MIMO radar multi-target detection method and system based on strong target limitation

Abstract

The invention relates to an MIMO radar multi-target detection method and system based on strong target limitation, and the method comprises the steps: designing a beam weight matrix solving method with strong target limitation on the basis of an MIMO radar multi-target detection method based on reinforcement learning, and taking out a possible strong target from a detection result of an MIMO radar; and calculating a power gain limit value corresponding to each strong target, designing a beam optimization scheme with strong target limitation, and solving by using a convex optimization toolbox to obtain a beam weight matrix. During detection, a radar waveform corresponding to a beam weight matrix is transmitted, state calculation and action selection are carried out after a Wald type statistic of a radar echo is calculated, and then an angle unit needing to be focused in a beam pattern of the MIMO radar is selected; and finally, quickly solving according to a beam weight matrix solving method with strong target limitation to obtain a beam weight matrix at the next detection moment, thereby improving the weak target detection performance of the MIMO radar.

Description

Technical Field

[0001] The invention belongs to the technical field of radar target detection, and relates to a MIMO radar multi-target detection method and system based on strong target restriction. Background Art

[0002] With the promotion and application of automotive radar, wireless communication (such as millimeter wave radar in 5G network) and drones in intelligent scenarios, MIMO (Multiple Input Multiple Output) radar is increasingly used in them. At present, reinforcement learning (RL) has been applied to the multi-target detection task of MIMO radar. Through reinforcement learning, MIMO radar system can adaptively adjust its configuration and operating parameters to cope with complex and dynamically changing environments, thereby achieving more accurate and efficient multi-target detection. This method not only improves the detection performance, but also effectively reduces human intervention and optimizes the use of system resources. In the beam pattern optimized by the existing beam optimization scheme, the power gain is basically consistent in the angle unit that needs to be focused, which leads to its insufficient detection performance for weak targets. Summary of the invention

[0003] In view of the problems existing in the above-mentioned traditional methods, the present invention proposes a MIMO radar multi-target detection method based on strong target limitation, a MIMO radar multi-target detection system based on strong target limitation, a radar device and a computer-readable storage medium, which can effectively improve the detection performance of weak targets.

[0004] In order to achieve the above objectives, the embodiments of the present invention adopt the following technical solutions:

[0005] On the one hand, a MIMO radar multi-target detection method based on strong target restriction is provided, comprising the steps of:

[0006] Initialize the current detection time and the beam weight matrix of the MIMO radar;

[0007] Transmit the radar waveform corresponding to the beam weight matrix and receive the radar echo of each angle unit, and calculate the Wald-type statistics of the radar echo;

[0008] Compute the state of the Markov decision process based on the Wald-type statistics of the radar echo;

[0009] Select an action in a Markov decision process based on the state;

[0010] When the detection time is 0, the angle unit on which the beam pattern of the MIMO radar needs to be focused is selected according to the action;

[0011] According to the beam weight matrix solution method with strong target restriction, the beam weight matrix at the next detection moment is solved based on the angle unit; the beam weight matrix at the next detection moment is used for MIMO radar multi-target detection at the next detection moment.

[0012] On the other hand, a MIMO radar multi-target detection system based on strong target restriction is also provided, comprising:

[0013] Initialization module, used to initialize the current detection time and the beam weight matrix of the MIMO radar;

[0014] The echo calculation module is used to transmit the radar waveform corresponding to the beam weight matrix and receive the radar echo of each angle unit, and calculate the Wald-type statistics of the radar echo;

[0015] A state calculation module is used to calculate the state of the Markov decision process based on the Wald-type statistics of the radar echo;

[0016] An action selection module, which is used to select the action of the Markov decision process according to the state;

[0017] An angle selection module, used to select the angle unit on which the beam pattern of the MIMO radar needs to be focused according to the action when the detection time is 0;

[0018] The weight solving module is used to solve the beam weight matrix at the next detection moment based on the angle unit according to the beam weight matrix solving method with strong target restriction; the beam weight matrix at the next detection moment is used for MIMO radar multi-target detection at the next detection moment.

[0019] On the other hand, a radar device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned MIMO radar multi-target detection method based on strong target restriction are implemented.

[0020] On the other hand, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned MIMO radar multi-target detection method based on strong target restriction are implemented.

