FDA-MIMO radar transmit-receive parameter joint optimization method

Abstract

The invention discloses an FDA-MIMO radar transmit-receive parameter joint optimization method comprising the following steps: modeling a system output signal to interference plus noise ratio of an FDA-MIMO radar system, and constructing an optimization problem used for maximizing the system output signal to interference plus noise ratio; the optimization variable of the optimization problem is (w, c, delta f); c represents a radar coding vector, w represents a weight vector of a receiving matching filter, and delta f represents a radar emission frequency stepping amount; by solving the optimization problem, the radar coding vector, the receiving matching filter and the radar emission frequency stepping amount are jointly optimized, so that the output signal to interference plus noise ratio of a radar system during target detection in a complex environment is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of radar, and particularly relates to a method for jointly optimizing the transceiver parameters of an FDA (Frequency Diverse Array)-MIMO (Multiple Input Multiple Output) radar. Background Art

[0002] By introducing a frequency step between the transmitting array elements, the FDA radar obtains a three-dimensional transmitting direction diagram of range-angle-time. Through receiving comprehensive matching processing, additional target range information can be obtained, expanding the dimension of the system's acquisition of target information. Sammartino et al. combined the FDA and MIMO technologies, and after separating the transmitting waveforms in the receiving end signal processing, independent angle and range information was obtained, overcoming the range-angle coupling and time-varying problems of the FDA direction diagram. Compared with traditional phased array or MIMO radars, the equivalent transmitting steering vector of the FDA-MIMO radar is a function of range and angle, and range-angle two-dimensional beamforming can be realized. By using spatial domain processing methods, range and angle information can be obtained simultaneously. However, in a complex environment, the system output signal-to-interference-plus-noise ratio (SINR) of the FDA-MIMO radar system for target detection still needs to be further improved. Summary of the Invention

[0003] In order to solve the above problems existing in the prior art, the present invention provides a method for jointly optimizing the transceiver parameters of an FDA-MIMO radar.

[0004] The technical problems to be solved by the present invention are realized through the following technical solutions:

[0005] A method for jointly optimizing the transceiver parameters of an FDA-MIMO radar, comprising:

[0006] By modeling the FDA-MIMO radar system, an optimization problem for maximizing the system output signal-to-interference-plus-noise ratio is constructed; the optimization variables of the optimization problem are (w, c, Δf); c represents the radar coding vector, w represents the weight vector of the receiving matching filter, and Δf represents the radar transmitting frequency step.

[0007] By solving the optimization problem, the radar coding vector, the receiving matching filter, and the radar transmitting frequency step are jointly optimized.

[0008] Optionally, the constructing an optimization problem for maximizing the system output signal-to-interference-plus-noise ratio by modeling the FDA-MIMO radar system includes:

[0009] Model the sample vector from the target echo to obtain a sample vector modeling model;

[0010] Model the clutter samples from the superposition of echoes from different uncorrelated scatterers near the range-azimuth cell to obtain a clutter sample modeling model;

[0011] According to the sample vector modeling model and the clutter sample modeling model, model the received signal generated by the range-azimuth cell to obtain a received signal modeling model;

[0012] By using the weight vector to process the received signal modeling model, construct a system output signal-to-interference-plus-noise ratio (SINR) modeling model;

[0013] According to the system output SINR modeling model, construct an optimization problem for maximizing the system output SINR.

[0014] Optionally, constructing an optimization problem for maximizing the system output SINR according to the system output SINR modeling model includes:

[0015] According to the system output SINR modeling model and multiple constraint conditions respectively related to c, w, and Δf, construct an optimization problem for maximizing the system output SINR.

[0016] Optionally, the optimization problem is:

[0017]

[0018] where SINR(w, c, Δf) is the system output SINR modeling model; M is the number of transmitting array elements, B w is the bandwidth, c represents the radar coding vector, c 0 represents the reference radar coding, and 0 < δ < 2 represents the range of the similarity constraint between c and c 0 .

