A method for underwater acoustic environment perception based on multi-element sparse channel estimation

Abstract

The application discloses a kind of based on multi-array sparse channel estimation underwater acoustic environment perception method, underwater acoustic receiving end is received through multi-array array signal that is propagated through underwater acoustic multipath channel;The received signal of each array element is subjected to Hilbert transform, and analytic signal is obtained, and the cross-correlation function of analytic signal and transmitting signal is calculated;Based on cross-correlation function, using the orthogonal matching pursuit algorithm of combining constant false alarm detection dynamic threshold, the sparse channel parameters of each array element are estimated, including path amplitude and time delay;Utilize the sparse channel parameters of multi-array array, construct array response vector, estimate the angle of arrival of multipath signal, including direction angle and pitch angle by space spectrum peak value search;Based on the angle of arrival, amplitude and time delay of direct path and reflected path, the underwater environment structure is inverted by ray acoustics theory, including reflection point distance and reflection surface normal vector, realize the three-dimensional perception of underwater reflector.The application can improve multipath resolution, effectively reduce false alarm and miss detection.

Description

Technical Field

[0001] This invention relates to the field of underwater acoustic signal processing, and in particular to an underwater acoustic environment perception method based on multi-element sparse channel estimation. Background Technology

[0002] Underwater acoustic environment sensing is a key technology for realizing underwater communication, detection, and navigation. Shallow water acoustic channels exhibit significant multipath effects. Traditional methods such as matched filtering and least squares estimation suffer from low resolution and poor adaptability, making it difficult to accurately extract path parameters in high-noise, multipath-dense environments. While sparse recovery theories such as compressed sensing offer new approaches, their iteration stopping criteria lack adaptability, easily leading to false alarms or missed detections, thus limiting the accuracy and reliability of environment inversion. Furthermore, most channel estimation methods fail to form an efficient and robust processing link with downstream environmental geometry inversion tasks, making it difficult to reliably convert multipath parameters into quantitative sensing of physical characteristics such as reflector position and attitude. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides an underwater acoustic environment perception method based on multi-element sparse channel estimation, which can dynamically adjust the threshold according to different underwater acoustic noise environments, thereby improving the reliability of multipath parameter estimation and the accuracy of environment perception.

[0004] The technical solution adopted in this invention is as follows:

[0005] A method for underwater acoustic environment perception based on multi-element sparse channel estimation includes the following steps:

[0006] S1: The underwater acoustic receiver receives signals propagating through underwater acoustic multipath channels via a multi-element array, thus obtaining multi-channel received signals;

[0007] S2: Perform Hilbert transform on the received signal of each array element to obtain the analytic signal, and calculate the cross-correlation function between the analytic signal and the transmitted signal;

[0008] S3: Based on the cross-correlation function, an orthogonal matching pursuit algorithm combining constant false alarm rate detection dynamic threshold is used to estimate the sparse channel parameters of each array element, including path amplitude and time delay;

[0009] S4: Utilize the sparse channel parameters of the multi-element array to construct the array response vector, and estimate the angle of arrival of the multipath signal, including the direction angle and elevation angle, through spatial spectrum peak search;

[0010] S5: Based on the arrival angle, amplitude, and time delay information of the direct path and the reflection path, the underwater environment structure is inverted through ray acoustic theory, including the three-dimensional distance of the reflection point and the three-dimensional normal vector of the reflection surface, to realize the three-dimensional perception of the underwater reflector.

[0011] Furthermore, step S3 includes the following sub-steps:

[0012] S3.1: Initialize the sparse channel parameter set to an empty set, and initialize the array element number m and the iteration number i to 1;

[0013] S3.2: Select a segment of the received signal that has no signal or only noise as a reference unit, and calculate the noise variance of that segment of the signal;

[0014] S3.3: Calculate the constant false alarm detection dynamic threshold based on noise variance and false alarm rate;

[0015] S3.4: Determine if m is not greater than the number of array elements. If it is, execute S3.5; otherwise, output the sparse parameter set.

[0016] S3.5: Assign the m-th row of the matrix composed of cross-correlation functions to the current residual;

[0017] S3.6: Determine if the iteration number i is not greater than the maximum iteration number. If it is, find the time delay corresponding to the maximum value in the current residual, calculate the corresponding path amplitude, and execute S3.7; otherwise, let i=1, m=m+1, and return to S3.4.