[0021] One of the above technical solutions has the following advantages and beneficial effects:

[0022] The above-mentioned MIMO radar multi-target detection method and system based on strong target restriction is further studied on the basis of the MIMO radar multi-target detection method based on reinforcement learning, and a beam weight matrix solution method with strong target restriction is designed. It extracts possible strong targets from the detection results of the MIMO radar, calculates the power gain limit value corresponding to each strong target, designs a beam optimization scheme with strong target restriction, and uses the convex optimization toolbox to solve and obtain the beam weight matrix. For each detection moment, the radar waveform corresponding to the beam weight matrix is ​​transmitted and the radar echo of each angle unit is received. After calculating the Wald-type statistics of the radar echo, state calculation and action selection are performed, and then the angle unit that the beam pattern of the MIMO radar needs to focus on is selected. Finally, according to the beam weight matrix solution method with strong target restriction, the beam weight matrix of the next detection moment is quickly solved based on the angle unit, thereby improving the detection performance of the MIMO radar for weak targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the conventional technology, the drawings required for use in the embodiments or the conventional technology descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 1 is a flow chart of a MIMO radar multi-target detection method based on strong target restriction in one embodiment;

[0025] Figure 2 This is a flowchart of the existing reinforcement learning-based MIMO radar multi-target detection method;

[0026] Figure 3 A schematic flow chart of a beam weight matrix solution method with strong target constraints in one embodiment;

[0027] Figure 4 A comparison of beam patterns obtained by an improved optimization scheme based on convex approximation and an original optimization scheme based on convex approximation in one embodiment;

[0028] Figure 5 It is a schematic diagram of the application process of a MIMO radar multi-target detection method based on strong target restriction in one embodiment;

[0029] Figure 6 A schematic diagram showing a comparison of beam patterns obtained by two beam optimization schemes in an embodiment;

[0030] Figure 7 is a normalized power spectral density function of clutter in one embodiment;

[0031] Figure 8 Schematic diagram of ablation experiment performance comparison in one embodiment, wherein (a) is a detection probability change curve with and without prior information assistance, and (b) is a detection probability change curve with and without strong target restriction;

[0032] Fig. 9 Schematic diagram of comparison of detection performance in one embodiment, wherein (a) is the detection performance of the method proposed in the present invention, and (b) is the detection performance of the classical method in the art;

[0033] Fig.10 Schematic diagram of the module architecture of a MIMO radar multi-target detection system based on strong target restriction in one embodiment. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and Examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0035] It should be noted that the reference to "embodiment" in this article means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The presentation of this phrase at various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It will be appreciated by those skilled in the art that the embodiments described herein may be combined with other embodiments. The term "and / or" used in the specification of the present invention and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0036] The following will describe the implementation of the present invention in detail with reference to the accompanying drawings in the embodiment diagram of the present invention.

[0037] Collocated MIMO Radar is a multiple-input multiple-output (MIMO) radar system with multiple co-located (i.e., antennas are located in the same or close positions) transmit and receive antenna arrays. Unlike traditional radar systems, MIMO radar uses multi-antenna technology to enhance target detection, positioning, and identification capabilities by transmitting different signals in multiple directions. Co-site MIMO radar is widely used in automotive radars (such as vehicle-mounted collision avoidance systems), wireless communications (such as millimeter-wave radars in 5G networks), and drone detection and tracking. By utilizing co-site MIMO technology, radar systems are able to provide higher resolution, stronger multi-target detection capabilities, and higher system robustness. The MIMO radar used in the present invention is a co-site MIMO radar, which means that the transmitting antennas and receiving antennas are closely arranged, and the array element spacing is set to half a wavelength.

[0038] In one embodiment, the direction angle of the target relative to the MIMO radar can be denoted as ψ, and the transmission steering vector a T (ψ), receiving steering vector a R (ψ) can be defined as:

[0039]

[0040] Among them, N T is the number of transmitting antennas of the MIMO radar, N R is the number of receiving antennas of the MIMO radar, [·] T Represents the transpose operation of a matrix or vector. It is generated by s(p) = WΦ(p), where By N T A vector of orthogonal signals, is the beam weight matrix of the MIMO radar, is the radar transmission signal set. The transmission beam pattern B(ψ) of the MIMO radar can be defined as:

[0041]

[0042] in,(·) * represents the conjugate transpose operation of a matrix or vector, (·) H represents the conjugate transpose operation of a matrix or vector. And the trace of the matrix R is tr(R) = P T , P T is the transmission power of the MIMO radar.

