A Single-Vector Hydrophone Orientation Estimation Method Based on Multiple Joint Processing Deconvolution Projection CBF Spatial Spectrum

Abstract

This invention discloses a single-vector hydrophone orientation estimation method based on a multi-joint processing deconvolution projected CBF spatial spectrum, belonging to the field of underwater target detection technology. This invention addresses the problem that existing methods do not consider the acoustic energy information of the autocorrelation components of each component in the sound field, resulting in limited performance improvement for weak target detection. First, this invention performs multi-joint processing on the received signals from the sound pressure and vibration velocity channels of the single-vector hydrophone to obtain the maximum acoustic energy of the combined sound pressure and vibration velocity, the acoustic energy of the received signals from the sound pressure channel and the vibration velocity x-channel, the acoustic energy of the received signals from the sound pressure channel and the vibration velocity y-channel, the acoustic energy of the vibration velocity x-channel and the vibration velocity y-channel, and the difference between the acoustic energy of the vibration velocity x-channel and the sound pressure channel. Then, based on the calculated acoustic energy and the difference in acoustic energy, the projected CBF spatial spectrum is calculated, and the projected CBF spatial spectrum is deconvolved based on the point spread function to obtain the underwater target orientation estimation result. This invention can be applied to underwater target orientation estimation.

Description

Technical Field

[0001] This invention belongs to the field of underwater target detection technology, specifically relating to a single-vector hydrophone orientation estimation method based on the CBF spatial spectrum of multiple joint processing deconvolution projection. Background Technology

[0002] Underwater target location estimation is a crucial research area in sonar technology. It typically involves using hydrophones to sense acoustic signals, followed by signal processing to extract useful information such as target location. Vector hydrophones, composed of a sound pressure hydrophone and a vibration velocity hydrophone, can simultaneously and point-to-point sense the sound pressure and particle velocity information at a specific point in the sound field. They possess figure-eight directional reception, more comprehensive sound field information, and stronger noise suppression capabilities. Compared to sound pressure hydrophones, a single vector hydrophone can perform underwater target location estimation, providing a new direction for underwater target location estimation.

[0003] The ability to estimate the location of underwater targets using a single vector hydrophone allows it to be applied to provide target location information in both non-cooperative and cooperative systems. The research results in Reference 1 (Hui Junying, Liu Hong, Yu Huabing, et al. Preliminary study on the joint processing of sound pressure and vibration velocity information and its physical basis [J]. Acta Acustica, 2000, 254, 303-307) and Reference 2 (Hawkes M, Nehorai A. Wideband source localization using a distributed acoustic vector-sensor array [J]. IEEE Trans. Signal Process., 2003, 51(6): 1479-1491.) show that for the far-field sound field radiated by a finite-scale sound source, sound pressure and vibration velocity are completely correlated; while for isotropic noise fields, sound pressure and vibration velocity at the same point are uncorrelated, theoretically revealing the noise reduction principle of the joint processing method of sound pressure and vibration velocity. Reference 3 (Sun Guiqing, Yang Desen, Zhang Lanyue, et al. Maximum likelihood ratio detection and maximum likelihood azimuth estimation based on vector hydrophone [J]. Acta Acustica, 2003, 1.66-72) obtained a spatial processing gain of 10-20 dB using sound intensity method based on lake test data, further confirming that the joint processing method of sound pressure and vibration velocity has strong noise suppression capability. Reference 4 (Bai Xingyu, Jiang Yu, Zhao Chunhui. Source number detection and azimuth estimation of acoustic vector array based on joint processing of sound pressure and vibration velocity [J]. Acta Acustica, 2008, 331.56-61) and Reference 5 (Shi S, Li Y, Yang D, et al., Sparse representation based direction-of-arrival estimation using circular acoustic vector sensor arrays [J]. Digit. Signal Prog., 2020, 99.) extended the sound intensity processing idea to different vector arrays, improving the detection performance of weak targets. However, the acoustic energy information of the autocorrelation component of each component in the sound field is still not considered. Therefore, the performance improvement for weak target detection is limited, and further improvements are needed. Summary of the Invention

[0004] The purpose of this invention is to address the problem that existing methods do not consider the acoustic energy information of the autocorrelation components of each component in the sound field, resulting in limited performance improvement for weak target detection. Therefore, this invention proposes a single-vector hydrophone orientation estimation method based on the deconvolution projection CBF spatial spectrum of multiple joint processing to improve the performance of weak target detection.

