A method and device for joint estimation of range and angle of sonar targets based on OFDM signals.

Abstract

This invention belongs to the field of signal processing, and specifically to the field of beam direction estimation technology for radar signals, acoustic signals, and electromagnetic signals. More specifically, it relates to a method and apparatus for joint estimation of sonar target range and angle based on OFDM signals. The method includes: one element in a sonar array transmitting an orthogonal frequency division multiplexed signal to a target region; each element receiving the target echo signal, forming an echo signal matrix, establishing a two-dimensional output signal model, and performing dimensionality reduction processing to obtain a one-dimensional output signal model; constructing an optimization function based on the obtained one-dimensional output signal model; and using a greedy algorithm to solve the constructed optimization function to obtain the target range and angle, thereby achieving target estimation.

Description

Technical Field

[0001] This invention belongs to the field of signal processing and beam direction estimation technology for radar signals, acoustic signals and electromagnetic signals. Specifically, it relates to a method and device for joint estimation of sonar target range and angle based on OFDM signals. Background Technology

[0002] Currently, underwater target detection technology is an important research direction in the fields of underwater acoustic signal processing and sonar. Parameter estimation of the range and azimuth of non-cooperative targets is one of the core technologies in marine applications such as environmental perception, marine monitoring, resource exploration, and intelligence gathering. With the development of target miniaturization, low power, and acoustic stealth technology, the stealth performance of underwater targets is getting better and better, and the corresponding target echo intensity of non-cooperative targets is decreasing year by year. In addition, the complexity, variability, and instability of the underwater acoustic field pose significant obstacles to underwater target detection in complex marine environments, as targets can be submerged in the background, making target detection extremely difficult. Therefore, it is necessary to study new systems and methods for non-cooperative target detection in complex marine environments to improve the detection performance of existing sonar systems.

[0003] Orthogonal Frequency Division Multiplexing (OFDM) technology is characterized by high spectral efficiency and significant multipath interference suppression, and is currently widely used in wireless positioning systems. The location of non-cooperative targets can be determined using distance and angle information; therefore, joint distance and angle parameter estimation has become a research hotspot, and many techniques have been proposed. Among these techniques, the two-dimensional (multiple signal classification, MUSIC) algorithm is the most well-known. This algorithm includes the formation of the covariance matrix, eigenvalue decomposition (EVD), and spectral peak search. Through two-dimensional search, the target's distance and orientation information can be determined.

[0004] However, existing MUSIC-based algorithms are computationally complex due to the two-dimensional search problem, which is not conducive to practical applications. Furthermore, most existing algorithms are based on passive detection mode or continuous wave detection mode, which cannot achieve high spectrum utilization. In addition, in the case of multiple targets, there will be mutual interference between targets, which makes it difficult to estimate the distance and angle of non-cooperative targets with high resolution. Summary of the Invention

[0005] To address the aforementioned shortcomings of existing technologies, this invention proposes a joint estimation method for sonar target range and angle based on OFDM signals. This method introduces the OFDM system into the field of sonar detection to improve sonar detection performance and anti-interference capabilities. The method includes:

[0006] An element in a sonar array transmits an orthogonal frequency division multiplexed signal toward a target within the sonar detection area;

[0007] Each array element receives the target echo signal, forms an echo signal matrix, establishes a two-dimensional output signal model, and performs dimensionality reduction processing to obtain a one-dimensional output signal model.

[0008] Based on the obtained one-dimensional output signal model, an optimization function is constructed;

[0009] A greedy algorithm is used to solve the constructed optimization function to obtain the target's distance and angle, thereby achieving target estimation.

[0010] As an improvement to the above technical solution, one element of the sonar array transmits an orthogonal frequency division multiplexed signal to a target within the sonar detection area; the specific process is as follows:

[0011] Assume the sonar array is a uniform linear array, which includes P array elements and has a one-to-many transmit mode. The distance between array elements is d = λ / 2, where λ is the subcarrier wavelength.