[0043] Assume that the direction angle vector θ where the target (detected by radar) may be located is known = {θ 1 ,θ 2 ,…,θK}, the traditional optimization scheme can be described as:

[0044]

[0045] Among them, max means taking the maximum value, min means taking the minimum value, and st means the constraint condition. By introducing auxiliary variables The above traditional optimization scheme can be equivalently transformed into:

[0046]

[0047] Where (i) represents constraint one, (ii) represents constraint two, (iii) represents constraint three, and K represents the total number of azimuth angles in the azimuth angle vector. The observation area of ​​the MIMO radar is evenly divided into L angular units, denoted by ψ 1 ,ψ 2 ,…,ψ L .

[0048] In one embodiment, Figure 1 As shown, a MIMO radar multi-target detection method based on strong target restriction is provided, which may include the following processing steps S10 to S20:

[0049] S10, initializing the current detection time and the beam weight matrix of the MIMO radar;

[0050] S12, transmitting a radar waveform corresponding to the beam weight matrix and receiving radar echoes of each angle unit, and calculating Wald-type statistics of the radar echoes;

[0051] S14, calculating the state of the Markov decision process based on the Wald-type statistics of the radar echo;

[0052] S16, select the action of Markov decision process according to the state;

[0053] S18, when the detection time is 0, selecting an angle unit on which the beam pattern of the MIMO radar needs to be focused according to the action;

[0054] S20, according to the beam weight matrix solution method with strong target restriction, solve the beam weight matrix at the next detection moment based on the angle unit; the beam weight matrix at the next detection moment is used for MIMO radar multi-target detection at the next detection moment.

[0055] It can be understood that in order to more intuitively demonstrate the improvement of the present invention, the process of the existing MIMO radar multi-target detection method based on reinforcement learning is briefly introduced below. The process is actually a Markov decision process (Markov Decision Process, MDP), such as Figure 2 , which is mainly composed of four elements, namely state, strategy, action and reward. And MDP is a sequential process, so Figure 2 Subscripts are used to indicate the time corresponding to the variable t.

[0056] Assume that at time t, the radar receives echoes from L angle units, which are recorded as l=1,2,…,L. Then execute the following steps one at a time:

[0057] Step (1), calculate The Wald-type statistic for l=1,2,…,L is denoted as

[0058]

[0059] in, stands for Kronecker product, W t Represents the beam weight matrix used by the radar at time t. The definition of can be as follows:

[0060]

[0061] represent The element corresponding to the i-th row and j-th column of . Represents round down. Representation vector The i-th element of , and:

[0062]

[0063] Among them, ||·|| 2 Represents the 2-norm of the orientation quantity.

[0064] Step (2), calculate the state s according to the following formula t :

[0065]

[0066] Where min{·} represents the minimum value of the elements in the sequence, H is the maximum number of targets that can be detected by the radar, is the threshold value, which is based on the false alarm rate P set in advance FA get, u(·) represents the step response. If Returns a value of 1, otherwise returns a value of 0. State Space Defined as

[0067] Step (3), select action a according to the ε-greedy strategy t :

[0068]

[0069] in, is the action space, where the action means the number of targets that may be contained in the radar detection area. Q represents the state-value function, which is modeled as a matrix of size (H+1)×H. Q(s,a) represents the value of taking action a in state s.

[0070] Step (4), select the angle unit that the beam pattern needs to focus on according to the following formula:

[0071]

[0072] in, a t argmax(Λ t ) represents the value of Λ t Middle t The angle unit corresponding to the maximum value.

[0073] Step (5), according to θ t Design a beam optimization scheme as shown in equation (5), and use the convex optimization toolbox to solve W t+1 .

[0074] Step (6), calculate the reward r t :

[0075]

[0076] Where ψ={ψ 1 ,ψ 2 ,…,ψ L}, is the angle unit detected by the radar at time t, and:

[0077]

[0078] Among them, Q 1 represents the first-order Malkum function.

[0079] Step (7), jump to step (1), and follow the same steps to obtain s t+1 , a t+1 , r t+1 .

[0080] Step (8), update Q according to the following formula:

[0081] Q(s t ,a t )←Q(st ,a t )+β·(r t+1 +γ·Q(s t+1 ,a t+1 )-Q(s t ,a t ))(14)

[0082] Among them, β,γ∈[0,1] are called learning rate and discount factor respectively.