[0005] The technical solution adopted by this invention to solve the above-mentioned technical problems is: a single-vector hydrophone orientation estimation method based on the spatial spectrum of CBF (convolutional blotting) after multiple joint processing deconvolution projection, the method specifically including the following steps:

[0006] Step 1: Denote the signals received by the single-vector hydrophone at time t as p(t) and v(t), respectively. x (t) and v y (t), where p(t) represents the received signal from the sound pressure channel, v x (t) represents the received signal of the vibration velocity x channel, v y (t) represents the received signal of the vibration velocity y channel;

[0007] Step 2: Perform multiple joint processing on the received signals from the single-vector hydrophone's sound pressure channel and vibration velocity channel to obtain the maximum acoustic energy I0 of the combined sound pressure and vibration velocity, and the acoustic energy I of the received signals from the sound pressure channel and vibration velocity channel. px The acoustic energy I of the sound pressure channel received signal and the vibration velocity y channel received signal py Acoustic energy I of vibration velocity x channel and vibration velocity y channel xy The difference between the acoustic energy of the vibration velocity x-channel and the acoustic energy of the sound pressure channel I xx ;

[0008] According to I0, I px I py I xy and I xx Construct a high-order covariance matrix for a single-vector hydrophone, calculate the projected CBF weighted vector, and then calculate the projected CBF spatial spectrum based on the high-order covariance matrix and the projected CBF weighted vector.

[0009] Step 3: Construct a point spread function based on the projection beam function, and deconvolve the projected CBF spatial spectrum based on the point spread function. Use the position of the spectral peak in the azimuth spectrum obtained by deconvolution as the azimuth of the underwater target.

[0010] Furthermore, the received signals of the sound pressure channel, the vibration velocity x channel, and the vibration velocity y channel are respectively:

[0011] p(t)=s(t)+n p (t)

[0012] v x (t)=s(t)cosθ+n x (t) (1)

[0013] v y (t)=s(t)sinθ+n y (t)

[0014] Where s(t) is the target signal, θ is the incident direction of the target signal, t is time, and n is the distance from the target signal to the time interval. p (t), n x (t) and n y (t) represents the sound pressure and vibration velocity received by the x and y channels, respectively.

[0015] Furthermore, in step two, the received signals from the single-vector hydrophone's sound pressure channel and vibration velocity channel undergo multiple joint processing to obtain the maximum acoustic energy I0 of the combined sound pressure and vibration velocity, and the acoustic energy I0 of the received signals from the sound pressure channel and the vibration velocity channel. px The acoustic energy I of the sound pressure channel received signal and the vibration velocity y channel received signal py Acoustic energy I of vibration velocity x channel and vibration velocity y channel xy The difference between the acoustic energy of the vibration velocity x-channel and the acoustic energy of the sound pressure channel I xx The specific process is as follows:

[0016] Step 2: Receive the vibration velocity x-channel signal v respectively. x (t) and the received signal v of the vibration velocity y channel y (t) Projected onto the φ direction, and then the combined vibration velocity signal v is obtained based on the projection result. c (t);

[0017] Then, based on the sound pressure channel received signal and the combined vibration velocity signal v c (t) Construct the maximum acoustic energy I0 of sound pressure and combined vibration velocity;

[0018] Step 22: The acoustic energy of the sound pressure channel received signal and the vibration velocity x-channel received signal, the acoustic energy of the sound pressure channel received signal and the vibration velocity y-channel received signal, the acoustic energy of the vibration velocity x-channel and the vibration velocity y-channel, and the difference between the acoustic energy of the vibration velocity x-channel and the acoustic energy of the sound pressure channel are respectively:

[0019]

[0020]

[0021]

[0022]

[0023] In the formula, I px I represents the acoustic energy of the signal received by the sound pressure channel and the signal received by the vibration velocity channel. py I represents the acoustic energy of the signal received by the sound pressure channel and the signal received by the vibration velocity channel. xy I represents the acoustic energy of the x-channel and y-channel of vibration. xx I represents the difference between the acoustic energy of the vibration velocity x-channel and the acoustic energy of the sound pressure channel. x I represents the acoustic energy of the x-channel, representing the vibration velocity.p This represents the acoustic energy of the sound pressure channel. This represents the noise power of the sound pressure channel. The noise power of the vibration velocity channel, and

[0024] Furthermore, the combined vibration velocity signal v obtained based on the projection result... c (t), specifically:

[0025]

[0026] In the formula, φ is the projection angle.