[0012] Assuming the transmitted signal has a bandwidth of B and N subcarriers, it can be represented as:

[0013]

[0014] Among them, W k For the complex weight value corresponding to the k-th subcarrier, satisfying Δf is the subcarrier spacing, Δf = 1 / T; t is the signal time; T is the length of the transmitted signal;

[0015] Assuming signal sampling interval T s =T / N=1 / B, then the discrete OFDM signal S(nT) s ) represents

[0016]

[0017] Where 0 ≤ n ≤ N-1;

[0018] Representing the discrete OFDM signal in vector form yields the discrete OFDM signal vector S:

[0019] S = [S(0), S(T)] s ),...,S((N-1)T s )]

[0020] Where S is the modulation symbol W = [W0, W1, ..., W N-1 The inverse Fourier transform of N sampling points is obtained; W N-1The modulation symbol for the (N-1)th sampling point;

[0021] This leads to the orthogonal frequency division multiplexing signal s. t (t) is

[0022]

[0023] Among them, f c For sonar carrier frequency;

[0024] One element in the sonar array transmits the aforementioned orthogonal frequency division multiplexed signal s to a target within the sonar detection area. t (t).

[0025] As an improvement to the above technical solution, each array element receives the target echo signal, forms an echo signal matrix, establishes a two-dimensional output signal model, and performs dimensionality reduction processing to obtain a one-dimensional output signal model; the specific process is as follows:

[0026] Assuming the target area can be reached by angle θ h =hρ θ and distance R k =lρ R The pixels are divided into angle and distance units; where h = 0, 1, ..., H-1; h is the angle unit index; ρ θ H is the angular resolution; H is the number of angular pixel units; l = 0, 1, ..., L-1; L is the number of distance pixel units; l is the distance unit index; ρ R For distance resolution;

[0027] Then the target echo signal G received by the p-th array element h,p (n) is represented as

[0028]

[0029] Among them, a h (l) represents the reflection coefficient of the l-th distance pixel unit and the h-th angle pixel unit; τ l,0 =lρ t ;ρ t =T s / 2=1 / (2B); p is the element index; d is the distance between elements; c is the signal propagation speed; θh is the h-th angle; W k The complex weights corresponding to the k-th subcarrier are: Δf is the subcarrier spacing; T s n is the signal sampling interval; n is the sampling point index;

[0030] in, Δl h,p This represents the distance cell offset of the p-th array element relative to the reference array element. f c For sonar carrier frequency;

[0031] yes l+Δl h,p Point cyclic shift;

[0032] in,

[0033] echo signal G h,p (n) can be represented in matrix form to obtain the echo signal matrix G:

[0034] G = FAS (5)

[0035] Where A is a reflection matrix of size H×L, whose components correspond to the target backscattering coefficients in the l-th range cell at angle θ; F is a matrix of size P×H; and S is a discrete OFDM signal vector.

[0036] Where F = [F0, F1, ..., F p ,...,F P-1 ] T The Fourier transform matrix representing the angular dimension;

[0037] in,

[0038] S = [S0,S1,...,S] n ,...,S N-1 Specifically, it can be represented as

[0039]

[0040] Wherein, G is the echo signal matrix of size P×H, and it is used as the two-dimensional output signal model to complete the establishment of the two-dimensional output signal model;

[0041] The above two-dimensional output signal model is reduced in dimension to a one-dimensional output signal model, thus obtaining the one-dimensional output signal model.

[0042]

[0043] in, This is the vectorized column vector of the reflection matrix A of size H×L;

[0044] in, vec(.) represents the vectorization of a matrix;

[0045] Φ is the measurement matrix. (.)H The conjugate of the matrix; For Kronecker product.