[0083] On this basis, this embodiment designs a beam weight matrix solution method with strong target constraints, such as Figure 3 As shown, the following processing steps S01 to S05 may be included:

[0084] S01, extracting possible strong targets from the detection results of the MIMO radar.

[0085] It can be understood that when searching for possible strong targets at time t, the set of angle units where the strong targets are located is initialized in the detection results of the MIMO radar. Let k = 1; find θ k The index value in ψ is denoted as l k , that is, θ k =ψ(l k );like It is the detection probability threshold set in advance. If k = K, end the process; otherwise, let k = k + 1 and go to find θ k The index value of the step in ψ.

[0086] S03, calculating the power gain limit value corresponding to each strong target.

[0087] It can be understood that when calculating the power gain limit value corresponding to each strong target at time t, generally, it can be remembered that M≤K. Let m=1 and find θ m The index value in ψ is denoted by c m , that is, θ m =ψ(c m );Calculate θ according to the following formula m The power gain limit

[0088]

[0089] in,

[0090]

[0091]

[0092] also, It can also be obtained by looking up a table. In this embodiment,

[0093] If m=M, end the process; otherwise, let m=m+1 and go to find θ m The index value of the step in ψ.

[0094] S05, design an improved beam optimization scheme with strong target constraints and solve it using the convex optimization toolbox.

[0095] The original beam optimization scheme is of the following form:

[0096]

[0097] in,

[0098] The above solution (i) is a non-convex problem, which can be improved into the following form by the inner convex approximation method:

[0099]

[0100] in,

[0101]

[0102] is of size N T ×N T The identity matrix of , and:

[0103]

[0104] Solution (ii) can be solved directly using the CVX toolbox, but due to f k (W) For W to be a quadratic convex function, we must have Therefore, the power gain of the beam pattern obtained by solution (ii) on the strong target must be higher than the preset value. In order to obtain a more reasonable beam pattern, the following formula is a feasible beam optimization scheme with strong target constraints after improving formula (ii):

[0105]

[0106] Assume that at a certain time t, we get θ t ={ν=-0.4,-0.25,0.05,0.25}, N T =32, P T =1. Figure 4As shown in FIG. 1 , the beam pattern obtained by comparing the improved optimization scheme based on the inner convex approximation (Equation (18)) and the original optimization scheme based on the inner convex approximation (Equation (ii)) is shown. The unit of power gain is dB, and the unit of spatial frequency is (1 / m) or no unit (after normalization). The same is true for the other figures below. Obviously, the beam optimization scheme corresponding to Equation (18) is more reasonable.

[0107] Then, the convex optimization toolbox (i.e., the CVX toolbox in MATLAB) is used to solve equation (18) to obtain the beam weight matrix W t .

[0108] Based on the above beam weight matrix solution method with strong target restriction, the above MIMO radar multi-target detection method based on strong target restriction provided in this embodiment can be specifically as follows: Figure 5 As shown, the following processing steps may be included:

[0109] S10, initializing the current detection time t=0 and the beam weight matrix of the MIMO radar

[0110] S12, transmit beam weight matrix W t Corresponding radar waveform, and receiving radar echoes from each angle unit l = 1, 2, ..., L, and the Wald statistics of radar echo are calculated according to formula (6):

[0111] S14, calculate the state s of the Markov decision process according to the Wald-type statistics of the radar echo according to equation (9): t ;

[0112] S16, select action a of the Markov decision process according to formula (10) based on the state t ;

[0113] S18: If the detection time t = 0, select the angle unit θ where the beam pattern needs to be focused according to equation (11): t .

[0114] S20, solving the beam weight matrix W at the next detection time according to the beam weight matrix solution method with strong target restriction t+1 .

[0115] The above-mentioned MIMO radar multi-target detection method based on strong target restriction is further studied on the basis of the MIMO radar multi-target detection method based on reinforcement learning, and a beam weight matrix solution method with strong target restriction is designed. It extracts possible strong targets from the detection results of the MIMO radar, calculates the power gain limit value corresponding to each strong target, designs a beam optimization scheme with strong target restriction, and uses the convex optimization toolbox to solve and obtain the beam weight matrix. For each detection moment, the radar waveform corresponding to the beam weight matrix is ​​transmitted and the radar echo of each angle unit is received. After calculating the Wald-type statistics of the radar echo, state calculation and action selection are performed, and then the angle unit that the beam pattern of the MIMO radar needs to focus on is selected. Finally, according to the beam weight matrix solution method with strong target restriction, the beam weight matrix of the next detection moment is quickly solved based on the angle unit, thereby improving the detection performance of the MIMO radar for weak targets.

