CN-121656961-B - Underwater sound environment sensing method based on multi-array element sparse channel estimation

CN121656961BCN 121656961 BCN121656961 BCN 121656961BCN-121656961-B

Abstract

The invention discloses an underwater sound environment sensing method based on multi-array element sparse channel estimation, which comprises the steps of receiving signals transmitted through an underwater sound multipath channel through a multi-array element array by an underwater sound receiving end, carrying out Hilbert transformation on the received signals of each array element to obtain analytic signals, calculating a cross-correlation function of the analytic signals and transmitting signals, estimating sparse channel parameters of each array element based on the cross-correlation function by adopting an orthogonal matching pursuit algorithm combined with a constant false alarm detection dynamic threshold value, wherein the sparse channel parameters comprise path amplitude and time delay, constructing an array response vector by utilizing the sparse channel parameters of the multi-array element array, estimating the arrival angle of the multipath signals comprising direction angle and pitch angle through space spectrum peak value search, and inverting an underwater environment structure comprising reflection point distance and reflection surface normal vector through ray acoustic theory to realize three-dimensional sensing of an underwater reflector. The invention can improve the multipath resolution and effectively reduce false alarm and missed detection.

Inventors

WEI YAN
WEI YIRAN
LI ZHIPENG
Qiao Yueyi
BAI HUAJUN
TU XINGBIN
ZHU JIANG
QU FENGZHONG

Assignees

浙江大学

Dates

Publication Date: 20260512
Application Date: 20260209

Claims (9)

1. The underwater sound environment sensing method based on multi-array element sparse channel estimation is characterized by comprising the following steps of: s1, a water sound receiving end receives signals transmitted by a water sound multipath channel through a multi-array element array to obtain multichannel receiving signals; s2, performing Hilbert transform on the received signals of each array element to obtain analysis signals, and calculating a cross-correlation function of the analysis signals and the transmitted signals; s3, based on a cross-correlation function, estimating sparse channel parameters of each array element, including path amplitude and time delay, by adopting an orthogonal matching pursuit algorithm combined with a constant false alarm detection dynamic threshold; S4, constructing an array response vector by using sparse channel parameters of the multi-array element array, and estimating an arrival angle of the multipath signal through space spectrum peak value search, wherein the arrival angle comprises a direction angle and a pitch angle; S5, inverting the underwater environment structure, including the three-dimensional distance of the reflecting point and the three-dimensional normal vector of the reflecting surface, by using ray acoustic theory based on the arrival angle, amplitude and time delay information of the direct path and the reflecting path to realize the three-dimensional perception of the underwater reflector; the step S3 comprises the following substeps: S3.1, initializing a sparse channel parameter set to be an empty set, and initializing an array element sequence number m and iteration times i to be 1; s3.2, selecting a section without signal or with noise only from the received signal as a reference unit, and calculating the noise variance of the section of signal; s3.3, calculating a constant false alarm detection dynamic threshold according to the noise variance and the false alarm rate; S3.4, judging whether m is not more than the number of array elements, if so, executing S3.5, otherwise, outputting a sparse parameter set; s3.5, assigning the m-th row of a matrix formed by the cross correlation functions to the current residual error; S3.6, judging whether the iteration number i is not more than the maximum iteration number, if so, searching the time delay corresponding to the maximum value in the current residual error, calculating the corresponding path amplitude, and executing S3.7, otherwise, making i=1, m=m+1, and returning to S3.4; s3.7, expanding a sparse channel parameter set and a dictionary; S3.8, recalculating the residual error of the next iteration; and S3.9, judging whether the maximum value in the residual error of the next iteration is not greater than the dynamic threshold value of the constant false alarm detection, if so, making i=1 and m=m+1, returning to S3.4, and otherwise, making i=i+1, and returning to S3.6.
2. The underwater sound environment sensing method based on multi-array element sparse channel estimation according to claim 1, wherein for the sparse parameter set obtained in S3, validity of each path element is verified by using maximum delay constraint among array elements.
3. The method for underwater sound environment sensing based on multi-array element sparse channel estimation according to claim 2, wherein said S4 comprises the sub-steps of: S4.1, calculating the relative time delay of each path in the sparse parameter set; S4.2, constructing an array response vector based on the path amplitude in the sparse parameter set and the relative time delay; S4.3, constructing a covariance matrix of the path based on the array response vector: s4.4, calculating a spatial spectrum based on the array manifold vector and the covariance matrix; and S4.5, obtaining estimated values of azimuth angle and pitch angle of each path by searching the space spectrum peak value.
4. The method for underwater sound environment sensing based on multi-array element sparse channel estimation according to claim 1, wherein said S5 comprises: S5.1, for the direct path, calculating the direct path distance by calculating the two-way propagation time difference; s5.2, for the reflection path, inverting the three-dimensional normal vector of the three-dimensional reflection point coordinates of the reflection surface and the reflection surface by utilizing the arrival angle, the pitch angle and the time delay difference relative to the direct path of the reflection path.
5. The method for underwater sound environment sensing based on multi-array element sparse channel estimation according to claim 4, wherein for the direct path, calculating the direct path distance by calculating the two-way propagation time difference comprises: S5.1.1, the matrix end sends a linear frequency modulation signal at time t 0 , and a corresponding time stamp t 0 is recorded; S5.1.2, the beacon end receives the linear frequency modulation signal at time t 1 , and returns the linear frequency modulation signal after hardware processing time t proc ; S5.1.3, the matrix end receives the linear frequency modulation signal at time t 2 and records a corresponding time stamp t 2 ; s5.1.4, calculating the total time difference by the matrix end; S5.1.5 calculating the distance from the matrix end.
6. The method for sensing the underwater sound environment based on multi-array element sparse channel estimation according to claim 4, wherein for the reflection path, using the arrival angle, pitch angle and time delay difference relative to the direct path, inverting the three-dimensional reflection point coordinates of the reflection surface and the three-dimensional normal vector of the reflection surface, comprises: S5.2.1, calculating the time delay difference between each path p and the direct path; S5.2.2 calculating the distance from the reflecting point to the center of the array; S5.2.3 calculating a unit incident vector and a unit reflection vector; s5.2.4 calculating the three-dimensional normal vector of the reflecting surface, and normalizing the three-dimensional normal vector.
7. The underwater sound environment sensing system based on multi-array element sparse channel estimation is characterized by being used for realizing the underwater sound environment sensing method based on multi-array element sparse channel estimation as set forth in any one of claims 1-6, and comprises the following steps: the underwater sound transmitting end is used for transmitting the linear frequency modulation signal; The underwater sound receiving end comprises a multi-array element array, a signal processing unit and a display unit, wherein the multi-array element array is used for receiving signals, the signal processing unit is used for carrying out signal preprocessing, sparse channel parameter estimation, arrival angle estimation and environment inversion, and the display unit is used for displaying three-dimensional perception results.
8. An electronic device, comprising: one or more processors; And the storage device is used for storing one or more programs, and when the one or more programs are executed by the electronic equipment, the electronic equipment realizes the underwater sound environment sensing method based on the multi-array element sparse channel estimation according to any one of claims 1-6.
9. A computer-readable storage medium, having stored thereon a program which, when executed by a processor, implements the method for underwater sound environment perception based on multi-array element sparse channel estimation of any one of claims 1 to 6.