[0019] Optionally, the system output SINR modeling model is:

[0020]

[0021] where is the adjoint matrix of w, s(θ 0 , Δτ, Δf) represents the joint transmit-receive steering vector, Σ c (c, Δf) represents the clutter covariance matrix, represents the noise power;

[0022] The joint transmit-receive steering vector is expressed as:

[0023]

[0024] wherein, represents the Kronecker product, and ⊙ represents the Hadamard product, represents the angle-dependent receive steering vector, represents the angle-dependent transmit steering vector, represents the range-dependent transmit steering vector, represents the complex number field, e is the natural base, j is the imaginary part, d represents the element spacing, and λ 0 represents the wavelength, and θ 0 represents the azimuth angle θ 0 , N is the number of receive elements, Δτ represents the incremental delay, T represents the matrix transpose;

[0025] The clutter covariance matrix is expressed as:

[0026]

[0027] wherein, z C represents the clutter sample obtained by superimposing echoes from different uncorrelated scatterers near the range-azimuth angle cell, is the adjoint matrix of z C , K is the number of scatterers in a single range-azimuth angle cell, I is the number of discrete azimuth angle cells, P is the length of the radar coding vector, represents the scattering intensity of the k-th scatterer in the (l, i)-th range-azimuth angle cell, θ i represents the azimuth angle of the i-th sector, and Δτ k represents the incremental delay of the k-th scatterer on the azimuth angle cell, and J l represents the binary shift matrix, is the adjoint matrix of J l , is the adjoint matrix of c, is the adjoint matrix of s(θ i , Δτ k , Δf), and s(θ i , Δτ k , Δf) represents the joint transmit-receive steering vector at the azimuth angle θ i and the incremental delay Δτ k , represents taking the expectation.

[0028] Optionally, the joint optimization of the radar coding vector, the receive matching filter, and the radar transmit frequency step amount by solving the optimization problem includes:

[0029] Decompose the optimization problem into a first sub-optimization problem, a second sub-optimization problem, and a third sub-optimization problem; wherein, the optimization variables of the first sub-optimization problem, the second sub-optimization problem, and the third sub-optimization problem are w, c, and Δf in sequence; the first sub-optimization problem, the second sub-optimization problem, and the third sub-optimization problem all take maximizing the system output signal-to-interference-plus-noise ratio as the optimization objective;

[0030] By jointly and iteratively solving the first sub-optimization problem, the second sub-optimization problem, and the third sub-optimization problem, jointly optimize the radar coding vector, the receiving matching filter, and the radar transmission frequency step size;

[0031] Wherein, in each iterative solution process, keep c and Δf unchanged to solve the first sub-optimization problem to obtain the optimized w and the corresponding system output signal-to-interference-plus-noise ratio increment, keep w and Δf unchanged to solve the second sub-optimization problem to obtain the optimized c and the corresponding system output signal-to-interference-plus-noise ratio increment, keep w and c unchanged to solve the third sub-optimization problem to obtain the optimized Δf and the corresponding system output signal-to-interference-plus-noise ratio increment; from the optimized w, the optimized c, and the optimized Δf, select the one corresponding to the maximum system output signal-to-interference-plus-noise ratio increment to update the optimization variable of this iteration, and continue the next iteration until the system output signal-to-interference-plus-noise ratio increment converges.

[0032] Optionally, the first sub-optimization problem is:

[0033]

[0034] Wherein, y 1 is the optimization variable of the first sub-optimization problem, y 2 is the optimization variable of the second sub-optimization problem, y 3 is the optimization variable of the third sub-optimization problem, n is the current iteration number, y 1 (n) represents y at the end of the nth iteration 1 , y 2 (n-1) represents y at the end of the (n - 1)th iteration 2 , y 3 (n-1) represents y at the end of the (n - 1)th iteration 3 ; SINR(y 1 , y 2 (n -1) , y 3 (n-1) ) represents the system output signal-to-interference-plus-noise ratio when w = y 1 , c = y 2 (n-1) , Δf = y 3 (n-1) ;

[0035] The methods for solving the first sub-optimization problem include: solving the closed-form solution of the first sub-optimization problem; the expression of the closed-form solution is:

[0036]

[0037] where denotes that c = y 2 (n-1) , Δf = y 3 (n-1) is the clutter covariance matrix; I is the identity matrix; denotes that Δf = y 3 (n-1) is the combined transmit-receive steering vector, is the closed-form solution.

[0038] Optionally, the second sub-optimization problem is:

[0039]

[0040] where y 1 is the optimization variable of the first sub-optimization problem, y 2 is the optimization variable of the second sub-optimization problem, y 3 is the optimization variable of the third sub-optimization problem, n is the current iteration number, y 2 (n) denotes y at the end of the nth iteration 2 , y 1 (n-1) denotes y at the end of the (n - 1)th iteration 1 , y 3 (n-1) denotes y at the end of the (n - 1)th iteration 3 ; SINR(y 1 (n-1) , y 2 , y 3 (n-1) ) denotes the system output signal-to-interference-plus-noise ratio when w = y 1 (n-1) , c = y 2 , Δf = y 3 (n-1) ;

[0041] The methods for solving the second sub-optimization problem include: solving the second sub-optimization problem in the way of solving the fractional quadratic optimization problem.

