCA algorithm is used to iteratively solve the sub-problems.

[0197] (3) In order to reduce the operation amount, the ADPM fast algorithm with parallel implementation potential is proposed to solve the sub-problems of SCA, which ensures the convergence while improving the operation efficiency, and can obtain a lower ISL in some cases.

[0198] In one embodiment, the effectiveness of the method proposed in the above embodiment can be verified by MATLAB numerical simulation.

[0199] Among them, the simulation parameter settings are as follows:

[0200] The initial transmission sequence and the receiving filter used in the experiment are all all-1 sequences. The number of transmitting and receiving antennas , the sequence length , the distance-Doppler unit , , , , , . The maximum iteration number of the outer layer MBI algorithm is , the maximum iteration number of the inner layer SCA algorithm is , and the maximum iteration number of the ADPM algorithm is .

[0201] The simulation data results and analysis are as follows:

[0202] Figure 2are the graphs of the objective function values of MBI-SCA and MBI-ADPM algorithms changing with the iteration number. Figure 2 It can be seen that the MBI-SCA algorithm converges after about 12 iterations, while the MBI-ADPM algorithm converges after about 10 iterations, has a faster convergence speed, and can achieve a lower objective function value .

[0203] Figure 3 is the ISL three-dimensional graph of the range Doppler unit obtained by the MBI-SCA method, Figure 4 is the ISL three-dimensional graph of the range Doppler unit obtained by the MBI-ADPM method, the top is , , and the bottom is , . From Figure 3 , Figure 4 It can be seen that both algorithms have good optimization ability, making the ISL value of the local optimization area much lower than that of the unoptimized area, and both are less than -200dB;

[0204] Figure 5 is the zero Doppler two-dimensional graph. From Figure 5 It can be seen that the MBI-ADPM has better local optimization ability than the MBI-SCA algorithm, and can make the local GCAFs value reach -320dB, while the MBI-SCA can only reach -260dB.

[0205] The step division of the above various methods is only for clear description, and can be combined into one step or split into multiple steps in implementation, as long as the same logical relationship is included, and it is within the protection scope of the present application; adding irrelevant modifications or introducing irrelevant designs in the algorithm or flow, but not changing the core design of the algorithm and flow are within the protection scope of the present application.

[0206] Another embodiment of the present application relates to a target detection system of a MIMO radar, and the implementation details of the target detection system of the MIMO radar of the present embodiment will be specifically described below. The following content is only provided for the implementation details for the convenience of understanding, and is not necessary for implementing the present solution. The target detection system of the MIMO radar of the present embodiment comprises:

[0207] The parameter acquisition module is configured to acquire a transmit waveform set composed of transmit waveforms of a plurality of transmit antennas, a mismatch filter vector set composed of receive vectors of a plurality of mismatch filters, and a main lobe loss in the MIMO radar.

[0208] The problem establishing module is configured to establish a total optimization problem with a minimum ISL value of a generalized cross ambiguity function (GCAF) of a MIMO radar signal as a target under constraints of amplitudes of transmit waveforms, receive vectors of mismatched filters, and main lobe loss.

[0209] The problem decomposition module is configured to solve the total optimization problem by using an MBI algorithm, and in each iteration of the MBI algorithm, the total optimization problem is decomposed into a first sub-optimization problem of optimizing the receive vectors of the mismatched filters with the minimum ISL value of the GCAF of the MIMO radar signal as a target and a second sub-optimization problem of optimizing the amplitudes of the transmit waveforms with the minimum ISL value of the GCAF of the MIMO radar signal as a target by fixing variables in the total optimization problem except the amplitudes of the transmit waveforms or fixing variables in the total optimization problem except the receive vectors of the mismatched filters.

[0210] The problem transformation module is configured to solve the first sub-optimization problem and the second sub-optimization problem by using an SCA algorithm, and in each iteration of the SCA algorithm, the first sub-optimization problem and the second sub-optimization problem are respectively transformed into a first convex optimization problem and a second convex optimization problem by using a preset convex approximation function to replace non-convex objective functions and non-convex constraints in the first sub-optimization problem and the second sub-optimization problem.

[0211] The problem solving module is configured to solve the first convex optimization problem and the second convex optimization problem respectively to obtain target amplitudes of the transmit waveforms and target receive vectors of the mismatched filters in the MIMO radar, so that the transmit antennas and the mismatched filters in the MIMO radar perform target detection jointly with the target amplitudes and the target receive vectors.

[0212] It can be found that the embodiment corresponds to the method embodiment described above, and the embodiment can be implemented in cooperation with the method embodiment. The related technical details and technical effects mentioned in the above embodiments are still valid in the embodiment, and to avoid repetition, they will not be described here. Correspondingly, the related technical details mentioned in the embodiment can also be applied to the above embodiments.