[0046] As an improvement to the above technical solution, an optimization function is constructed based on the obtained one-dimensional output signal model; a greedy algorithm is used to solve the constructed optimization function to obtain the target's distance and angle, thereby achieving target estimation; the specific process is as follows:

[0047] Based on the obtained one-dimensional output signal model

[0048]

[0049] in, Φ is the measurement matrix.

[0050] in, Let be the vectorized column vector of the reflection matrix A of size H×L; vect(·) represents matrix vectorization; (.) H The conjugate of the matrix; For Kronecker product;

[0051] Extract the measurement matrix Φ from it. Construct an optimization function;

[0052]

[0053] Where ξ is the error tolerance; ||·|| F Denotes the F norm of a matrix;

[0054] A greedy method is used to solve the constructed optimization function, resulting in... And according to Obtain the target's distance R = c·n / 2B and angle θ = sin -1 (2πh / H), thus enabling the estimation of the target parameters.

[0055] The present invention also provides a sonar target range-angle joint estimation device based on OFDM signals, the device comprising:

[0056] The signal acquisition module is used to transmit orthogonal frequency division multiplexed signals from a specific element in the sonar array to a target within the sonar detection area.

[0057] The signal model acquisition module is used for each array element to receive the target echo signal, form an echo signal matrix, establish a two-dimensional output signal model, and perform dimensionality reduction processing to obtain a one-dimensional output signal model.

[0058] The optimization function construction module is used to construct optimization functions based on the obtained one-dimensional output signal model; and

[0059] The joint distance and angle estimation module is used to solve the constructed optimization function using a greedy algorithm to obtain the target's distance and angle, thereby estimating the target's position.

[0060] The present invention also provides a computer device for joint estimation of target angle and distance, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the method when executing the computer program.

[0061] The present invention also provides a computer-readable storage medium comprising a stored computer program; wherein, when the computer program is executed, it controls the device in which the computer-readable storage medium is located to perform the method described thereon.

[0062] The present invention also provides an information data processing terminal, which is used to implement the method described herein.

[0063] The advantages of this invention compared to the prior art are:

[0064] The estimation method of this invention achieves good noise and sidelobe suppression effects for target distance and target angle. Mathematical analysis and simulation results demonstrate the feasibility of the algorithm. The method of this invention does not require EVD calculation or peak search, reducing the computational load of the algorithm. It uses OFDM waveforms to suppress multipath interference, reduce sidelobes, and ensure the orthogonality between subcarriers. Applying compressed sensing super-resolution technology to OFDM systems can significantly improve the parameter estimation of the system. Attached Figure Description

[0065] Figure 1 This is a schematic diagram comparing the target distance estimation results of the sonar target distance and angle joint estimation method based on orthogonal frequency division multiplexing of the present invention with the target distance estimation of the traditional MUSIC algorithm;

[0066] Figure 2 This is a schematic diagram comparing the target angle and azimuth estimation results of the sonar target range and angle joint estimation method based on orthogonal frequency division multiplexing of the present invention with the target angle and azimuth estimation of the traditional MUSIC algorithm;

[0067] Figure 3 This is a schematic diagram comparing the RMSE curve of the sonar target range-angle joint estimation method based on orthogonal frequency division multiplexing of the present invention with the signal-to-noise ratio curve with the RMSE curve of the traditional MUSIC algorithm.

[0068] Figure 4This is a flowchart of the sonar target range and angle joint estimation method based on orthogonal frequency division multiplexing of the present invention. Detailed Implementation

[0069] The present invention will now be further described in conjunction with the accompanying drawings and examples.

[0070] like Figure 4 As shown, this invention provides a joint range and angle estimation method for sonar targets based on OFDM signals. This method is a novel range and azimuth estimation algorithm for sonar based on OFDM waveforms. In non-cooperative positioning environments, based on a uniform linear array, to address the low-resolution problem caused by Fast Fourier Transform (FFT) operations for finite element angle estimation, this invention proposes a super-resolution joint range and angle estimation method based on compressed sensing. It utilizes the sparsity of the non-cooperative target space as a global metric, transforming the parameter estimation problem into a sparse solution problem. The proposed method avoids eigenvalue decomposition (EVD) or peak search operations, significantly reducing computational complexity, while exhibiting good noise and sidelobe robustness.