[0116] In one embodiment, before the above step S20, the above MIMO radar multi-target detection method based on strong target restriction may further include the following processing steps:

[0117] When the detection time is greater than 0, the angle unit on which the beam pattern of the MIMO radar needs to be focused is selected based on the action combined with the prior information of the previous moment;

[0118] Compute the reward of a Markov decision process based on the Wald-type statistics of the radar echoes;

[0119] Update the state-value function based on the action.

[0120] It can be understood that if the detection time t is not t = 0, then according to the following formula (21), combined with the prior information of the previous moment (that is, Λ t-1 ) to select the angle unit where the beam pattern needs to be focused:

[0121]

[0122] In this way, the proposed method (ie, the above-mentioned MIMO radar multi-target detection method based on strong target restriction) can be prevented from having a degradation in the detection performance of strong targets.

[0123] If the detection time t>0, the reward r of the Markov decision process is calculated according to formula (12): t .

[0124] If the detection time t>0, the state-value function Q is updated according to formula (14).

[0125] Afterwards, the process proceeds to the above step S20.

[0126] In one embodiment, after the above step S20, the above MIMO radar multi-target detection method based on strong target restriction may further include the following processing steps:

[0127] When the detection time is equal to the time step number of MIMO radar multi-target detection, the process ends.

[0128] It can be understood that if the detection time t=T, T is the time step number of MIMO radar multi-target detection, the process ends; otherwise, the detection time is set to the next detection time, that is, t←t+1, and the process goes to step S11. In this way, accurate detection process control can be achieved, calculation process errors can be avoided, and calculation resources can be saved.

[0129] In one embodiment, in order to more intuitively introduce the effect of the above-mentioned MIMO radar multi-target detection method based on strong target restriction, one of the experimental examples is given. Those skilled in the art should understand that the experimental example is only illustrative and is used to assist in understanding the above-mentioned MIMO radar multi-target detection method based on strong target restriction, but is not the only limitation to the above-mentioned MIMO radar multi-target detection method based on strong target restriction.

[0130] The observation area of ​​the MIMO radar is evenly divided into L = 20 angle units, and the array element spacing d is set to half the wavelength λ2, and the spatial frequency ν is defined as:

[0131]

[0132] Then each angle unit can be represented by a unique spatial frequency, namely {-0.5, -0.45, …, 0.45}.

[0133] Assume that at a certain time t, we get θ t ={ν=-0.4,-0.25,0.05,0.25}, N T =32. Figure 6 By comparing the beam patterns obtained by the two beam optimization schemes, it can be seen that the beam optimization scheme with strong target limitation can effectively limit the power gain on the strong target, thereby improving the power gain on the weak target.

[0134] Assume that there are 4 targets located at {ν=-0.4,-0.2,-0.05,0.35}, and the signal-to-noise ratio is -9dB. β=0.5,γ=0.5,H=6,ε=0.5,P T =1. N T =N R =32, P FA =10 -4 The normalized power spectral density function of clutter is as follows: Figure 7 shown.

[0135] like Figure 8 The schematic diagram of the ablation experiment performance comparison shown compares the detection probability change curves of the method proposed in the present invention with / without prior information, and with / without strong target restrictions. It can be found that when there is no prior information support, the detection performance of the method proposed in the present invention is significantly reduced. When there is no strong target restriction, although the detection performance of the method proposed in the present invention has a slight recovery for strong targets, the detection performance for weak targets has a significant decline. (a) is the detection probability change curve with and without the assistance of prior information, and (b) is the detection probability change curve with and without strong target restrictions. The unit of time can be milliseconds.

[0136] The experimental parameters are the same as above, such as Fig. 9 The detection performance comparison shown also compares the detection performance of the method proposed in the present invention with the detection performance of the classical method in the art. (a) is the detection performance of the method proposed in the present invention, and (b) is the detection performance of the classical method in the art. The unit of time can be milliseconds. It can be seen that the performance of the method proposed in the present invention is better than that of the classical method.