[0042] Optionally, the third sub-optimization problem is:

[0043]

[0044] where y 1 is the optimization variable of the first sub-optimization problem, y 2 is the optimization variable of the second sub-optimization problem, y 3 is the optimization variable of the third sub-optimization problem, n is the current iteration number, and y 1 (n-1) represents y at the end of the (n - 1)-th iteration 1 , y 2 (n-1) represents y at the end of the (n - 1)-th iteration 2 , y 3 (n) represents y at the end of the n-th iteration 3 ; SINR(y 1 (n -1) , y 2 (n-1) , y 3 ) represents the system output signal-to-interference-plus-noise ratio when w = y 1 (n-1) , c = y 2 (n-1) , Δf = y 3 ;

[0045] The method for solving the third sub-optimization problem includes: using the MM algorithm to solve the third sub-optimization problem to obtain an approximate solution of the third sub-optimization problem;

[0046] where the surrogate function used when using the MM algorithm to solve the third sub-optimization problem is:

[0047]

[0048] where represents optimizing the variable y 3 at the (n - 1)-th iteration, and the surrogate function of SINR(w (n-1) , c (n-1) , y 3 ) found using the MM algorithm, X (n-1) , and are quadratic term coefficients, Ψ = {x: 0 ≤ x ≤ B w / (M - 1)}; the approximate solution is

[0049] The joint optimization method for transceiver parameters of FDA-MIMO radar provided by the present invention models the FDA-MIMO radar system to construct an optimization problem for maximizing the output signal-to-interference-plus-noise ratio (SINR) of the system. By solving the optimization problem, the radar coding vector, the receiving matching filter, and the radar transmit frequency step size are jointly optimized. Considering the optimization of the frequency step size in the FDA-MIMO radar system and combining radar coding and the receiving filter for joint optimization can further improve the output SINR of the radar system during target detection, and enhance the output signal-to-interference-plus-noise ratio of the radar system for target detection in complex environments.

[0050] The following will further elaborate on the present invention in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a flowchart of a joint optimization method for transceiver parameters of an FDA-MIMO radar provided by an embodiment of the present invention;

[0052] Figure 2 is a schematic diagram of an FDA-MIMO radar;

[0053] Figure 3 shows a flowchart of the joint optimization process for transceiver parameters of the FDA-MIMO radar of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] The following further describes the present invention in detail with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0055] To improve the output signal-to-interference-plus-noise ratio of the radar system for target detection in complex environments, an embodiment of the present invention provides a joint optimization method for transceiver parameters of an FDA-MIMO radar, as Figure 1 shown, the method includes the following steps:

[0056] S10. Model the FDA-MIMO radar system to construct an optimization problem for maximizing the output signal-to-interference-plus-noise ratio of the system; the optimization variables of this optimization problem are (w, c, Δf); c represents the radar coding vector, w represents the weight vector of the receiving matching filter, and Δf represents the radar transmit frequency step size.

[0057] Specifically, Figure 2 shows a schematic diagram of the FDA-MIMO radar system. By modeling the FDA-MIMO radar system, constructing an optimization problem for maximizing the output signal-to-interference-plus-noise ratio of the system includes:

[0058] (1-1) Model the sample vector from the target echo to obtain a sample vector modeling model.

[0059] Specifically, consider a co-located FDA-MIMO radar system with M transmit array elements and N receive array elements. Both the transmit and receive arrays are half-wavelength uniform linear arrays. The frequency of each transmit array element starts from the reference carrier frequency f 0 and linearly increases with a radar transmit frequency step Δf. The actual carrier frequency of the m-th (m = 1, 2,..., M) transmit array element is given by the following formula:

[0060] f m = f 0 + (m - 1)Δf

[0061] Assume that the array elements transmit orthogonal waveforms and use a radar coding vector of length P (P ≥ 3) to modulate the signals transmitted in fast time. At the receiving end, the received signals are down-converted, matched-filtered, and sampled to generate a PMN-dimensional vector representing the fast observations of the range-azimuth cells. Then, the sample vector from the target echo can be expressed by the following formula:

[0062]

[0063] where z S represents the sample vector from the target echo, represents the Kronecker product, α 0 represents the complex echo coefficient of the received signal, c represents the radar coding vector, represents the complex number field, M is the number of transmit array elements, N is the number of transmit array elements, θ 0 represents the azimuth angle θ 0 , Δτ = t θ - τ 0 represents the incremental delay of the target, t * is the sampling time, τ 0 = 2R 0 / c is the envelope delay, R 0 is the distance between the radar and the target, c is the speed of light, s(θ 0 , Δτ, Δf) represents the joint transmit-receive steering vector, and its specific expression is:

[0064]

[0065] where ⊙ represents the Hadamard product, represents the angle-dependent receive steering vector, represents the angle-dependent transmit steering vector, Denote the distance-dependent transmit steering vector, \(e\) is the base of the natural logarithm, \(j\) represents the imaginary part of a complex number, \(d\) represents the element spacing, and \(\lambda\) 0 represents the wavelength, and \(T\) represents the matrix transpose.