[0018] S3.7: Expand the sparse channel parameter set and dictionary;

[0019] S3.8: Recalculate the residual for the next iteration;

[0020] S3.9: Determine whether the maximum value in the residual of the next iteration is not greater than the constant false alarm detection dynamic threshold. If yes, let i=1, m=m+1, and return to S3.4; otherwise, let i=i+1 and return to S3.6.

[0021] Furthermore, for the sparse parameter set obtained in S3, the validity of each path element is verified using the maximum time delay constraint between array elements.

[0022] Further, step S4 includes the following sub-steps:

[0023] S4.1: Calculate the relative time delay for each path in the sparse parameter set;

[0024] S4.2: Construct an array response vector based on the path magnitude in the sparse parameter set and the relative time delay;

[0025] S4.3: Constructing the covariance matrix of the path based on the array response vector:

[0026] S4.4: Calculate the spatial spectrum based on the array manifold vectors and the covariance matrix;

[0027] S4.5: By searching for peak values ​​in the spatial spectrum, the estimated values ​​of the azimuth and elevation angles for each path are obtained.

[0028] Further, S5 includes:

[0029] S5.1: For a direct path, the distance of the direct path is calculated by calculating the two-way propagation time difference;

[0030] S5.2: For the reflection path, the three-dimensional coordinates of the reflection point and the three-dimensional normal vector of the reflection surface are inverted using the arrival angle, pitch angle and time delay difference relative to the direct path of the reflection path.

[0031] Furthermore, for the direct path, the distance of the direct path is calculated by calculating the two-way propagation time difference, specifically including:

[0032] S5.1.1: The array sends a linear frequency modulated signal at time t0 and records the corresponding timestamp t0;

[0033] S5.1.2: The beacon receives the linear frequency modulated signal at time t1, and after hardware processing for time t... proc The linear frequency modulation signal is then transmitted back.

[0034] S5.1.3: The array terminal receives the linear frequency modulation signal at time t2 and records the corresponding timestamp t2;

[0035] S5.1.4: Total time difference for array-side calculation;

[0036] S5.1.5: Calculate the distance at the array end.

[0037] Furthermore, for the reflection path, using the angle of arrival, elevation angle, and time delay difference relative to the direct path of the reflection path, the three-dimensional coordinates of the reflection point on the reflecting surface and the three-dimensional normal vector of the reflecting surface are inverted, specifically including:

[0038] S5.2.1: Calculate the time delay difference between each path p and the direct path;

[0039] S5.2.2: Calculate the distance from the reflection point to the center of the array;

[0040] S5.2.3: Calculate the unit incident vector and the unit reflected vector;

[0041] S5.2.4: Calculate the three-dimensional normal vector of the reflecting surface and normalize it.

[0042] An underwater acoustic environment sensing system based on multi-element sparse channel estimation is disclosed. This system is used to implement an underwater acoustic environment sensing method based on multi-element sparse channel estimation. The system includes:

[0043] The underwater acoustic transmitter is used to transmit linear frequency modulated signals.

[0044] The underwater acoustic receiver includes a multi-element array, a signal processing unit, and a display unit; wherein the multi-element array is used to receive signals; the signal processing unit is used to perform signal preprocessing, sparse channel parameter estimation, angle of arrival estimation, and environment inversion; and the display unit is used to display the three-dimensional perception results.

[0045] An electronic device, comprising:

[0046] One or more processors;

[0047] A storage device for storing one or more programs that, when executed by the electronic device, enable the electronic device to implement an underwater acoustic environment perception method based on multi-element sparse channel estimation.

[0048] A computer-readable storage medium having a program stored thereon that, when executed by a processor, implements an underwater acoustic environment perception method based on multi-element sparse channel estimation.

[0049] The beneficial effects of this invention are as follows:

[0050] (1) By using a multi-element joint processing framework, we can make full use of spatial information to achieve sparse estimation of the path arrival angle and improve multipath resolution.

[0051] (2) The constant false alarm detection mechanism is combined with the orthogonal matching pursuit algorithm to adapt to the noisy environment and effectively reduce false alarms and missed detections;

[0052] (3) A complete technical link is formed from sparse channel estimation, angle of arrival calculation to environmental reconstruction and inversion, so as to realize high-precision three-dimensional perception of underwater reflector structure. Attached Figure Description

[0053] Figure 1 This is a flowchart of the underwater acoustic environment perception method based on multi-element sparse channel estimation in an embodiment of the present invention.