[0213] It is worth mentioning that each module involved in the embodiment is a logical module, and in actual application, one logical unit can be one physical unit, or a part of one physical unit, or a combination of multiple physical units. In addition, in order to highlight the innovative part of the application, units not closely related to solving the technical problems proposed in the application are not introduced in the embodiment, but this does not mean that there are no other units in the embodiment.

[0214] Another embodiment of the present application relates to a computer device, comprising: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the low ISL target detection method in the above-mentioned embodiments.

[0215] The memory and the processor are connected by a bus in a bus mode, the bus can include any number of interconnected buses and bridges, and the bus connects various circuits of the one or more processors and the memory together. The bus can also connect various other circuits such as peripheral devices, voltage stabilizers and power management circuits together, which are well known in the art, and thus, further description thereof will not be given herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements such as multiple receivers and transmitters, which provide a unit for communicating with various other devices on the transmission medium. The data processed by the processor is transmitted on the wireless medium through the antenna, and further, the antenna also receives data and transmits the data to the processor.

[0216] The processor is responsible for managing the bus and general processing, and can also provide various functions including timing, peripheral interface, voltage regulation, power management and other control functions. The memory can be used to store data used by the processor in performing operations.

[0217] Another embodiment of the present application relates to a computer readable storage medium, which stores a computer program. The computer program is executed by the processor to implement the method embodiments.

[0218] That is, those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by programs instructing related hardware, the programs are stored in a storage medium, and the storage medium includes a plurality of instructions for enabling a device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk and various storage medium capable of storing program codes.

[0219] Those skilled in the art can understand that the above-mentioned embodiments are specific embodiments for implementing the present application, and in actual applications, various changes can be made in form and details without departing from the spirit and scope of the present application.

Claims

1. A target detection method of a MIMO radar, characterized by, The method comprises: acquiring a transmitting waveform set composed of transmitting waveforms of a plurality of transmitting antennas, a mismatch filter vector set composed of receiving vectors of a plurality of mismatch filters, and a main lobe loss in a MIMO radar; under the constraints of the amplitudes of the transmitting waveforms, the receiving vectors of the mismatch filters, and the main lobe loss, establishing a total optimization problem with the minimum ISL value of a MIMO radar signal generalized cross ambiguity function (GCAF) as a target; solving the total optimization problem by using an MBI algorithm, in each iteration of the MBI algorithm, fixing variables in the total optimization problem except the amplitudes of the transmitting waveforms, or fixing variables in the total optimization problem except the receiving vectors of the mismatch filters, decomposing the total optimization problem into a first sub-optimization problem of optimizing the receiving vectors of the mismatch filters with the minimum ISL value of the MIMO radar signal GCAF as a target, and a second sub-optimization problem of optimizing the amplitudes of the transmitting waveforms with the minimum ISL value of the MIMO radar signal GCAF as a target; solving the first sub-optimization problem and the second sub-optimization problem by using an SCA algorithm, in each iteration of the SCA algorithm, replacing non-convex objective functions and non-convex constraints in the first sub-optimization problem and the second sub-optimization problem by using a preset convex approximation function, and converting the first sub-optimization problem and the second sub-optimization problem into a first convex optimization problem and a second convex optimization problem respectively; solving the first convex optimization problem and the second convex optimization problem respectively to obtain target amplitudes of the transmitting waveforms and target receiving vectors of the mismatch filters in the MIMO radar, so that the transmitting antennas and the mismatch filters in the MIMO radar jointly perform target detection with the target amplitudes and the target receiving vectors respectively; wherein the GCAF of the MIMO radar signal is: ； wherein denotes a set of transmit waveforms for the th transmit antenna, denotes a set of mismatched filter vectors for the th receive antenna, denotes a length of the set of transmit waveforms, denotes a set of Doppler shift vectors, denotes a set of time delays, N -1= K ; a simplified representation is: ； wherein denotes the conjugate transpose operation, denotes a diagonal matrix composed of the vector and denote the Doppler shift vector and the matrix of time delays, respectively;​ ； ； the total optimization problem is represented by the following formula: ； wherein C1, C2 and C3 represent a main lobe loss constraint, a transmit waveform amplitude constraint and a mismatched filter receive vector constraint, respectively, denotes a preset main lobe gain coefficient, denotes a main lobe set, denotes a degree of fluctuation of the transmit waveform amplitude, denotes a set of all side lobes of the local GCAF; the first sub-optimization problem and the second sub-optimization problem are represented by the following formula respectively: ； 。 2. The target detection method of a MIMO radar according to claim 1, wherein the solving of the first sub-optimization problem and the second sub-optimization problem respectively comprises: obtaining a first-order Taylor expansion of three concave functions , and the first and second sub-optimization problems; substituting first-order Taylor expansions of the three concave functions into the first sub-optimization problem and the second sub-optimization problem respectively, so as to convert the first sub-optimization problem and the second sub-optimization problem into the first convex optimization problem and the second convex optimization problem respectively.