[0066] (1 - 2) Model the clutter samples obtained by superimposing the echoes from different uncorrelated scatterers near the range-azimuth cell to obtain the clutter sample modeling model.

[0067] Here, the expression for the clutter sample obtained by superimposing the echoes from different uncorrelated scatterers near the range-azimuth cell is:

[0068]

[0069] where \(z\) C denotes the clutter sample obtained by superimposing the echoes from different uncorrelated scatterers near the range-azimuth cell, \(K\) is the number of scatterers in a single range-azimuth cell, \(I\) is the number of discrete azimuth cells, \(L\) is the number of range rings in the interference region, which satisfies \(2L - 2, L\leq P\), \(s(\theta\) i ,\(\Delta\tau\) k ,\(\Delta f)\) represents the joint transmit-receive steering vector at azimuth \(\theta\) i and incremental delay \(\Delta\tau\) k , \(\beta\) l,i,k represents the amplitude of the \(k\)-th scatterer from the \((l, i)\)-th range-azimuth cell, \(J\) l denotes the binary shift matrix, and the specific expression is:

[0070]

[0071] (1 - 3) According to the sample vector modeling model and the clutter sample modeling model, model the received signal generated by the range-azimuth cell to obtain the received signal modeling model.

[0072] Specifically, assume that there is a target with azimuth \(\theta\) 0 and distance \(R\) 0 in the far field. Then the received signal generated by the range-azimuth cell can be modeled as:

[0073] \(v = z\) S + \(z\) C + \(n\);

[0074] where denotes the noise vector, which is modeled as a zero-mean, complex, circularly symmetric random vector and satisfies Here, assume that the noise power is is the adjoint matrix of \(n\).

[0075] (1 - 4) Process the received signal modeling model using the weight vector to construct the system output signal - to - interference - plus - noise ratio (SINR) modeling model.

[0076] Specifically, by using the weight vector The signal v obtained by processing, the system output SINR can be expressed as:

[0077]

[0078] where, is the adjoint matrix of w, s(θ 0 , Δτ, Δf) represents the joint transmit - receive steering vector, represents the noise power, Σ c (c, Δf) represents the clutter covariance matrix of the clutter samples, and its expression is:

[0079]

[0080] where, is the adjoint matrix of z C , represents the scattering intensity of the k - th scatterer in the (l, i) - th range - azimuth cell, θ i represents the azimuth angle of the i - th sector, Δτ k represents the incremental delay of the k - th scatterer on the azimuth cell, is the adjoint matrix of J l , is the adjoint matrix of c, is the adjoint matrix of s(θ i , Δτ k , Δf), s(θ i , Δτ k , Δf) represents the joint transmit - receive steering vector at the azimuth angle θ i and the incremental delay Δτ k , represents taking the expectation.

[0081] (1 - 5) According to the system output SINR modeling model, construct an optimization problem for maximizing the system output SINR.

[0082] Specifically, according to the system output SINR modeling model and multiple constraint conditions related to c, w, and Δf respectively, construct an optimization problem for maximizing the system output SINR. This optimization problem is:

[0083]

[0084] where, B w is the bandwidth, c 0The radar code indicating reference, and δ represents c and c 0 The range of similarity constraint.

[0085] S20. By solving the above optimization problem, jointly optimize the radar code vector, the receive matching filter, and the radar transmit frequency step size.

[0086] According to 's expression, it can be seen that obtaining 's global optimal solution is a difficult task and an analytical closed-form solution cannot be obtained. Since is a non-convex and NP (Nondeterministic Polynomial time) hard optimization problem, the present invention develops a suboptimal solution strategy with certain optimality characteristics to reduce complexity, that is, the Minorization Maximization (MM)-Maximum Block Improvement (MBI) algorithm to be described below. This algorithm can provide a high-quality solution with affordable computational complexity. Briefly speaking, in the MM-MBI algorithm proposed by the present invention, each variable block, namely the frequency increment, the radar code, or the filter, is locally optimized (possibly using the MM paradigm) in each iteration, and the corresponding SINR increment is obtained. Then, only the variable block that generates the maximum increment is updated after each iteration, and the next iteration continues until the SINR increment converges.