[0137] It should be understood that although Figure 1 The steps in the method are shown in sequence as indicated by the arrows, but the steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of the steps, and the steps can be executed in other orders. Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequentially, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0138] In one embodiment, Fig.10As shown, a MIMO radar multi-target detection system 100 based on strong target restriction is provided, which may include an initialization module 11, an echo calculation module 13, a state calculation module 15, an action selection module 17, an angle selection module 19 and a weight solution module 21. Among them, the initialization module 11 is used to initialize the current detection time and the beam weight matrix of the MIMO radar. The echo calculation module 13 is used to transmit the radar waveform corresponding to the beam weight matrix and receive the radar echo of each angle unit, and calculate the Wald-type statistics of the radar echo. The state calculation module 15 is used to calculate the state of the Markov decision process according to the Wald-type statistics of the radar echo. The action selection module 17 is used to select the action of the Markov decision process according to the state. The angle selection module 19 is used to select the angle unit on which the beam pattern of the MIMO radar needs to be focused according to the action when the detection time is 0. The weight solving module 21 is used to solve the beam weight matrix at the next detection moment based on the angle unit according to the beam weight matrix solving method with strong target restriction; the beam weight matrix at the next detection moment is used for MIMO radar multi-target detection at the next detection moment.

[0139] The above-mentioned MIMO radar multi-target detection system 100 based on strong target restriction is further studied on the basis of the MIMO radar multi-target detection method based on reinforcement learning, and a beam weight matrix solution method with strong target restriction is designed, which takes out possible strong targets from the detection results of the MIMO radar, calculates the power gain limit value corresponding to each strong target, designs a beam optimization scheme with strong target restriction and uses the convex optimization toolbox to solve and obtain the beam weight matrix. For each detection moment, the radar waveform corresponding to the beam weight matrix is ​​transmitted and the radar echo of each angle unit is received. After calculating the Wald-type statistics of the radar echo, state calculation and action selection are performed, and then the angle unit that the beam pattern of the MIMO radar needs to focus on is selected. Finally, according to the beam weight matrix solution method with strong target restriction, the beam weight matrix of the next detection moment is quickly solved based on the angle unit, thereby improving the detection performance of the MIMO radar for weak targets.

[0140] In one embodiment, the angle selection module 19 is also used to select the angle unit that the beam pattern of the MIMO radar needs to focus on according to the action combined with the prior information of the previous moment when the detection time is greater than 0. The above-mentioned MIMO radar multi-target detection system 100 based on strong target restriction also includes: a reward calculation module for calculating the reward of the Markov decision process according to the Wald-type statistics of the radar echo; and a function update module for updating the state-value function according to the action.

[0141] In one embodiment, the angle selection module 19 ends the process when the detection time is equal to the time step number of MIMO radar multi-target detection.

[0142] In one embodiment, the MIMO radar is a co-sited MIMO radar.

[0143] Regarding the specific limitations of the MIMO radar multi-target detection system 100 based on strong target restrictions, please refer to the corresponding limitations of the MIMO radar multi-target detection method based on strong target restrictions above, which will not be repeated here. Each module in the above-mentioned MIMO radar multi-target detection system 100 based on strong target restrictions can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of a device with data processing functions in the form of hardware, or can be stored in the memory of the aforementioned device in the form of software, so that the processor can call and execute the operations corresponding to the above modules. The aforementioned device can be but not limited to various types of MIMO radar devices already available in the art.

[0144] In one embodiment, a radar device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the following processing steps when executing the computer program: initializing the current detection time and the beam weight matrix of the MIMO radar; transmitting a radar waveform corresponding to the beam weight matrix and receiving radar echoes of each angle unit, and calculating the Wald-type statistics of the radar echoes; calculating the state of a Markov decision process according to the Wald-type statistics of the radar echoes; selecting an action of the Markov decision process according to the state; when the detection time is 0, selecting an angle unit on which the beam pattern of the MIMO radar needs to be focused according to the action; solving the beam weight matrix of the next detection time based on the angle unit according to a beam weight matrix solving method with strong target restriction; the beam weight matrix of the next detection time is used for MIMO radar multi-target detection at the next detection time.

[0145] In one embodiment, when the processor executes the computer program, the added steps or sub-steps in each embodiment of the above-mentioned MIMO radar multi-target detection method based on strong target restriction can also be implemented.