[0054] Figure 2 This is an iterative flowchart of the orthogonal matching tracking algorithm that combines constant false alarm rate detection with dynamic threshold in an embodiment of the present invention.

[0055] Figure 3 This is a schematic diagram of an underwater acoustic environment sensing system based on multi-element sparse channel estimation, according to an embodiment of the present invention.

[0056] Figure 4 This is a schematic diagram of six real signals received by a six-element hydrophone array in a specific embodiment of the present invention.

[0057] Figure 5 The diagram shows the environmental inversion and reconstruction results obtained from steps one through five of a single experiment.

[0058] Figure 6 The image shows the environmental inversion and reconstruction results (in a relative coordinate system) obtained from steps one through five of multiple experiments. Detailed Implementation

[0059] The present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments. The purpose and effects of the present invention will become clearer. It should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0060] This invention provides a method for underwater acoustic environment perception based on multi-element sparse channel estimation, applicable to shallow water acoustic environments. For example... Figure 1 As shown, the method includes the following steps one through five.

[0061] Step 1: The underwater acoustic transmitter transmits a linear frequency modulated signal, and the underwater acoustic receiver receives the signal propagating through the underwater acoustic multipath channel via a multi-element array, thus obtaining a multi-channel received signal.

[0062] Among them, the linear frequency modulated signal s transmitted by the underwater acoustic transmitter tx (t) is as follows:

[0063]

[0064] Where x(t) represents a unit amplitude complex linear frequency modulated signal, T is the duration of the linear frequency modulated signal, f0 is the starting frequency, and K is the linear frequency modulation slope.

[0065] The receiver has M array elements receiving signals, where the m-th array element receives the signal s. rx,m (t) is:

[0066]

[0067] Where * represents the convolution symbol, h m (t) represents the impulse response of the underwater acoustic multipath channel of the m-th element, n m (t) represents the additive noise of the m-th array element. The impulse response h of the underwater acoustic multipath channel of the m-th array element is... m (t) can be sparsely represented as:

[0068]

[0069] Represent the received signals of all array elements in matrix form:

[0070]

[0071] Among them, A p,m and τ p,mLet represent the amplitude and delay of the p-th path received by the m-th array element, respectively; δ(∙) represents the impulse function.

[0072] Step 2: Perform Hilbert transform on the received signal of each array element to obtain the analytic signal, and calculate the cross-correlation function between the analytic signal and the transmitted signal.

[0073] At the receiving end, a Hilbert transform is first performed to obtain the analytic signal y of the single-sideband of the m-th array element. rx,m (t):

[0074]

[0075] Represent the analytical signals of all array elements as matrices:

[0076]

[0077] The analytic signal of each array element is cross-correlated with the transmitted unit amplitude linear frequency modulated signal x(t):

[0078]

[0079] Among them, R rx,m (τ) is the cross-correlation function of the m-th array element, R rx (τ) is a matrix consisting of the cross-correlation functions of all array elements; x * (-t) is the conjugate of x(-t).

[0080] Step 3: Based on the cross-correlation function, an orthogonal matching pursuit algorithm combining constant false alarm rate detection dynamic threshold is used to estimate the sparse channel parameters of each array element, including path amplitude and time delay.

[0081] Step three includes the following sub-steps:

[0082] The sparse channel parameters of each array element are estimated using an orthogonal matching pursuit algorithm that combines constant false alarm rate (CFAR) detection with a dynamic threshold. Specifically, this includes:

[0083] The input to the orthogonal matching pursuit algorithm with constant false alarm rate (CFAR) detection and dynamic threshold is the number of array elements M, the linear frequency modulated signal x(t), and the matrix R composed of the cross-correlation function. rx (τ), Maximum number of iterations I max False alarm rate α, noise variance The output is a sparse channel parameter set Ω. A,τ ={A p,m ,τ p,m | m = 1, …, M ; p = 1, …, P}.

[0084] like Figure 2 As shown, the algorithm iteration process is as follows:

[0085] S3.1: Initialize the sparse channel parameter set Initialize the array element number m and the iteration number i to 1;

[0086] S3.2: Select a segment of the received signal that has no signal or only noise as a reference unit, and calculate the noise variance of that segment. :

[0087]

[0088] Where, r k For the sample values ​​in the reference cell, is the sample mean, and N is the reference cell length.