[0087] The MM-MBI algorithm proposed by the present invention will be described in detail below.

[0088] First, in order to describe the process of the MM-MBI algorithm, the optimization variables are introduced:

[0089]

[0090] Among them, y contains 3 variable blocks y 3 = Δf, corresponding to the receive matching filter, the radar code vector, and the radar transmit frequency step size to be optimized respectively. In addition, the optimization vector obtained at the nth iteration is denoted as y n = [w (n)T , c (n)T , Δf (n) T .

[0091] Thus, by solving the above optimization problem, jointly optimize the radar code vector, the receive matching filter, and the radar transmit frequency step size, including:

[0092] ​(2-1) Decompose the above optimization problem into a first sub-optimization problem, a second sub-optimization problem, and a third sub-optimization problem; among them, the optimization variables of the first sub-optimization problem, the second sub-optimization problem, and the third sub-optimization problem are w, c, and Δf in sequence; the first sub-optimization problem, the second sub-optimization problem, and the third sub-optimization problem all take maximizing the system output signal-to-interference-plus-noise ratio as the optimization goal.

[0093] The first sub-optimization problem is:

[0094]

[0095] where y 1 is the optimization variable of the first sub-optimization problem, y 2 is the optimization variable of the second sub-optimization problem, y 3 is the optimization variable of the third sub-optimization problem, n is the current iteration number, and y 1 (n) represents y at the end of the nth iteration 1 , y 2 (n-1) represents y at the end of the (n - 1)th iteration 2 , y 3 (n-1) represents y at the end of the (n - 1)th iteration 3 ; SINR(y 1 , y 2 (n-1) , y 3 (n-1) ) represents the system output signal-to-interference-plus-noise ratio when w = y 1 , c = y 2 (n-1) , Δf = y 3 (n-1) .

[0096] The second sub-optimization problem is:

[0097]

[0098] where y 2 (n) represents y at the end of the nth iteration 2 , y 1 (n-1) represents y at the end of the (n - 1)th iteration 1 , y 3 (n-1) represents y at the end of the (n - 1)th iteration 3 ; SINR(y 1 (n-1) , y 2 , y 3 (n-1) ) represents w = y1 (n-1) and c = y 2 and Δf = y 3 (n-1) System output signal-to-interference-plus-noise ratio when...

[0099] The third sub-optimization problem is:

[0100]

[0101] where y 1 (n-1) represents y at the end of the (n - 1)-th iteration 1 and y 2 (n-1) represents y at the end of the (n - 1)-th iteration 2 and y 3 (n) represents y at the end of the n-th iteration 3 ; SINR(y 1 (n-1) , y 2 (n-1) , y 3 ) represents the system output signal-to-interference-plus-noise ratio when w = y 1 (n-1) and c = y 2 (n-1) and Δf = y 3 System output signal-to-interference-plus-noise ratio when...

[0102] (2 - 2) By jointly iteratively solving the first sub-optimization problem, the second sub-optimization problem, and the third sub-optimization problem, the joint optimization of the radar coding vector, the receiving matching filter, and the radar transmit frequency step size is achieved; among them, in each iterative solution process, the first sub-optimization problem is solved while keeping c and Δf unchanged to obtain the optimized w and the corresponding system output signal-to-interference-plus-noise ratio increment, the second sub-optimization problem is solved while keeping w and Δf unchanged to obtain the optimized c and the corresponding system output signal-to-interference-plus-noise ratio increment, and the third sub-optimization problem is solved while keeping w and c unchanged to obtain the optimized Δf and the corresponding system output signal-to-interference-plus-noise ratio increment; from the optimized w, the optimized c, and the optimized Δf, the item corresponding to the largest system output signal-to-interference-plus-noise ratio increment is selected for updating the optimization variable in this iteration, and the next iteration continues until the system output signal-to-interference-plus-noise ratio increment converges.

[0103] Specifically, since the problem is implicitly convex, therefore, in each iterative solution process, the closed-form solution can be directly solved, and the expression of this closed-form solution is:

[0104]

[0105] where represents c = y 2(n-1) 、 The clutter covariance matrix when Δf = y; I is the identity matrix; 3 (n-1) When; Indicates Δf = y 3 (n-1) When the joint transmit - receive steering vector, Is a closed - form solution.