[0027] Furthermore, the signal received based on the sound pressure channel and the combined vibration velocity signal v c (t) Construct the maximum acoustic energy I0 of the combined sound pressure and vibration velocity, specifically:

[0028] Calculate the acoustic energy I of sound pressure and combined vibration velocity pvc :

[0029]

[0030] In the formula, (·) H This is the conjugate transpose operation; Indicates the power of the target signal;

[0031] And by iterating through the projection angle φ, the maximum acoustic energy I0 of the combined sound pressure and vibration velocity is obtained:

[0032]

[0033] Furthermore, the statement based on I0, I px I py I xy and I xx The specific process for constructing the high-order covariance matrix of a single-vector hydrophone is as follows:

[0034] The acoustic energy information I0, I from the single-vector hydrophone px I py I xy and I xx Form a vector z:

[0035]

[0036] In the formula, b(θ) is the steering vector matrix of the higher-order covariance matrix, b(θ) = [1, cosθ, sinθ, cos(2θ), sin(2θ)] T The superscript T represents the transpose of the matrix;

[0037] make Where u1 is the left singular vector of z, λ1 is the singular value, and β is the linear coefficient;

[0038] The higher-order covariance matrix R of the vector hydrophone is:

[0039]

[0040] Where E[·] represents expectation.

[0041] Further, the projected CBF weighted vector is calculated, and then the projected CBF spatial spectrum is calculated based on the higher-order covariance matrix and the projected CBF weighted vector; the specific process is as follows:

[0042] The steering vector matrix of the higher-order covariance matrix is ​​projected onto the signal subspace to obtain the projected CBF weighted vector.

[0043]

[0044] In the formula, For the scanning angle, U s Represents the signal subspace;

[0045] Then projected CBF spatial spectrum for:

[0046]

[0047] Where |·| represents the calculation of the determinant.

[0048] Furthermore, the specific process of step three is as follows:

[0049] Step 31: Rewrite formula (12) as follows:

[0050]

[0051] in, It is the distribution function of the target signal power in the azimuth plane, and * denotes the convolution calculation symbol. For the projection beam function, Let be the point spread function of the projected CBF;

[0052] Step 3.2: Based on the point spread function Deconvolve the projected CBF spatial spectrum to obtain a new azimuth spectrum.

[0053] Furthermore, the distribution function of the target signal power in the angular plane is:

[0054]

[0055] Where δ(·) represents the unit impulse function.

[0056] Furthermore, the specific process of step three two is as follows:

[0057] Step 321: Initialize the iteration count n=1, and initialize the initial value of the target signal power distribution in the azimuth plane. Set the total number of iterations to M;

[0058] Step 3.2.2 Calculation

[0059]

[0060] In the formula, This represents the target power distribution result obtained after the (n+1)th iteration. This represents the target power distribution result obtained after the nth iteration;

[0061] Step 3: Determine if n = M is satisfied;

[0062] If the condition is not met, then let n = n + 1 and return to step 3.2.2.

[0063] If satisfied, the target power distribution result obtained in the last iteration is used as the azimuth spectrum obtained by deconvolution.

[0064]

[0065] The beneficial effects of this invention are:

[0066] This invention proposes a single-vector hydrophone orientation estimation method based on a multi-joint processing deconvolution projection CBF spatial spectrum. The method performs multi-joint processing on the received signals from the sound pressure and vibration velocity channels of the single-vector hydrophone to obtain the maximum acoustic energy of the combined sound pressure and vibration velocity, the acoustic energy of the received signals from the sound pressure channel and the vibration velocity x-channel, the acoustic energy of the received signals from the sound pressure channel and the vibration velocity y-channel, the acoustic energy of the vibration velocity x-channel and the vibration velocity y-channel, and the difference between the acoustic energy of the vibration velocity x-channel and the sound pressure channel. The projected CBF spatial spectrum is then calculated based on the calculated acoustic energy and the difference in acoustic energy. Finally, the projected CBF spatial spectrum is deconvolved based on the point spread function to obtain the underwater target orientation estimation result. Compared with traditional CBF and MVDR methods, this invention not only has stronger robustness but also features a narrow beamwidth and low sidelobes, further improving the target resolution and orientation estimation accuracy of the single-vector hydrophone, enhancing its performance in detecting weak targets, and making it more suitable for use in complex underwater acoustic environments. Attached Figure Description

[0067] Figure 1 This is a flowchart of the method of the present invention;

[0068] Figure 2(a) is a comparison of the spatial spectra of CBF, MVDR, HCBF and DU-HCBF methods when the signal-to-noise ratio (SNR) is -5dB.