[0071] like Figure 4 As shown, this invention provides a method for joint estimation of sonar target range and angle based on OFDM signals, the method comprising:

[0072] An element in a sonar array transmits an orthogonal frequency division multiplexed signal toward a target within the sonar detection area;

[0073] Specifically, assume the sonar receiving array is a uniform linear array comprising P elements, with the distance between the elements being d = λ / 2, where λ is the subcarrier wavelength; consider a one-to-many transmit / receive mode.

[0074] Assume the transmitted signal is a signal s with bandwidth B and N subcarriers. t (t) 1 Represented as:

[0075]

[0076] Among them, W k (k = 0, 1, ..., N-1) represents the complex weights corresponding to the nth subcarrier, satisfying... Δf is the subcarrier spacing; t is the signal time; T is the length of the transmitted signal;

[0077] As can be seen from the properties of OFDM, in order to avoid mutual interference between the subcarriers of OFDM signals, the orthogonality of the subcarriers must be ensured, which requires satisfying 1 / T=Δf;

[0078] Assuming signal sampling interval T s =T / N=1 / B, the discrete OFDM signal is represented as

[0079]

[0080] Wherein S(nT) s (n) represents a discrete OFDM signal; 0 ≤ n ≤ N-1

[0081] The discrete OFDM signal is expressed in vector form, resulting in the discrete OFDM signal vector S:

[0082] S = [S(0), S(T)] s ),...,S((N-1)T s )]

[0083] S is the modulation symbol. N 1 Obtained by point inverse Fourier transform; where, For the Nth 1 The modulation symbol is -1 point. This takes into account the sonar carrier frequency f. c Then, the orthogonal frequency division multiplexed signal s is obtained. t (t) is

[0084]

[0085] Among them, f c For sonar carrier frequency

[0086] One of the array elements in the sonar array transmits the orthogonal frequency division multiplexed signal obtained above to the target in the sonar detection area.

[0087] Each array element receives the target echo signal, forms an echo signal matrix, establishes a two-dimensional output signal model, and performs dimensionality reduction processing to obtain a one-dimensional output signal model.

[0088] Specifically, assuming the target area can be accessed via angle θ h =hρ θ and distance R k =lρ R The pixels are divided into angle and distance units; where h = 0, 1, ..., H-1; h is the angle unit index; ρ θ H is the angular resolution; H is the number of angular pixel units; l = 0, 1, ..., L-1; L is the number of distance pixel units; l is the distance unit index; ρ R For distance resolution;

[0089] Then the target echo signal G received by the p-th array element h,p (n) is represented as

[0090]

[0091] Among them, ah (l) represents the reflection coefficient of the l-th distance pixel unit and the h-th angle pixel unit; τ l,0 =lρ t ;ρ t =T s / 2=1 / (2B); p is the element index; d is the distance between elements; c is the signal propagation speed; θ h For the h-th angle; W k The complex weights corresponding to the k-th subcarrier are: Δf is the subcarrier spacing; T s n is the signal sampling interval; n is the sampling point index;

[0092] in, Δl h,p This represents the distance cell offset of the p-th array element relative to the reference array element. f c For sonar carrier frequency;

[0093] yes l+Δl h,p Point cyclic shift;

[0094] in,

[0095] echo signal G h,p (n) can be represented in matrix form to obtain the echo signal matrix G:

[0096] G = FAS (5)

[0097] Where A is a reflection matrix of size H×L, whose components correspond to the target backscattering coefficients in the l-th range cell at angle θ; F is a matrix of size P×H; and S is a discrete OFDM signal vector.