[0106] The proof process of the closed - form solution for the first sub - optimization problem is as follows:

[0107] To solve the first sub - optimization problem, transform SINR(w, c, Δf) into the following form:

[0108]

[0109]

[0110] Where, And abbreviate the clutter covariance matrix Σ c (c, Δf) as Σ c , Denotes the real - part operation.

[0111] First, prove that the problem And Are equivalent, where Compared with In Adds a constraint, making v(·) represents the optimal value of problem (·).

[0112] Let w 1' Be the optimal solution of the problem , then The optimal solution of can be expressed as And arg(·) is the function to find the complex - number argument.

[0113] By transforming the maximum - value problem into a minimum - value problem, can be equivalent to: Equivalent to:

[0114]

[0115] At this time, And assume that w 1”' * Is 's optimal solution, then w 1”' * Is also 's optimal solution. Therefore, And Are equivalent. Due to the added constraint here, there is

[0116] Assume that w 1” * is the optimal solution of, then the optimal solution of can be expressed as: and Since makes w 1”” ' = w 1” * .

[0117] In summary, it can be obtained that are equivalent to each other, and the expression of the solution is:

[0118]

[0119] Normalize it to obtain the closed-form solution of the weight vector as:

[0120]

[0121] Regarding the solution method of the second sub-optimization problem , since the problem is implicitly convex, the optimal solution of can be found in polynomial time, and the second sub-optimization problem can be solved specifically in the way of solving the fractional quadratic optimization problem.

[0122] Specifically, by simplifying the problem and solving a relaxed semi-definite programming (SDP) problem (i.e., removing the rank-one constraint):

[0123]

[0124] Then, combine the Charnes and Cooper transformation and a specific rank-one decomposition method to solve the radar coding vector.

[0125] Regarding the solution method of the third sub-optimization problem, the MM algorithm can be used to solve the third sub-optimization problem to obtain an approximate solution of the third sub-optimization problem; among them, the surrogate function used when solving the third sub-optimization problem by the MM algorithm is:

[0126]

[0127] Among them, represents the optimization of the variable y 3 at the (n - 1)-th iteration, and the surrogate function of SINR(w (n-1) , c (n-1) , y 3 ) found by using the MM algorithm, X (n-1) , and is the quadratic coefficient, Ψ = {x: 0 ≤ x ≤ B w / (M - 1)}; The approximate solution of the third sub-optimization problem is

[0128] In the MM-MBI algorithm proposed in the present invention, for the optimization of the frequency step size, the MM algorithm is used to find a surrogate function for approximate solution optimization, and the closed-form solution of the surrogate function is used for approximate solution; for the optimization of radar coding, it is solved by a polynomial-time algorithm; for the optimization of the receive matching filter, since this problem is implicitly convex, a closed-form solution can be found for solution; finally, the MBI algorithm is used to alternately optimize each variable block, that is, by using the optimized output value obtained in each iteration, the variable that generates the maximum system output signal-to-interference-plus-noise ratio increment is updated by the MBI (Maximum Block Improvement) algorithm, effectively improving the problem-solving speed. Figure 3 The flow chart of the joint optimization processing of the transceiver parameters of the FDA-MIMO radar according to the present invention is shown, where respectively represent the increment of the system output signal-to-interference-plus-noise ratio corresponding to the optimized w, the increment of the system output signal-to-interference-plus-noise ratio corresponding to the optimized c, and the increment of the system output signal-to-interference-plus-noise ratio corresponding to the optimized Δf and.

[0129] When after multiple iterations of the MBI algorithm, the increment of the system output signal-to-interference-plus-noise ratio remains at a small level and no longer increases, it indicates that the increment of the system output signal-to-interference-plus-noise ratio has converged. At this time, the current optimized radar coding vector, receive matching filter, and radar transmit frequency step size are the approximate optimal solutions finally completed by the joint optimization.

[0130] In summary, the present invention combines the advantages of FDA and MIMO technologies. By jointly optimizing the radar transmit frequency step size, radar coding vector, and receive matching filter of the FDA-MIMO radar, the detection performance of the radar for targets in complex environments is improved. By optimizing the transmit frequency increment, the FDA-MIMO radar can achieve high-resolution measurement of distance and angle, and by optimizing the radar coding and receive matching filter, the output SINR of the target can be improved.

[0131] The method provided by the embodiment of the present invention can be applied to an electronic device. Specifically, the electronic device can be: a desktop computer, a portable computer, a smart mobile terminal, a server, etc. There is no limitation here. Any electronic device that can implement the present invention belongs to the protection scope of the present invention.

[0132] It should be noted that the terms "first", "second", etc. are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present invention.