[0069] Figure 2(b) is a comparison of the spatial spectra of CBF, MVDR, HCBF and DU-HCBF methods when the signal-to-noise ratio (SNR) is -10dB.

[0070] Figure 2(c) is a comparison of the spatial spectra of CBF, MVDR, HCBF and DU-HCBF methods when the signal-to-noise ratio (SNR) is -15dB.

[0071] Figure 3 This is a comparison of the azimuth estimation accuracy of the CBF, MVDR, HCBF, and DU-HCBF methods.

[0072] Figure 4(a) shows the data processing results of the anechoic tank using the CBF method;

[0073] Figure 4(b) shows the data processing results of the anechoic tank using the HCBF method;

[0074] Figure 4(c) shows the data processing results of the anechoic tank using the MVDR method;

[0075] Figure 4(d) shows the data processing results of the anechoic water tank using the DU-HCBF method. Detailed Implementation

[0076] Specific implementation method one: Combining Figure 1 This embodiment describes a single-vector hydrophone orientation estimation method based on the spatial spectrum of the deconvolution projection CBF (multiple joint processing) of hydrophones. The method specifically includes the following steps:

[0077] Step 1: Using the center of the vector hydrophone as the reference point, denot the known signals received by the single vector hydrophone at time t as p(t) and v(t), respectively. x (t) and v y (t), where p(t) represents the received signal from the sound pressure channel, v x (t) represents the received signal of the vibration velocity x channel, v y (t) represents the received signal of the vibration velocity y channel;

[0078] Step 2: Perform multiple joint processing on the received signals from the single-vector hydrophone's sound pressure channel and vibration velocity channel to obtain the maximum acoustic energy I0 of the combined sound pressure and vibration velocity, and the acoustic energy I of the received signals from the sound pressure channel and vibration velocity channel. px The acoustic energy I of the sound pressure channel received signal and the vibration velocity y channel received signal py Acoustic energy I of vibration velocity x channel and vibration velocity y channel xy The difference between the acoustic energy of the vibration velocity x-channel and the acoustic energy of the sound pressure channel Ixx ;

[0079] According to I0, I px I py I xy and I xx Construct a high-order covariance matrix for a single-vector hydrophone, calculate the projected CBF weighted vector, and then calculate the projected CBF spatial spectrum based on the high-order covariance matrix and the projected CBF weighted vector.

[0080] Step 3: Construct a point spread function based on the projection beam function, and deconvolve the projected CBF spatial spectrum based on the point spread function. Use the position of the spectral peak in the azimuth spectrum obtained by deconvolution as the azimuth of the underwater target.

[0081] This invention fully exploits the acoustic information and inherent relationships carried by the cross-correlation and autocorrelation of each channel of a vector hydrophone. Compared with conventional beamforming (CBF) and minimum variance distortionless response (MVDR) methods, the method of this invention not only has strong robustness, but also has a narrow beamwidth and low sidelobes, which can further improve the target resolution and azimuth estimation accuracy of a single vector hydrophone, making it more suitable for use in real complex underwater acoustic environments.

[0082] This invention provides a detailed explanation of the method for a single target. However, it is also applicable to estimating the location of multiple targets. When there are multiple targets, since the radiated noise spectral components of different targets are different, this characteristic can be utilized to perform a Fourier transform on the signal received by the vector hydrophone, converting the received signal to the frequency domain. The frequency corresponding to each target signal can be obtained from the Fourier transform result. Then, by processing the signals at the corresponding frequencies using the method of this invention, the location of each target can be accurately estimated.

[0083] Specific Implementation Method Two: This implementation method differs from Specific Implementation Method One in that the received signals of the sound pressure channel, the vibration velocity x channel, and the vibration velocity y channel are respectively:

[0084]

[0085] Where s(t) is the target signal, θ is the incident direction of the target signal, t is time, t = 1, 2, ..., L, L is the number of snapshots of data collected within the test time, and n p (t), n x (t) and n y (t) represents the sound pressure and vibration velocity received by the x and y channels, respectively.

[0086] The other steps and parameters are the same as in Specific Implementation Method 1.