[0098] Where F = [F0, F1, ..., F p ,...,F P-1 ] T The Fourier transform matrix representing the angular dimension;

[0099] in, S = [S0,S1,...,S] n ,...,S N-1 Specifically, it can be represented as

[0100]

[0101] Wherein, G is the echo signal matrix of size P×H, which is a known echo signal matrix, and it is used as a two-dimensional output signal model to complete the establishment of the two-dimensional output signal model;

[0102] The above two-dimensional output signal model is reduced in dimension to a one-dimensional output signal model, thus obtaining the one-dimensional output signal model.

[0103]

[0104] in, This is the vectorized column vector of the reflection matrix A of size H×L;

[0105] in, vec(.) represents the vectorization of a matrix, that is, arranging the column vectors of the matrix into a single column vector in order;

[0106] Φ is the measurement matrix. (.) H The conjugate of the matrix; For Kronecker product.

[0107] Based on the obtained one-dimensional output signal model, an optimization function is constructed;

[0108] A greedy algorithm is used to solve the constructed optimization function to obtain the target's distance and angle, thereby achieving target estimation.

[0109] Specifically, based on the obtained one-dimensional output signal model

[0110]

[0111] in, Φ is the measurement matrix. Let be the vectorized column vector of the reflection matrix A of size H×L; vect(·) represents matrix vectorization; (.) H The conjugate of the matrix; For Kronecker product;

[0112] Extract the measurement matrix Φ from it. Construct an optimization function;

[0113]

[0114] Where ξ is the error tolerance; ||·|| F Denotes the F norm of the matrix; ||·||1 represents the l1 norm;

[0115] A greedy method is used to solve the constructed optimization function, resulting in... And according to Obtain the target's distance R = c·n / 2B and angle θ = sin-1 (2πh / H), thereby achieving the estimation of the target parameters;

[0116] Since H ≥ P, solving for A becomes an ill-conditioned problem, requiring us to utilize prior information to obtain the optimal solution. According to compressed sensing theory, if A is a sparse vector, it can be obtained with extremely high probability from... Recovery

[0117] For sonar detection, since the number of non-cooperative targets is much smaller than the number of grid elements in the target detection area, the distribution of non-cooperative targets in the target detection area is highly sparse. This characteristic can be used to transform the distance and orientation estimation problem into a process of solving an optimization function. Conventional algorithms for solving optimization problems generally include convex optimization methods and greedy algorithms. Since convex optimization algorithms suffer from excessive computation, this embodiment uses a greedy algorithm to solve the optimization function.

[0118] The present invention also provides a sonar target range-angle joint estimation device based on OFDM signals, the device comprising:

[0119] The signal acquisition module is used to transmit orthogonal frequency division multiplexed signals from a specific element in the sonar array to a target within the sonar detection area.

[0120] The signal model acquisition module is used for each array element to receive the target echo signal, form an echo signal matrix, establish a two-dimensional output signal model, and perform dimensionality reduction processing to obtain a one-dimensional output signal model.

[0121] The optimization function construction module is used to construct optimization functions based on the obtained one-dimensional output signal model; and

[0122] The joint distance and angle estimation module is used to solve the constructed optimization function using a greedy algorithm to obtain the target's distance and angle, thereby estimating the target's position.

[0123] The present invention also provides a computer device for joint estimation of target angle and distance, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the method when executing the computer program.

[0124] The present invention also provides a computer-readable storage medium comprising a stored computer program; wherein, when the computer program is executed, it controls the device in which the computer-readable storage medium is located to perform the method described thereon.

[0125] The present invention also provides an information data processing terminal, which is used to implement the method described herein.