[0133] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification.

[0134] Although the present invention has been described in connection with various embodiments herein, however, in the process of implementing the claimed present invention, those skilled in the art can understand and implement other variations of the disclosed embodiments by viewing the accompanying drawings and the disclosure. In the description of the present invention, the term "including" does not exclude other components or steps, the term "a" or "one" does not exclude a plurality of cases, and the meaning of "a plurality" is two or more, unless otherwise specifically defined. In addition, certain measures are described in different embodiments, but this does not mean that these measures cannot be combined to produce good results.

[0135] The above content is a further detailed description of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention pertains, without departing from the concept of the present invention, several simple deductions or substitutions can still be made, and all should be regarded as belonging to the protection scope of the present invention.

Claims

1. A joint optimization method for FDA-MIMO radar transceiver parameters, characterized in that: include: By modeling the FDA-MIMO radar system, an optimization problem for maximizing the signal-to-interference-noise ratio of the system output is constructed; the optimization variables of the optimization problem are (w, c, Δf); c represents the radar code vector, w represents the weight vector of the receiving matched filter, and Δf represents the radar transmission frequency step; By solving the optimization problem, the radar coding vector, the receiving matched filter and the radar transmitting frequency stepping amount are jointly optimized.

2. The FDA-MIMO radar transceiver parameter joint optimization method according to claim 1, characterized in that: The FDA-MIMO radar system is modeled to construct an optimization problem for maximizing the system output signal-to-interference-noise ratio, including: Modeling a sample vector from a target echo to obtain a sample vector modeling model; Modeling the clutter samples from the superposition of echoes from different unrelated scatterers near the range azimuth unit to obtain a clutter sample modeling model; Modeling the received signal generated by the range and azimuth angle unit according to the sample vector modeling model and the clutter sample modeling model to obtain a received signal modeling model; The received signal modeling model is processed by using the weight vector to construct a system output signal-to-interference-and-noise ratio modeling model; According to the system output signal to interference plus noise ratio modeling model, an optimization problem for maximizing the system output signal to interference plus noise ratio is constructed.

3. The FDA-MIMO radar transceiver parameter joint optimization method according to claim 2, characterized in that: According to the system output signal to interference and noise ratio modeling model, an optimization problem for maximizing the system output signal to interference and noise ratio is constructed, including: According to the system output signal to interference noise ratio modeling model and c , w Multiple constraints related to Δf are used to construct an optimization problem for maximizing the system output signal-to-interference-noise ratio.

4. The FDA-MIMO radar transceiver parameter joint optimization method according to claim 3, characterized in that: The optimization problem is: Among them, SINR(w,c,Δf) is the signal-to-interference-noise ratio modeling model of the system output; M is the number of transmitting array elements, B w is the bandwidth, c represents the radar code vector, c0 represents the reference radar code, and 0<δ<2 represents the range of the similarity constraint between c and c0.

5. The FDA-MIMO radar transceiver parameter joint optimization method according to claim 4, characterized in that: The system output signal to interference and noise ratio modeling model is: in, is the adjoint matrix of w, s(θ0,Δτ,Δf) represents the joint transmit and receive steering vector, Σ c (c,Δf) represents the clutter covariance matrix, represents the noise power; The joint transmit-receive steering vector is expressed as: in, represents the Kronecker product, ⊙ represents the Hadamard product, represents the angle-dependent receive steering vector, represents the angle-dependent launch steering vector, represents the distance-dependent launch steering vector, represents the complex domain, e is the natural base, j is the imaginary part, d is the array element spacing, λ0 is the wavelength, θ0 is the azimuth angle θ0, N is the number of receiving array elements, Δτ is the incremental delay, and T is the matrix transpose; The clutter covariance matrix is ​​expressed as: Among them, z C represents the clutter samples from the superposition of echoes from different unrelated scatterers near the azimuth unit, For z C The adjoint matrix of , K is the number of scatterers in a single range azimuth unit, I is the number of discrete azimuth units, P is the length of the radar code vector, represents the scattering intensity of the kth scatterer at the (l,i)th distance azimuth unit, θ i represents the azimuth of the i-th sector, Δτ k represents the incremental delay of the kth scatterer in the azimuth unit, J l represents a binary shift matrix, For J l The adjoint matrix of is the adjoint matrix of c, is s(θ i ,Δτ k ,Δf), s(θ i ,Δτ k ,Δf) represents the azimuth angle θ i and incremental delay Δτ k The combined transmit and receive steering vector under Expressing expectation.