[0087] Specific Implementation Method Three: This implementation method differs from Specific Implementation Method One or Two in that, in step two, the received signals from the single-vector hydrophone's sound pressure channel and vibration velocity channel undergo multiple joint processing to obtain the maximum acoustic energy I0 of the combined sound pressure and vibration velocity, and the acoustic energy I of the received signals from the sound pressure channel and vibration velocity channel. px The acoustic energy I of the sound pressure channel received signal and the vibration velocity y channel received signal py Acoustic energy I of vibration velocity x channel and vibration velocity y channel xy The difference between the acoustic energy of the vibration velocity x-channel and the acoustic energy of the sound pressure channel I xx The specific process is as follows:

[0088] Step 2: Receive the vibration velocity x-channel signal v respectively. x (t) and the received signal v of the vibration velocity y channel y (t) Projected onto the φ direction, and then the combined vibration velocity signal v is obtained based on the projection result. c (t);

[0089] Then, based on the sound pressure channel received signal and the combined vibration velocity signal v c (t) Construct the maximum acoustic energy I0 of sound pressure and combined vibration velocity;

[0090] Step 22: Utilizing the characteristic that the received signals of each channel of a single-vector hydrophone are completely correlated and the received noise components are uncorrelated, the sound intensity of the received signal of the sound pressure channel and the received signal of the vibration velocity x channel, the sound intensity of the received signal of the sound pressure channel and the received signal of the vibration velocity y channel, the sound energy of the vibration velocity x channel, and the sound energy of the vibration velocity y channel can be constructed, which can effectively suppress isotropic noise.

[0091] The acoustic energy of the sound pressure channel received signal and the vibration velocity x-channel received signal, the acoustic energy of the sound pressure channel received signal and the vibration velocity y-channel received signal, the acoustic energy of the vibration velocity x-channel and the vibration velocity y-channel, and the difference between the acoustic energy of the vibration velocity x-channel and the acoustic energy of the sound pressure channel are respectively:

[0092]

[0093]

[0094]

[0095]

[0096] In the formula, I px I represents the acoustic energy of the signal received by the sound pressure channel and the signal received by the vibration velocity channel. pyI represents the acoustic energy of the signal received by the sound pressure channel and the signal received by the vibration velocity channel. xy I represents the acoustic energy of the x-channel and y-channel of vibration. xx I represents the difference between the acoustic energy of the vibration velocity x-channel and the acoustic energy of the sound pressure channel. x I represents the acoustic energy of the x-channel, representing the vibration velocity. p This represents the acoustic energy of the sound pressure channel. This represents the noise power of the sound pressure channel. The noise power of the vibration velocity channel, and

[0097] Other steps and parameters are the same as in specific implementation method one or two.

[0098] To simplify the representation, the present invention omits the time parameter t in each acoustic energy calculation result and in the subsequent calculation process. The entire method of the present invention is executed once for each data sampling moment, so that the target orientation corresponding to each data sampling moment can be obtained respectively.

[0099] Specific Implementation Method Four: This implementation method differs from Specific Implementation Methods One to Three in that the combined vibration velocity signal v obtained based on the projection result is... c (t), specifically:

[0100]

[0101] In the formula, φ is the projection angle.

[0102] The other steps and parameters are the same as those in one of the specific implementation methods one to three.

[0103] Specific Implementation Method Five: This implementation method differs from Specific Implementation Methods One to Four in that the method based on the sound pressure channel receiving signal and the combined vibration velocity signal v... c (t) Construct the maximum acoustic energy I0 of the combined sound pressure and vibration velocity, specifically:

[0104] Calculate the acoustic energy I of sound pressure and combined vibration velocity pvc :

[0105]

[0106] In the formula, (·) H This is the conjugate transpose operation; Indicates the power of the target signal;

[0107] According to the above formula, when φ = θ or φ = θ + 180°, I pvc Maximum. And by iterating through the projection angle φ, the maximum acoustic energy I0 of the combined sound pressure and vibration velocity is obtained:

[0108]

[0109] The other steps and parameters are the same as those in one of the specific implementation methods one to four.

[0110] Specific Implementation Method Six: This implementation method differs from Specific Implementation Methods One to Five in that the step of using I0, I... px I py I xy and I xx The specific process for constructing the high-order covariance matrix of a single-vector hydrophone is as follows:

[0111] The acoustic energy information I0, I from the single-vector hydrophone px I py I xy and I xx Form a vector z:

[0112]

[0113] In the formula, b(θ) is the steering vector matrix of the higher-order covariance matrix, b(θ) = [1, cosθ, sinθ, cos(2θ), sin(2θ)] T The superscript T represents the transpose of the matrix;

[0114] make Where u1 is the left singular vector of z, λ1 is the singular value, and β is the linear coefficient;

[0115] The higher-order covariance matrix R of the vector hydrophone is:

[0116]

[0117] Where E[·] represents expectation.