[0089] S3.3: Based on noise variance Given the false alarm rate α, the constant false alarm detection dynamic threshold is calculated according to the following formula. :

[0090]

[0091] S3.4: Determine if m satisfies m≤M. If it does, proceed to S3.5; otherwise, output the sparse parameter set Ω. A,τ ;

[0092] S3.5: The matrix R composed of cross-correlation functions rx The m-th row of (τ) is assigned to the current residual R. rx,m (i) (τ);

[0093] S3.6: Determine if the iteration number i satisfies i≤I max If satisfied, then search for R. rx,m (i) The time delay τ corresponding to the maximum value in (τ) i,m Calculate the corresponding path amplitude If not, continue executing S3.7; otherwise, set i=1, m=m+1, and return to S3.4.

[0094] S3.7: Expanding the sparse channel parameter set Expand the dictionary ;

[0095] S3.8: Recalculate the residual for the next iteration ;in, It is the generalized inverse of dictionary D;

[0096] S3.9: If If the condition is met, then set i=1, m=m+1, and return to S3.4; otherwise, set i=i+1 and return to S3.6.

[0097] To further reduce false alarms, the sparse channel parameter set Ω obtained in step three... A,τ The validity of each path element is verified by using the maximum time delay constraint between array elements.

[0098] For any delay Calculate τ p,m The time delay of any array element m1 other than m in the array element set time delay difference ,Right now , If it exists If the delay is valid, it will be retained; otherwise, it will be considered a valid delay. If the delay is invalid, it will be discarded.

[0099] Step 4: Utilize the sparse channel parameters Ω of the multi-element array A,τ Construct the array response vector a p Through spatial spectrum Peak search estimates the angle of arrival (Angle) of multipath signals, including the direction angle. and pitch angle .

[0100] Step four includes the following sub-steps:

[0101] S4.1: For the sparse parameter set Ω estimated in step three A,τ For each path p in the equation, calculate the relative delay. ;

[0102] S4.2: Based on the sparse parameter set Ω A,τ Path amplitude A p,m and relative delay Constructing the array response vector:

[0103]

[0104] S4.3: Based on array response vector a p Construct the covariance matrix R of the path xx :

[0105]

[0106] S4.4: Calculating the spatial spectrum based on arrayed manifold vectors and covariance matrices for:

[0107]

[0108]

[0109] in, Let λ be the array manifold vector, and λ be the wavelength. This indicates the position of each element in the array.

[0110] S4.5: By searching the spatial spectrum Peak value, yielding the azimuth angle of each path p. and pitch angle The estimated value:

[0111]

[0112] Step 5: Based on the arrival angle, amplitude, and time delay information of the direct path and the reflection path, the underwater environment structure is inverted using ray acoustic theory, including the three-dimensional distance of the reflection point and the three-dimensional normal vector of the reflection surface, to achieve three-dimensional perception of the underwater reflector.

[0113] Step five includes:

[0114] S5.1: For the p=1th path (direct path), the distance of the direct path is calculated by calculating the two-way propagation time difference. The specific process includes:

[0115] S5.1.1: The array sends a linear frequency modulated signal at time t0 and records the corresponding timestamp t0.

[0116] S5.1.2: The beacon receives a linear frequency modulated signal at time t1, and after hardware processing for time t... proc Then a linear frequency modulated signal is transmitted back.

[0117] S5.1.3: The array receives a linear frequency modulation signal at time t2 and records the corresponding timestamp t2.

[0118] S5.1.4: Total time difference for array-side computation for

[0119]

[0120] Among them, the hardware processing time t proc From the laboratory to zero distance Calibration under specified conditions.

[0121] S5.1.5: Calculation of distance at the array end for

[0122] Where c is the speed of sound.

[0123] S5.2: For the p>1th path (reflection path), use the angle of arrival of the reflection path. Pitch angle and the time delay difference relative to the direct route The coordinates of the three-dimensional reflection point P of the inverted reflecting surface and the three-dimensional normal vector of the reflecting surface are obtained. The specific process includes:

[0124] S5.2.1: Calculate the time delay difference between each path p and the first path. ,in This represents the time delay parameter for all array elements along the p-th path. The operation of taking the average.

[0125] S5.2.2: Calculate the coordinates of the reflection point To the array center distance :

[0126]

[0127] in, and The estimated heading and pitch angles for step S4.4; total reflection path length. d1 is the distance of the first path (direct route) obtained in step S5.1. .