6. The FDA-MIMO radar receiving and transmitting parameter joint optimization method according to claim 5 is characterized in that: The method of jointly optimizing the radar code vector, the receiving matched filter and the radar transmission frequency step amount by solving the optimization problem includes: Decomposing the optimization problem into a first sub-optimization problem, a second sub-optimization problem and a third sub-optimization problem; wherein the optimization variables of the first sub-optimization problem, the second sub-optimization problem and the third sub-optimization problem are w, c and Δf respectively; and the first sub-optimization problem, the second sub-optimization problem and the third sub-optimization problem all take maximizing the system output signal to interference noise ratio as the optimization goal; By jointly iteratively solving the first sub-optimization problem, the second sub-optimization problem and the third sub-optimization problem, the radar code vector, the receiving matched filter and the radar transmission frequency step amount are jointly optimized; In each iterative solution process, c and Δf are kept unchanged to solve the first sub-optimization problem to obtain the optimized w and the corresponding system output signal to interference and noise ratio increment, w and Δf are kept unchanged to solve the second sub-optimization problem to obtain the optimized c and the corresponding system output signal to interference and noise ratio increment, w and c are kept unchanged to solve the third sub-optimization problem to obtain the optimized Δf and the corresponding system output signal to interference and noise ratio increment; from the optimized w, the optimized c and the optimized Δf, one corresponding to the maximum system output signal to interference and noise ratio increment is selected to perform optimization variable update of this iteration, and the next iteration is continued until the system output signal to interference and noise ratio increment converges.

7. The FDA-MIMO radar transceiver parameter joint optimization method according to claim 6, characterized in that: The first sub-optimization problem is: Wherein, y1 is the optimization variable of the first sub-optimization problem, y2 is the optimization variable of the second sub-optimization problem, y3 is the optimization variable of the third sub-optimization problem, n is the current iteration number, y1 (n) Represents y1, y2 at the end of the nth iteration (n-1) Represents y2, y3 at the end of the n-1th iteration (n-1) represents y3 at the end of the n-1th iteration; SINR(y1,y2 (n-1) ,y3 (n-1) ) means w=y1、c=y2 (n-1) , Δf=y3 (n-1) The system output signal to noise ratio when The method of solving the first sub-optimization problem includes: solving a closed-form solution to the first sub-optimization problem; the expression of the closed-form solution is: in, It means c = y2 (n-1) , Δf=y3 (n-1) The clutter covariance matrix at ; I is the unit matrix; It means Δf=y3 (n-1) The joint transmit-receive steering vector at is the closed-form solution.

8. The FDA-MIMO radar transceiver parameter joint optimization method according to claim 6, characterized in that: The second sub-optimization problem is: Where y1 is the optimization variable of the first sub-optimization problem, y2 is the optimization variable of the second sub-optimization problem, y3 is the optimization variable of the third sub-optimization problem, n is the current iteration number, y2 is the optimization variable of the second sub-optimization problem, (n) Represents y2, y1 at the end of the nth iteration (n-1) Represents y1, y3 at the end of the n-1th iteration (n-1) represents y3 at the end of the n-1th iteration; SINR(y1 (n-1) ,y2,y3 (n-1) ) means w=y1 (n-1) , c=y2、Δf=y3 (n-1) The system output signal to noise ratio when The method for solving the second sub-optimization problem includes: solving the second sub-optimization problem in a manner of solving a fractional quadratic optimization problem.

9. The FDA-MIMO radar receiving and transmitting parameter joint optimization method according to claim 6, characterized in that: The third sub-optimization problem is: Wherein, y1 is the optimization variable of the first sub-optimization problem, y2 is the optimization variable of the second sub-optimization problem, y3 is the optimization variable of the third sub-optimization problem, n is the current iteration number, y1 (n-1) Represents y1, y2 at the end of the n-1th iteration (n-1) Represents y2, y3 at the end of the n-1th iteration (n) represents y3 at the end of the nth iteration; SINR(y1 (n-1) ,y2 (n -1) ,y3) means w=y1 (n-1) 、c=y2 (n-1) , the system output signal to noise ratio when Δf = y3; The method of solving the third sub-optimization problem includes: solving the third sub-optimization problem by using the MM algorithm to obtain an approximate solution to the third sub-optimization problem; Among them, the substitution function used when solving the third sub-optimization problem using the MM algorithm is: in, Indicates that the variable y3 is optimized at the n-1th iteration, and the SINR (w (n-1) ,c (n-1) ,y3) is a substitute function, X (n-1) , is the coefficient of the quadratic term, Ψ={x:0≤x≤B w / (M-1)}; the approximate solution is

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