[0118] The other steps and parameters are the same as those in one of the specific implementation methods one to five.

[0119] Specific Implementation Method Seven: This implementation method differs from Specific Implementation Methods One through Six in that the calculation of the projected CBF weighted vector, and then the calculation of the projected CBF spatial spectrum based on the higher-order covariance matrix and the projected CBF weighted vector; the specific process is as follows:

[0120] The steering vector matrix of the higher-order covariance matrix is ​​projected onto the signal subspace to obtain the projected CBF weighted vector.

[0121]

[0122] In the formula, For the scanning angle, U s Represents the signal subspace;

[0123] Then projected CBF spatial spectrum for:

[0124]

[0125] Where |·| represents the calculation of the determinant.

[0126] The other steps and parameters are the same as those in one of the specific implementation methods one to six.

[0127] Specific Implementation Method Eight: This implementation method differs from Specific Implementation Methods One to Seven in that the specific process of step three is as follows:

[0128] Step 31: Rewrite formula (12) as follows:

[0129]

[0130] in, It is the distribution function of the target signal power in the azimuth plane, and * denotes the convolution calculation symbol. For the projection beam function, The point spread function (PSF) is the projection of the CBF.

[0131] As can be seen from equation (13), the projected CBF spatial spectrum can be expressed as the convolution of the distribution function of the target signal power in the azimuth plane and the square of the projected beam function;

[0132] In order to Perform deconvolution, and let the square of the projection beam function... Using the point spread function, the spatial spectrum of the projected CBF method is deconvolved using the Richardson-Lucy (RL) iterative algorithm to obtain the output spatial spectrum of the deconvolved projected CBF algorithm.

[0133] Step 3.2: Based on the point spread function Deconvolve the projected CBF spatial spectrum to obtain a new azimuth spectrum.

[0134] The other steps and parameters are the same as those in any of the specific implementation methods one to seven.

[0135] Specific Implementation Method Nine: This implementation method differs from Specific Implementation Methods One through Eight in that the distribution function of the target signal power in the angular plane is:

[0136]

[0137] Where δ(·) represents the unit impulse function.

[0138] The other steps and parameters are the same as those in one of the specific implementation methods one to eight.

[0139] Specific Implementation Method Ten: This implementation method differs from Specific Implementation Methods One to Nine in that the specific process of step three-two is as follows:

[0140] Step 321: Initialize the iteration count n=1, and initialize the initial value of the target signal power distribution in the azimuth plane. Set the total number of iterations to M;

[0141] Step 3.2.2 Calculation

[0142]

[0143] In the formula, This represents the target power distribution result obtained after the (n+1)th iteration. This represents the target power distribution result obtained after the nth iteration;

[0144] Step 3: Determine if n = M is satisfied;

[0145] If the condition is not met, then let n = n + 1 and return to step 3.2.2.

[0146] If satisfied, the target power distribution result obtained in the last iteration is used as the azimuth spectrum obtained by deconvolution. Right now

[0147] The other steps and parameters are the same as those in any of the specific implementation methods one to nine.

[0148] The method of this invention can, under the condition of a given point spread function, obtain the noise from the input according to the Poisson noise statistical standard. Deconvolution in the fuzzy spectrum yields a clear target signal power distribution curve, thereby improving the accuracy of target orientation estimation.

[0149] Specific simulation verification examples:

[0150] The spatial spectrum method based on deconvolution projection CBF (DU-HCBF) of the present invention was verified by simulation, and the results are explained.

[0151] Simulation conditions: Assume the two velocity channels of the vector hydrophone are along the positive x and y axes of a Cartesian coordinate system, respectively. Assume the incident signals are narrowband signals and uncorrelated. Assume the background noise is isotropic, and the received noise power of the sound pressure channel and the two velocity channels are respectively... and SNR is defined as Among them, A sig This represents the signal amplitude.