Description

Underwater sound environment sensing method based on multi-array element sparse channel estimation Technical Field The invention relates to the field of underwater acoustic signal processing, in particular to an underwater acoustic environment sensing method based on multi-array element sparse channel estimation. Background Underwater acoustic environment sensing is a key technology for realizing functions of underwater communication, detection, navigation and the like. Shallow sea underwater acoustic channels have obvious multipath effects, and the traditional methods such as matched filtering and least square estimation have the problems of low resolution and poor adaptability, so that the path parameters are difficult to accurately extract in a high-noise multipath dense environment. Although a new approach is provided by a sparse recovery theory such as compressed sensing, the iteration stopping criterion lacks self-adaptability, false alarm or omission is easily caused, and the precision and reliability of environment inversion are limited. In addition, most channel estimation methods fail to form an efficient and robust processing link with the downstream environment geometric inversion task, and it is difficult to reliably convert multipath parameters into quantitative perceptions of physical characteristics such as reflector positions, postures and the like. Disclosure of Invention Aiming at the defects of the prior art, the invention provides the underwater sound environment sensing method based on multi-array element sparse channel estimation, which can dynamically adjust the threshold according to different underwater sound noise environments and improve the reliability of multipath parameter estimation and the accuracy of environment sensing. The technical scheme adopted by the invention is as follows: a method for sensing underwater sound environment based on multi-array element sparse channel estimation comprises the following steps: s1, a water sound receiving end receives signals transmitted by a water sound multipath channel through a multi-array element array to obtain multichannel receiving signals; s2, performing Hilbert transform on the received signals of each array element to obtain analysis signals, and calculating a cross-correlation function of the analysis signals and the transmitted signals; s3, based on a cross-correlation function, estimating sparse channel parameters of each array element, including path amplitude and time delay, by adopting an orthogonal matching pursuit algorithm combined with a constant false alarm detection dynamic threshold; S4, constructing an array response vector by using sparse channel parameters of the multi-array element array, and estimating an arrival angle of the multipath signal through space spectrum peak value search, wherein the arrival angle comprises a direction angle and a pitch angle; s5, inverting the underwater environment structure including the three-dimensional distance of the reflecting point and the three-dimensional normal vector of the reflecting surface by using ray acoustic theory based on the arrival angle, amplitude and time delay information of the direct path and the reflecting path, so as to realize the three-dimensional perception of the underwater reflector. Further, the step S3 includes the following sub-steps: S3.1, initializing a sparse channel parameter set to be an empty set, and initializing an array element sequence number m and iteration times i to be 1; s3.2, selecting a section without signal or with noise only from the received signal as a reference unit, and calculating the noise variance of the section of signal; s3.3, calculating a constant false alarm detection dynamic threshold according to the noise variance and the false alarm rate; S3.4, judging whether m is not more than the number of array elements, if so, executing S3.5, otherwise, outputting a sparse parameter set; s3.5, assigning the m-th row of a matrix formed by the cross correlation functions to the current residual error; S3.6, judging whether the iteration number i is not more than the maximum iteration number, if so, searching the time delay corresponding to the maximum value in the current residual error, calculating the corresponding path amplitude, and executing S3.7, otherwise, making i=1, m=m+1, and returning to S3.4; s3.7, expanding a sparse channel parameter set and a dictionary; S3.8, recalculating the residual error of the next iteration; and S3.9, judging whether the maximum value in the residual error of the next iteration is not greater than the dynamic threshold value of the constant false alarm detection, if so, making i=1 and m=m+1, returning to S3.4, and otherwise, making i=i+1, and returning to S3.6. Further, for the sparse parameter set obtained in the step S3, the validity of each path element is verified by using the maximum time delay constraint among the array elements. Further, the step S4 includes the following sub-st