[0128] S5.2.3: Calculate the unit incident vector and unit reflection vector The coordinates of the sound source , and The heading and pitch angles of the first path (direct route) estimated in step S4.4.

[0129] S5.2.4: Calculate the three-dimensional normal vector of the reflecting surface and normalize it to .

[0130] Another embodiment of the present invention provides an underwater acoustic environment sensing system based on multi-element sparse channel estimation, such as... Figure 3 As shown, it includes:

[0131] The underwater acoustic transmitter is used to transmit linear frequency modulated signals.

[0132] The underwater acoustic receiver includes a multi-element array, a signal processing unit, and a display unit. The multi-element array is used to receive signals; the signal processing unit is used to perform signal preprocessing, sparse channel parameter estimation, angle of arrival estimation, and environment inversion; and the display unit is used to display the three-dimensional sensing results.

[0133] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of the present invention according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0134] This invention also provides a computer-readable storage medium storing a program thereon, which, when executed by a processor, implements the underwater acoustic environment perception method based on multi-element sparse channel estimation described in the above embodiments.

[0135] The computer-readable storage medium can be an internal storage unit of any data processing device described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., equipped on the device. Furthermore, the computer-readable storage medium can include both internal storage units of any data processing device and external storage devices. The computer-readable storage medium is used to store the computer program and other programs and data required by the data processing device, and can also be used to temporarily store data that has been output or will be output.

[0136] The effectiveness of the method and system of the present invention will be demonstrated below through a specific application example.

[0137] The parameters for this embodiment are: number of array elements M=6; linear frequency modulation signal T=0.2s, f0=13kHz, K=15kHz / s; false alarm rate α=10 -4 Maximum number of iterations I max =15; Array radius r=0.046m; Sound speed c=1461m / s.

[0138] In this embodiment, the multi-element array receives the signal s. rx,m (t), m=1,…,M Figure 4 As shown, it contains six real signals received by a six-element hydrophone array.

[0139] In this embodiment, Figure 5The results of environmental inversion and reconstruction obtained from steps one to five of a single experiment include the reconstruction results of the water surface reflection path due to the lack of a muffler, as well as the reconstruction results of the underwater target's reflective surface, demonstrating the invention's ability to perceive the three-dimensional position and reflective surface structure of underwater reflectors.

[0140] Figure 6 The results of environmental inversion and reconstruction (in a relative coordinate system) obtained from steps one to five of multiple experiments summarize the perception results of water surface reflection and underwater target reflection in multiple experiments, verifying the consistency and feasibility of the embodiments of the present invention in repeated experiments.

[0141] It will be understood by those skilled in the art that the above descriptions are merely preferred examples of the invention and are not intended to limit the invention. Although the invention has been described in detail with reference to the foregoing examples, those skilled in the art can still modify the technical solutions described in the foregoing examples or make equivalent substitutions for some of the technical features. All modifications and equivalent substitutions made within the spirit and principles of the invention should be included within the scope of protection of the invention.

Claims

1. A method for underwater acoustic environment perception based on multi-element sparse channel estimation, characterized in that, Includes the following steps: S1: The underwater acoustic receiver receives signals propagating through underwater acoustic multipath channels via a multi-element array, thus obtaining multi-channel received signals; S2: Perform Hilbert transform on the received signal of each array element to obtain the analytic signal, and calculate the cross-correlation function between the analytic signal and the transmitted signal; S3: Based on the cross-correlation function, an orthogonal matching pursuit algorithm combining constant false alarm rate detection dynamic threshold is used to estimate the sparse channel parameters of each array element, including path amplitude and time delay; S4: Utilize the sparse channel parameters of the multi-element array to construct the array response vector, and estimate the angle of arrival of the multipath signal, including the direction angle and elevation angle, through spatial spectrum peak search; S5: Based on the arrival angle, amplitude and time delay information of the direct path and the reflection path, the underwater environment structure is inverted through ray acoustic theory, including the three-dimensional distance of the reflection point and the three-dimensional normal vector of the reflection surface, to realize the three-dimensional perception of the underwater reflector. S3 includes the following sub-steps: S3.1: Initialize the sparse channel parameter set to an empty set, and initialize the array element number m and the iteration number i to 1; S3.2: Select a segment of the received signal that has no signal or only noise as a reference unit, and calculate the noise variance of that segment of the signal; S3.3: Calculate the constant false alarm detection dynamic threshold based on noise variance and false alarm rate; S3.4: Determine if m is not greater than the number of array elements. If it is, execute S3.5; otherwise, output the sparse parameter set. S3.5: The first matrix formed by the cross-correlation functions m The row is assigned to the current residual; S3.6: Determine if the iteration number i is not greater than the maximum iteration number. If it is, find the time delay corresponding to the maximum value in the current residual, calculate the corresponding path amplitude, and execute S3.7; otherwise, let i=1, m=m+1, and return to S3.