[0152] Figures 2(a), 2(b), and 2(c) show the spatial spectrum variations of the CBF method, MVDR method, higher-order cumulant CBF method (HCBF), and DU-HCBF method under different signal-to-noise ratios (SNRs), with a target incident angle of 110° and a signal frequency of 2kHz. As shown in Figures 2(a), 2(b), and 2(c), with decreasing SNR, the background spectrum of the CBF and MVDR methods increases significantly, and the spectral peak positions gradually deviate from the true target angle. In contrast, HCBF and DU-HCBF maintain low sidelobes under low SNR conditions, enabling them to estimate the target's orientation with high accuracy. Furthermore, the DU-HCBF method of this invention has a narrower main lobe than the HCBF method, resulting in higher target resolution.

[0153] Figure 3 The variation of azimuth estimation accuracy with SNR for the CBF, MVDR, HCBF, and DU-HCBF methods is presented, with the target incident azimuth at 120°. Figure 3 It can be seen that as the SNR increases, the root mean square error of the orientation estimation of the above methods gradually decreases; the target estimation accuracy of the HCBF method and the DU-HCBF method is similar, and both have smaller orientation estimation errors than other methods.

[0154] Specific experimental verification examples:

[0155] To further verify the CBF spatial spectrum method based on multiple joint processing deconvolution projection of the present invention, a single-vector hydrophone orientation estimation experiment was conducted in an anechoic pool, and the data were processed, analyzed, and explained.

[0156] Figures 4(a), 4(b), 4(c), and 4(d) show the azimuth estimation results of the CBF, HCBF, MVDR, and DU-HCBF methods, respectively. As can be seen from Figures 4(a), 4(b), 4(c), and 4(d), compared with the CBF, HCBF, and MVDR methods, the DU-HCBF method has a narrower beam, a lower background spectrum, and a sharper azimuth spectrum.

[0157] The above examples of the present invention are merely illustrative of the computational model and process of the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is impossible to exhaustively list all possible implementations here. Any obvious variations or modifications derived from the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for estimating the azimuth of a single-vector hydrophone based on the spatial spectrum of CBF (Convolutional-Based Fractional-Fractional) spatial spectrum through multiple joint processing, characterized in that... The method specifically includes the following steps: Step 1: Record the signals received by the single-vector hydrophone at time t as follows: , and ,in, This indicates the received signal from the sound pressure channel. This represents the received signal of the vibration velocity x channel. The received signal in the y-channel represents the vibration velocity; The received signals of the sound pressure channel, the vibration velocity x channel, and the vibration velocity y channel are respectively: (1) in, For target signal, The incident direction of the target signal. For time, , and These represent the sound pressure and vibration velocity received by the x and y channels, respectively; Step 2: Perform multiple joint processing on the received signals from the sound pressure channel and vibration velocity channel of the single-vector hydrophone to obtain the maximum acoustic energy of the combined sound pressure and vibration velocity. The acoustic energy of the sound pressure channel received signal and the vibration velocity x channel received signal The acoustic energy of the sound pressure channel received signal and the vibration velocity y channel received signal Acoustic energy of the vibration velocity x-channel and vibration velocity y-channel The difference between the acoustic energy of the vibration velocity x-channel and the acoustic energy of the sound pressure channel. ; according to , , , and Construct a high-order covariance matrix for a single-vector hydrophone, calculate the projected CBF weighted vector, and then calculate the projected CBF spatial spectrum based on the high-order covariance matrix and the projected CBF weighted vector. In step two, the received signals from the sound pressure channel and vibration velocity channel of the single-vector hydrophone undergo multiple joint processing to obtain the maximum acoustic energy of the combined sound pressure and vibration velocity. The acoustic energy of the sound pressure channel received signal and the vibration velocity x channel received signal The acoustic energy of the sound pressure channel received signal and the vibration velocity y channel received signal Acoustic energy of the vibration velocity x-channel and vibration velocity y-channel The difference between the acoustic energy of the vibration velocity x-channel and the acoustic energy of the sound pressure channel. The specific process is as follows: Step 21: Receive the signals from the vibration velocity x channel respectively. and the received signal of the vibration velocity y-channel Project to The direction is then used to obtain the combined vibration velocity signal based on the projection results. ; Then, based on the sound pressure channel receiving signal and the combined vibration velocity signal Constructing the maximum acoustic energy of sound pressure and combined vibration velocity ; Step 22: The acoustic energy of the sound pressure channel received signal and the vibration velocity x-channel received signal, the acoustic energy of the sound pressure channel received signal and the vibration velocity y-channel received signal, the acoustic energy of the vibration velocity x-channel and the vibration velocity y-channel, and the difference between the acoustic energy of the vibration velocity x-channel and the acoustic energy of the sound pressure channel are respectively: (2) (3) (4) (5) In the formula, This represents the acoustic energy of the signal received by the sound pressure channel and the signal received by the vibration velocity channel. This represents the acoustic energy of the signal received by the sound pressure channel and the signal received by the vibration velocity channel. This represents the acoustic energy of the x-channel and y-channel of vibration. This represents the difference between the acoustic energy of the vibration velocity x-channel and the acoustic energy of the sound pressure channel. The x-channel acoustic energy represents the vibration velocity. This represents the acoustic energy of the sound pressure channel. This represents the noise power of the sound pressure channel. This represents the noise power of the vibration velocity channel; Step 3: Construct a point spread function based on the projection beam function, and deconvolve the projected CBF spatial spectrum based on the point spread function. Use the position of the spectral peak in the azimuth spectrum obtained by deconvolution as the azimuth of the underwater target.