[0146] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following processing steps are implemented: initializing the current detection time and the beam weight matrix of the MIMO radar; transmitting a radar waveform corresponding to the beam weight matrix and receiving radar echoes of each angle unit, and calculating the Wald-type statistics of the radar echo; calculating the state of a Markov decision process according to the Wald-type statistics of the radar echo; selecting an action of the Markov decision process according to the state; when the detection time is 0, selecting an angle unit on which the beam pattern of the MIMO radar needs to be focused according to the action; solving the beam weight matrix of the next detection time based on the angle unit according to a beam weight matrix solving method with strong target restriction; the beam weight matrix of the next detection time is used for MIMO radar multi-target detection at the next detection time.

[0147] In one embodiment, when the computer program is executed by a processor, it can also implement the steps or sub-steps added in each embodiment of the above-mentioned MIMO radar multi-target detection method based on strong target restriction.

[0148] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus dynamic random access memory (RambusDRAM, referred to as RDRAM) and interface dynamic random access memory (DRDRAM).

[0149] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0150] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of protection of the invention. It should be pointed out that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the attached claims.

Claims

1. A MIMO radar multi-target detection method based on strong target restriction, characterized in that: Includes steps: Initialize the current detection time and the beam weight matrix of the MIMO radar; Transmit the radar waveform corresponding to the beam weight matrix and receive the radar echo of each angle unit, and calculate the Wald-type statistics of the radar echo; Compute the state of the Markov decision process based on the Wald-type statistics of the radar echo; Select an action in a Markov decision process based on the state; When the detection time is 0, the angle unit on which the beam pattern of the MIMO radar needs to be focused is selected according to the action; According to the beam weight matrix solution method with strong target restriction, the beam weight matrix at the next detection moment is solved based on the angle unit; the beam weight matrix at the next detection moment is used for MIMO radar multi-target detection at the next detection moment.

2. The MIMO radar multi-target detection method based on strong target restriction according to claim 1, characterized in that: According to the beam weight matrix solving method with strong target restriction, before the step of solving the beam weight matrix at the next detection moment based on the angle unit, the method further includes the following steps: When the detection time is greater than 0, the angle unit on which the beam pattern of the MIMO radar needs to be focused is selected based on the action combined with the prior information of the previous moment; Compute the reward of a Markov decision process based on the Wald-type statistics of the radar echoes; Update the state-value function based on the action.

3. The MIMO radar multi-target detection method based on strong target restriction according to claim 1 or 2, characterized in that: According to the beam weight matrix solving method with strong target restriction, after the step of solving the beam weight matrix at the next detection moment based on the angle unit, the method further includes the steps of: When the detection time is equal to the time step number of MIMO radar multi-target detection, the process ends.

4. A MIMO radar multi-target detection system based on strong target restriction, characterized in that: include: Initialization module, used to initialize the current detection time and the beam weight matrix of the MIMO radar; The echo calculation module is used to transmit the radar waveform corresponding to the beam weight matrix and receive the radar echo of each angle unit, and calculate the Wald-type statistics of the radar echo; A state calculation module, used to calculate the state of the Markov decision process based on the Wald-type statistics of the radar echo; An action selection module, which is used to select the action of the Markov decision process according to the state; An angle selection module, used to select the angle unit on which the beam pattern of the MIMO radar needs to be focused according to the action when the detection time is 0; The weight solving module is used to solve the beam weight matrix at the next detection moment based on the angle unit according to the beam weight matrix solving method with strong target restriction; the beam weight matrix at the next detection moment is used for MIMO radar multi-target detection at the next detection moment.

5. The MIMO radar multi-target detection system based on strong target restriction according to claim 4 is characterized in that: The angle selection module is also used to select the angle unit on which the beam pattern of the MIMO radar needs to be focused according to the action combined with the prior information of the previous moment when the detection moment is greater than 0; The MIMO radar multi-target detection system based on strong target limitation also includes: A reward calculation module, which is used to calculate the reward of the Markov decision process based on the Wald-type statistics of the radar echo; Function update module, used to update the state-value function according to the action.

6. The MIMO radar multi-target detection system based on strong target limitation according to claim 4 or 5, characterized in that: The angle selection module ends the process when the detection time is equal to the time step number of the MIMO radar multi-target detection.

7. The MIMO radar multi-target detection system based on strong target limitation according to claim 6 is characterized in that: The MIMO radar is a co-site MIMO radar.

8. A radar device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the MIMO radar multi-target detection method based on strong target restriction according to any one of claims 1 to 3 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the MIMO radar multi-target detection method based on strong target restriction according to any one of claims 1 to 3 are implemented.

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