3. The target detection method for a MIMO radar according to claim 2, wherein the first convex optimization problem and the second convex optimization problem are represented by the following formula respectively: ； 。 4. The target detection method for a MIMO radar according to claim 3, wherein the first convex optimization problem is solved by the following steps: For the first convex optimization problem, introduce auxiliary variables , and to equivalently transform the first convex optimization problem as ； the augmented Lagrange function of the equivalent first convex optimization problem is: ； wherein and respectively denote the Lagrange multiplier vector and the penalty factor; Based on each item in , an optimization problem about , an optimization problem about , an optimization problem about , and an optimization problem about are obtained: ； In the formula, , , ; ； In the formulae, , ; ； In the formulae, ; ； In the formulae, ; Based on the ADPM algorithm, the optimization problem about , the optimization problem about , the optimization problem about and the optimization problem about are solved respectively, so that the penalty factor in the ADPM algorithm is dynamically updated in each iteration, and the penalty term gradually approaches zero.

5. A target detection system of a MIMO radar, characterized by, The system comprises: a parameter acquisition module configured to acquire a transmitting waveform set composed of transmitting waveforms of a plurality of transmitting antennas, a mismatch filter vector set composed of receiving vectors of a plurality of mismatch filters, and a main lobe loss in a MIMO radar; a problem establishing module configured to, under the constraints of the amplitudes of the transmitting waveforms, the receiving vectors of the mismatch filters, and the main lobe loss, establish a total optimization problem with the minimum ISL value of a MIMO radar signal generalized cross ambiguity function (GCAF) as a target; The problem decomposition module is configured to solve the total optimization problem by using an MBI algorithm, and in each iteration of the MBI algorithm, by fixing variables in the total optimization problem except for amplitudes of the transmit waveform or fixing variables in the total optimization problem except for a receive vector of the mismatched filter, the total optimization problem is decomposed into a first sub-optimization problem of optimizing the receive vector of the mismatched filter with a minimum ISL value of the GCAF of the MIMO radar signal as an objective and a second sub-optimization problem of optimizing the amplitudes of the transmit waveform with the minimum ISL value of the GCAF of the MIMO radar signal as the objective; The problem transformation module is configured to solve the first sub-optimization problem and the second sub-optimization problem by using an SCA algorithm respectively, and in each iteration of the SCA algorithm, by using a preset convex approximation function to replace a non-convex objective function and a non-convex constraint in the first sub-optimization problem and the second sub-optimization problem, the first sub-optimization problem and the second sub-optimization problem are respectively transformed into a first convex optimization problem and a second convex optimization problem; The problem solving module is configured to solve the first convex optimization problem and the second convex optimization problem respectively to obtain target amplitudes of the transmit waveform and a target receive vector of the mismatched filter in the MIMO radar, so that the transmit antenna and the mismatched filter of the MIMO radar jointly perform target detection with the target amplitudes and the target receive vector respectively. The GCAF of the MIMO radar signal is: ； wherein denotes a set of transmit waveforms for the th transmit antenna, denotes a set of mismatched filter vectors for the th receive antenna, denotes a length of the set of transmit waveforms, denotes a set of Doppler shift vectors, denotes a set of time delays, N -1= K ; The total optimization problem is represented by the following formula: ； wherein denotes the conjugate transpose operation, denotes a diagonal matrix composed of the vector and denote the Doppler shift vector and the matrix of time delays, respectively;​ ； ； The first sub-optimization problem and the second sub-optimization problem are respectively represented by the following formula: ； wherein C1, C2 and C3 represent a main lobe loss constraint, a transmit waveform amplitude constraint and a mismatched filter receive vector constraint, respectively, represents a preset main lobe gain coefficient, represents a main lobe set, represents a degree of fluctuation of the transmit waveform amplitude, represents a set of all side lobes of the local GCAF; The method comprises the following steps: ； 。 6. A computer device, comprising: at least one processor; and a memory in communication 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 at least one processor to enable the at least one processor to perform the target detection method of the MIMO radar according to any one of claims 1 to 4. The computer program is executed by the processor to implement the target detection method of the MIMO radar according to any one of claims 1 to 4.

7. A computer readable storage medium storing a computer program, characterized in that, ​

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