[0126] Performance Analysis

[0127] In this embodiment, the effectiveness of the proposed super-resolution estimation and azimuth estimation based on joint estimation of OFDM sonar target range and angle will be verified. It is assumed that the OFDM signal with a carrier frequency of 1 GHz is sampled at a sampling rate of 1 MHz, the subcarrier weights are binary PN value sequences with magnitudes of -1 and 1, the uniform linear array consists of 6 elements, and the distance between each element is d = λ / 2 = 15 cm. The element located at coordinates (0, 0) transmits the OFDM signal, and Gaussian white noise is added to the echo signal received by each element.

[0128] First, comparing the proposed method with the traditional 2-D MUSIC algorithm in terms of distance estimation performance, with a signal-to-noise ratio (SNR) of 10 dB and a target count of 3, the results are as follows: Figure 1 As shown. From Figure 1 As can be seen, due to noise and multi-target interference, the target range estimation result obtained by the traditional two-dimensional MUSIC algorithm has high sidelobes, and the echoes of weak targets are submerged in the sidelobes of nearby strong target signals. The first two targets out of three cannot be separated, leading to a decrease in detection rate. In contrast, the OFDM waveform-based estimation method proposed in this invention can recover all target signals, with sidelobes below -35dB. This demonstrates that the OFDM waveform-based super-resolution estimation method has higher range parameter estimation performance than the MUSIC method.

[0129] Next, we analyzed the azimuth-dimensional super-resolution performance of the algorithm. Applying the MUSIC algorithm, the method proposed in this invention was used for 4x, 8x, and 16x super-resolution estimation, and the results are as follows. Figure 2 As shown, traditional MUSIC algorithms can obtain angle estimation results for three targets, but the sidelobe of the MUSIC algorithm is -50dB. The method proposed in this invention can achieve a sidelobe suppression performance of -100dB, and has higher angular resolution.

[0130] The noise immunity of the method of this invention is verified below. The root mean square error (RMSE) of the interval between the estimated and actual angle positions in 200 experiments is used as the standard for measuring accuracy. The RMSE versus signal-to-noise ratio (SNR) curve is shown below when the signal-to-noise ratio is between -20 and 20 dB. Figure 3 As shown, with the increase of SNR, the estimation error RMSE of the two algorithms gradually decreases, but the RMSE of the algorithm proposed in this invention is always lower than that of the MUSIC algorithm, indicating that the method proposed in this invention has better noise suppression performance than the MUSIC algorithm.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the embodiments, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of the present invention do not depart from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for joint estimation of range and angle of a sonar target based on OFDM signals, the method comprising: An element in a sonar array transmits an orthogonal frequency division multiplexed signal toward a target area; Each array element receives the target echo signal, forms an echo signal matrix, establishes a two-dimensional output signal model, and performs dimensionality reduction processing to obtain a one-dimensional output signal model. Based on the obtained one-dimensional output signal model, an optimization function is constructed; A greedy algorithm is adopted, which uses the sparsity of the non-cooperative target space as a global metric to transform the parameter estimation problem into a sparse solution problem. The constructed optimization function is solved to obtain the target's distance and angle, thereby achieving target estimation. Each array element receives the target echo signal, forming an echo signal matrix, establishing a two-dimensional output signal model, and then performing dimensionality reduction processing to obtain a one-dimensional output signal model; the specific process is as follows: Assuming the target area can be reached by angle θ h =hρ θ and distance R k =lρ R The pixels are divided into angle and distance units; where h = 0, 1, ..., H-1; h is the angle unit index; ρ θ H is the angular resolution; H is the number of angular pixel units; l = 0, 1, ..., L-1; L is the number of distance pixel units; l is the distance unit index; ρ R For distance resolution; Then the target echo signal G received by the p-th array element h,p (n) is represented as Among them, a h (l) represents the reflection coefficient of the l-th distance pixel unit and the h-th angle pixel unit; τ l,0 =lρ t ;ρ t =T s / 2=1 / (2B); p is the element index; d is the distance between elements; c is the signal propagation speed; θ h For the h-th angle; W k The complex weights corresponding to the k-th subcarrier are: Δf is the subcarrier spacing; T s n is the signal sampling interval; n is the sampling point index; in, Δl h,p This represents the distance cell offset of the p-th array element relative to the reference array element. f c For sonar carrier frequency; yes l+Δl h,p Point cyclic shift; in, echo signal G h,p (n) can be represented in matrix form to obtain the echo signal matrix G: G = FAS (5) Where A is a reflection matrix of size H×L, whose components correspond to the target backscattering coefficients in the l-th range cell at angle θ; F is a matrix of size P×H; and S is a discrete OFDM signal vector. Where F = [F0, F1, ..., F p ,...,F P-1 ] T The Fourier transform matrix represents the angular dimension. in, S = [S0,S1,...,S] n ,...,S N-1 Specifically, it can be represented as Wherein, G is the echo signal matrix of size P×H, and it is used as the two-dimensional output signal model to complete the establishment of the two-dimensional output signal model; The above two-dimensional output signal model is reduced in dimension to a one-dimensional output signal model, thus obtaining the one-dimensional output signal model. in, This is the vectorized column vector of the reflection matrix A of size H×L; in, vec(.) represents the vectorization of a matrix; Φ is the measurement matrix. (.) H The conjugate of the matrix; For Kronecker product; Based on the obtained one-dimensional output signal model, an optimization function is constructed. A greedy algorithm is employed, using the sparsity of the non-cooperative target space as a global metric, to transform the parameter estimation problem into a sparse solution problem. The constructed optimization function is then solved to obtain the target's distance and angle, thereby achieving target position estimation. The specific process is as follows: Based on the obtained one-dimensional output signal model in, Φ is the measurement matrix. in, Let be the vectorized column vector of the reflection matrix A of size H×L; vect(·) represents matrix vectorization; (.) H The conjugate of the matrix; For Kronecker product; Extract the measurement matrix Φ from it. Construct an optimization function; Where ξ is the error tolerance; ||·|| F Denotes the F norm of a matrix; A greedy method is used to solve the constructed optimization function, resulting in... And according to Obtain the target's distance R = c·n / 2B and angle θ = sin -1 (2πh / H), thus enabling the estimation of the target parameters.