2. The single-vector hydrophone orientation estimation method based on the CBF spatial spectrum of multiple joint processing deconvolution projection as described in claim 1, characterized in that, The combined vibration velocity signal obtained based on the projection result Specifically: (6) In the formula, The projection angle.

3. The single-vector hydrophone orientation estimation method based on the CBF spatial spectrum of multiple joint processing deconvolution projection as described in claim 2, characterized in that, The sound pressure channel received signal and combined vibration velocity signal Constructing the maximum acoustic energy of sound pressure and combined vibration velocity Specifically: Calculate the acoustic energy of sound pressure and combined vibration velocity : (7) In the formula, This is the conjugate transpose operation; Indicates the power of the target signal; And iterate through the projection angles To obtain the maximum acoustic energy of the combined sound pressure and vibration velocity. : (8)。 4. The single-vector hydrophone orientation estimation method based on the CBF spatial spectrum of multiple joint processing deconvolution projection as described in claim 3, characterized in that, According to , , , and The specific process for constructing the high-order covariance matrix of a single-vector hydrophone is as follows: The acoustic energy information of the single-vector hydrophone , , , and Form a vector : (9) In the formula, The guiding vector matrix of the higher-order covariance matrix. The superscript T represents the transpose of the matrix; make ,in, for The left singular vector, It is a singular value. The coefficients are linear. The higher-order covariance matrix of the vector hydrophone for: (10) in, It expresses expectation.

5. The single-vector hydrophone orientation estimation method based on the CBF spatial spectrum of multiple joint processing deconvolution projection as described in claim 4, characterized in that, The process involves calculating the projected CBF weighted vector, and then calculating the projected CBF spatial spectrum based on the higher-order covariance matrix and the projected CBF weighted vector. The specific steps are as follows: The steering vector matrix of the higher-order covariance matrix is ​​projected onto the signal subspace to obtain the projected CBF weighted vector. : (11) In the formula, For scanning angle, Represents the signal subspace; Then projected CBF spatial spectrum for: (12) in, This indicates the calculation of the determinant value.

6. The single-vector hydrophone orientation estimation method based on the CBF spatial spectrum of multiple joint processing deconvolution projection as described in claim 5, characterized in that, The specific process of step three is as follows: Step 31: Rewrite formula (12) as follows: (13) in, It is the distribution function of the target signal power in the azimuth plane. Represents the convolution calculation symbol. For the projection beam function, , Let be the point spread function of the projected CBF; Step 3.2: Based on the point spread function Deconvolve the projected CBF spatial spectrum to obtain a new azimuth spectrum. .

7. The single-vector hydrophone orientation estimation method based on the CBF spatial spectrum of multiple joint processing deconvolution projection as described in claim 6, characterized in that, The distribution function of the target signal power in the angular plane is: (14) in, This represents the unit impact function.

8. The single-vector hydrophone orientation estimation method based on the CBF spatial spectrum of multiple joint processing deconvolution projection as described in claim 7, characterized in that, The specific process of step 32 is as follows: Step 321: Initialize the number of iterations The initial value for the target signal power distribution in the azimuth plane is... Set the total number of iterations to ; Step 3.2.2 Calculation : (15) In the formula, Indicates the first The target power distribution results obtained after the iteration. Indicates the first The target power distribution results obtained after the iteration; Step 3.2.3: Determine if the condition is met. ; If not satisfied, then let Return to step 322; If satisfied, the target power distribution result obtained in the last iteration is used as the azimuth spectrum obtained by deconvolution. .

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