Direction finding method for weak broadband targets using single vector hydrophone based on azimuth integrator

Abstract

The present invention is applicable to the field of underwater acoustics and provides a single-vector hydrophone weak broadband target direction-finding method based on an azimuth accumulation meter. The method comprises the following steps: intercepting the sound pressure, x-direction vibration velocity, and y-direction vibration velocity signals collected by the single-vector hydrophone for a duration of T and dividing them into N snapshots. The spectrum of each snapshot is obtained by Fourier transform, and the average sound intensity spectrum in the x- and y-directions is calculated. A frequency point azimuth estimation curve is obtained according to a formula. Histogram statistics are generated based on angular intervals and using sound intensity as weights, and a time-azimuth history diagram of the acoustic energy flow cross-spectrum method is repeatedly generated. The direction-finding method described in the present invention is resistant to interference and adapts to low signal-to-noise ratios. By focusing on a target-dominated set of high-directional concentration frequencies, the effects of noise and outlier interference are weakened, making the direction-finding result more consistent with the actual target trajectory and improving the direction-finding accuracy of the single-vector hydrophone.

Description

Technical Field

[0001] The invention belongs to the field of underwater acoustics, and in particular relates to a weak broadband target direction finding method using a single vector hydrophone based on an azimuth internal accumulation meter. Background Art

[0002] The single-vector hydrophone can synchronously measure sound pressure and particle velocity information. Through the joint processing of sound pressure and velocity, it can achieve all-round passive detection of the target, showing significant advantages in applications such as sonar buoys and underwater mobile platforms.

[0003] At present, a variety of single-vector hydrophone direction-finding methods have been developed, among which the acoustic energy flow cross-spectrum histogram method (Yao Zhixiang, Hui Junying, Yin Jingwei, et al. Four azimuth estimation methods based on single-vector hydrophone [J]. Ocean Engineering, 2006, (01): 122-127) has the most stable direction-finding results and is the most widely used in practical engineering. This method calculates the cross-spectrum of sound pressure and vibration velocity and generates a azimuth statistical histogram using the frequency intensity as the weight. It works better for strong signals. When the signal-to-noise ratio is low or there is strong interference, the azimuth estimation of the interference frequency will obscure the true target azimuth, resulting in serious deviations in the statistical results.

[0004] Brooker et al. proposed a method for processing low signal-to-noise ratio signals (JDB, FGE. Alucky covariance estimator based on cumulative coherence. [J]. The Journal of the Acoustical Society of America, 2023, 154 (4): 2572-2578.). By calculating the cumulative coherence of different snapshot signals, screening snapshots with strong coherence, and eliminating low signal-to-noise ratio snapshot signals that are greatly affected by noise, the target detection performance under low signal-to-noise ratio conditions is significantly improved. This method works well for long linear arrays, but the performance improvement for single-vector hydrophones is very limited.

[0005] Therefore, a single-vector hydrophone weak broadband target direction finding method based on azimuth accumulation meter is needed to solve the above problems. Summary of the Invention

[0006] The purpose of the embodiments of the present invention is to provide a weak broadband target direction finding method using a single vector hydrophone based on an intra-azimuth accumulation meter to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] The method for weak broadband target direction finding using a single vector hydrophone based on azimuth internal accumulation meter includes the following steps:

[0009] S1. Use a single vector hydrophone to collect sound pressure and vibration velocity signals;

[0010] S2. Preprocess the signal obtained in S1 to obtain the corresponding spectrum; calculate the vibration velocity signal using the formula x Direction and y Average sound intensity in a direction 、 , obtain the frequency point azimuth estimation curve ; By angle interval , using sound intensity as weight, using formula to calculate the histogram statistics, repeatedly generating the time-direction history diagram of the sound energy flow cross-spectrum method;

[0011] S3. Build m Frequency azimuth vector , No. n Frequency azimuth vector Similarly, calculate the frequency pair Azimuth inner product, assembling the orientation vector matrix V And construct the inner product matrix G ;

[0012] S4, yes G Sort each row in descending order to get the matrix , after discarding the first column, k Sum the columns and find the frequency point corresponding to the maximum cumulative value in the azimuth and the most concentrated frequency point subset in azimuth distribution , use the subset sound intensity to re-count, generate the filtered time-azimuth history diagram, and repeat the processing of the full-time signal, where, k <M,M is the total number of frequency points.

[0013] A further technical solution is that the signal is sound pressure 、 x Directional vibration velocity 、 y Directional vibration signal ; The signal preprocessing is to intercept the duration T The signal data is divided into N A quick shot, and the spectrum is obtained by Fourier transform.

[0014] In a further technical solution, in step S2, the average sound intensity in each direction is calculated using the formula to obtain the frequency point direction estimation. , the formula is: and ,in, is the conjugate operation, To get the real part, For the n The sound pressure spectrum of a quick beat, For the n A quick shot x Directional vibration velocity spectrum, For the n A quick shot y Directional vibration velocity spectrum, for x The average sound intensity in the direction, for y The average sound intensity in a direction.