4. S3.7: Expand the sparse channel parameter set and dictionary; S3.8: Recalculate the residual for the next iteration; S3.9: Determine whether the maximum value in the residual of the next iteration is not greater than the constant false alarm detection dynamic threshold. If yes, let i=1, m=m+1, and return to S3.4; otherwise, let i=i+1 and return to S3.

6.

2. The underwater acoustic environment perception method based on multi-element sparse channel estimation according to claim 1, characterized in that, For the sparse parameter set obtained by S3, the validity of each path element is verified by the maximum time delay constraint between array elements.

3. The underwater acoustic environment perception method based on multi-element sparse channel estimation according to claim 2, characterized in that, S4 includes the following sub-steps: S4.1: Calculate the relative time delay for each path in the sparse parameter set; S4.2: Construct an array response vector based on the path magnitude in the sparse parameter set and the relative time delay; S4.3: Constructing the covariance matrix of the path based on the array response vector: S4.4: Calculate the spatial spectrum based on the array manifold vectors and the covariance matrix; S4.5: By searching for peak values ​​in the spatial spectrum, the estimated values ​​of the azimuth and elevation angles for each path are obtained.

4. The underwater acoustic environment perception method based on multi-element sparse channel estimation according to claim 1, characterized in that, S5 includes: S5.1: For a direct path, the distance of the direct path is calculated by calculating the two-way propagation time difference; S5.2: For the reflection path, the three-dimensional coordinates of the reflection point and the three-dimensional normal vector of the reflection surface are inverted using the arrival angle, pitch angle and time delay difference relative to the direct path of the reflection path.

5. The underwater acoustic environment perception method based on multi-element sparse channel estimation according to claim 4, characterized in that, For the direct route, the distance is calculated by determining the two-way propagation time difference, specifically including: S5.1.1: The array sends a linear frequency modulated signal at time t0 and records the corresponding timestamp t0; S5.1.2: The beacon end in time t 1. Upon receiving the linear frequency modulated signal, it undergoes hardware processing time. t proc The linear frequency modulation signal is then transmitted back. S5.1.3: Array end in time t 2. Upon receiving the linear frequency modulated signal, record the corresponding timestamp. t 2; S5.1.4: Total time difference for array-side calculation; S5.1.5: Calculate the distance at the array end.

6. The underwater acoustic environment perception method based on multi-element sparse channel estimation according to claim 4, characterized in that, For the reflection path, using the angle of arrival, elevation angle, and time delay difference relative to the direct path, the three-dimensional coordinates of the reflection point on the reflecting surface and the three-dimensional normal vector of the reflecting surface are inverted. Specifically, this includes: S5.2.1: Calculate each path p The time delay difference compared to the direct route; S5.2.2: Calculate the distance from the reflection point to the center of the array; S5.2.3: Calculate the unit incident vector and the unit reflected vector; S5.2.4: Calculate the three-dimensional normal vector of the reflecting surface and normalize it.

7. An underwater acoustic environment sensing system based on multi-element sparse channel estimation, characterized in that, This system is used to implement the underwater acoustic environment perception method based on multi-element sparse channel estimation as described in any one of claims 1 to 6; the system includes: The underwater acoustic transmitter is used to transmit linear frequency modulated signals. The underwater acoustic receiver includes a multi-element array, a signal processing unit, and a display unit; wherein the multi-element array is used to receive signals; the signal processing unit is used to perform signal preprocessing, sparse channel parameter estimation, angle of arrival estimation, and environment inversion; and the display unit is used to display the three-dimensional perception results.

8. An electronic device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by the electronic device, cause the electronic device to implement the underwater acoustic environment perception method based on multi-element sparse channel estimation as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, It stores a program that, when executed by a processor, implements the underwater acoustic environment perception method based on multi-element sparse channel estimation as described in any one of claims 1 to 6.

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