2. The method for joint estimation of sonar target range and angle based on OFDM signals according to claim 1, characterized in that, A specific element in the sonar array transmits an orthogonal frequency division multiplexed signal to a target within the sonar detection area; the specific process is as follows: Assume the sonar array is a uniform linear array, which includes P array elements and has a one-to-many transmit mode. The distance between array elements is d = λ / 2, where λ is the subcarrier wavelength. Assuming the transmitted signal has a bandwidth of B and N subcarriers, it can be represented as: Among them, W k For the complex weights corresponding to the k-th subcarrier, satisfying Δf is the subcarrier spacing, Δf = 1 / T; t is the signal time; 0 ≤ t ≤ T; T is the length of the transmitted signal; Assuming signal sampling interval T s =T / N=1 / B, then the discrete OFDM signal S(nT) s ) represents Where 0 ≤ n ≤ N-1; Representing the discrete OFDM signal in vector form yields the discrete OFDM signal vector S: S=[S(0),S(T s ),...,S((N-1)T s )] Where S is the modulation symbol W = [W0, W1, ..., W N-1 The inverse Fourier transform of N sampling points is obtained; W N-1 The modulation symbol for the (N-1)th sampling point; This leads to the orthogonal frequency division multiplexing signal s. t (t) is Among them, f c For sonar carrier frequency; One element in the sonar array transmits the aforementioned orthogonal frequency division multiplexed signal s to a target within the sonar detection area. t (t).

3. A sonar target range and angle joint estimation device based on OFDM signals, characterized in that, The device includes: The signal acquisition module is used to transmit orthogonal frequency division multiplexed signals from a specific element in the sonar array to a target within the sonar detection area. The signal model acquisition module is used for each array element to receive the target echo signal, form an echo signal matrix, establish a two-dimensional output signal model, and perform dimensionality reduction processing to obtain a one-dimensional output signal model. The optimization function construction module is used to construct optimization functions based on the obtained one-dimensional output signal model; and The joint distance and angle estimation module is used to transform the parameter estimation problem into a sparse solution problem by using a greedy algorithm and taking the sparsity of the non-cooperative target space as a global metric. It solves the constructed optimization function to obtain the target's distance and angle, thereby achieving target estimation. Each array element receives the target echo signal, forming an echo signal matrix, establishing a two-dimensional output signal model, and then performing dimensionality reduction processing to obtain a one-dimensional output signal model; the specific process is as follows: Assuming the target area can be reached by angle θ h =hρ θ and distance R k =lρ R The pixels are divided into angle and distance units; where h = 0, 1, ..., H-1; h is the angle unit index; ρ θ H is the angular resolution; H is the number of angular pixel units; l = 0, 1, ..., L-1; L is the number of distance pixel units; l is the distance unit index; ρ R For distance resolution; Then the target echo signal G received by the p-th array element h,p (n) is represented as Among them, a h (l) represents the reflection coefficient of the l-th distance pixel unit and the h-th angle pixel unit; τ l,0 =lρ t ;ρ t =T s / 2=1 / (2B); p is the element index; d is the distance between elements; c is the signal propagation speed; θ h For the h-th angle; W k The complex weights corresponding to the k-th subcarrier are: Δf is the subcarrier spacing; T s n is the signal sampling interval; n is the sampling point index; in, Δl h,p This represents the distance cell offset of the p-th array element relative to the reference array element. f c For sonar carrier frequency; yes l+Δl h,p Point cyclic shift; in, echo signal G h,p (n) can be represented in matrix form to obtain the echo signal matrix G: G = FAS (5) Where A is a reflection matrix of size H×L, whose components correspond to the target backscattering coefficients in the l-th range cell at angle θ; F is a matrix of size P×H; and S is a discrete OFDM signal vector. Where F = [F0, F1, ..., F p ,...,F P-1 ] T The Fourier transform matrix represents the angular dimension. in, S = [S0,S1,...,S] n ,...,S N-1 Specifically, it can be represented as Wherein, G is the echo signal matrix of size P×H, and it is used as the two-dimensional output signal model to complete the establishment of the two-dimensional output signal model; The above two-dimensional output signal model is reduced in dimension to a one-dimensional output signal model, thus obtaining the one-dimensional output signal model. in, This is the vectorized column vector of the reflection matrix A of size H×L; in, vec(.) represents the vectorization of a matrix; Φ is the measurement matrix. (.) H The conjugate of the matrix; For Kronecker product; Based on the obtained one-dimensional output signal model, an optimization function is constructed. A greedy algorithm is employed, using the sparsity of the non-cooperative target space as a global metric, to transform the parameter estimation problem into a sparse solution problem. The constructed optimization function is then solved to obtain the target's distance and angle, thereby achieving target position estimation. The specific process is as follows: Based on the obtained one-dimensional output signal model in, Φ is the measurement matrix. in, Let be the vectorized column vector of the reflection matrix A of size H×L; vect(·) represents matrix vectorization; (.) H The conjugate of the matrix; For Kronecker product; Extract the measurement matrix Φ from it. Construct an optimization function; Where ξ is the error tolerance; ||·|| F Denotes the F norm of a matrix; A greedy method is used to solve the constructed optimization function, resulting in... And according to Obtain the target's distance R = c·n / 2B and angle θ = sin -1 (2πh / H), thus enabling the estimation of the target parameters.

4. A computer device for joint estimation of target angle and distance, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the method as claimed in claim 1 or 2.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program; wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform the method as described in claim 1 or 2.

6. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the method as described in claim 1 or 2.

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