[0015] A further technical solution is that in step S2, the frequency point direction estimation curve The calculation formula is: , It is the inverse tangent operation, and the angle corresponding to the tangent value is obtained.

[0016] Further technical solution, in "step S2", the formula of the histogram statistics is ,and ,in 、 For the l intervals and their corresponding statistical weights, 、 、 、 Respectively m Angle estimation value of each frequency point, x Directional sound intensity, y Directional sound intensity and total sound intensity.

[0017] Further technical solution, in "step S3", the frequency point The calculation formula of azimuthal inner product is: , where 、 Respectively m 、 n The unit vector of the frequency point direction, For the m and n The azimuth inner product value of the frequency point; assemble the azimuth vector matrix V The formula is: , construct the inner product matrix G The formula is: , for V Transpose of a matrix.

[0018] A further technical solution is that in step S4, the frequency point corresponding to the maximum azimuth cumulative value is found. and the most concentrated frequency point subset in azimuth distribution The formula is: ,in, for The new matrix obtained by sorting each row of in descending order is: for No. i Rank n Column element.

[0019] This method mainly addresses the problem that single-vector hydrophones are difficult to detect targets under conditions of low signal-to-noise ratio or strong line spectrum interference. The azimuth vector of each frequency point is used to construct the azimuth inner product matrix and the azimuth inner product accumulation value is obtained to find the subset of frequency points with the most concentrated azimuth distribution. This method can weaken the influence of low signal-to-noise ratio adverse conditions and outlier strong line spectrum interference on target direction finding to a certain extent, and improve the accuracy of single-vector hydrophone direction finding.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] The present invention is anti-interference and adaptable to low signal-to-noise ratio: it uses the accumulation meter within the bearing to screen the frequency points, eliminates noise and interference frequencies in non-target directions, and significantly reduces the direction finding error in low signal-to-noise ratio or strong interference scenarios;

[0022] The present invention improves accuracy: by focusing on the target-dominated high-directional concentration frequency point, the influence of noise and outlier interference is weakened, the direction finding result is more consistent with the actual target trajectory, and the direction finding accuracy of the single vector hydrophone is improved;

[0023] The present invention has engineering practicality: verified by actual sea trials, it effectively improves the direction finding fluctuations under low signal-to-noise ratio, solves the problem that traditional methods are susceptible to strong line spectrum interference, and is suitable for engineering scenarios such as sonar buoys and underwater platforms.

[0024] In order to more clearly illustrate the structural features and effects of the present invention, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Flowchart of the present invention;

[0026] Figure 2 This is a schematic diagram of the actual distance of the present invention;

[0027] Figure 3 This is a schematic diagram of the actual speed of the present invention;

[0028] Figure 4 Schematic diagram of weighted histogram statistical results of the present invention;

[0029] Figure 5 This is a schematic diagram of the statistical results of the method of the present invention;

[0030] Figure 6 A schematic diagram comparing the maximum value trajectories of various methods of the present invention;

[0031] Figure 7 This is a schematic diagram of the hit rate of the azimuth interval in the first 40 minutes of the present invention;

[0032] Figure 8 This is a schematic diagram comparing the average hit rates of the azimuth intervals in the first 40 minutes of the present invention. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0034] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0035] Example 1

[0036] like Figures 1-8 As shown, an embodiment of the present invention provides a single vector hydrophone weak broadband target direction finding method based on azimuth intra-integration meter, comprising the following steps:

[0037] S1. Use a single vector hydrophone to collect sound pressure and vibration velocity signals;

[0038] S2, preprocess the signal obtained in S1 to obtain the spectrum; the signal is the sound pressure 、 x Directional vibration velocity 、 y Directional vibration speed signal ; The signal preprocessing is to intercept the duration T The vibration velocity signal data is divided into N A snapshot, obtained by Fourier transform n Snapshot spectrum 、 、 , using the formula and formula Calculate each snapshot x Direction and y Directional average sound intensity;

[0039] Using the formula Get the estimated value of the azimuth of each frequency point;

[0040] By angle interval , using the frequency point sound intensity as the weight to make histogram statistics, l The interval range is , where Indicates a left-closed and right-open interval;

[0041] If a frequency If the corresponding azimuth value is exactly in this interval, the statistical value of the interval is added to the sound intensity of the frequency point. According to formula (4) Calculation, frequency point sound intensity According to formula (5) Calculate and repeat the above steps for all time to obtain the time-orientation history diagram of the acoustic energy flow cross-spectrum method;

[0042] S3. Construct the mth frequency point azimuth vector , No. m Frequency The estimated signal bearing value is , No. n The same applies to each frequency point. m and n The inner product formula of the frequency point direction estimation vector To measure the similarity of the estimated azimuth results of the two frequency points;

[0043] Inner product matrix According to the orientation vector matrix formula: Calculate, where is the matrix after assembling the unit vectors of each frequency point;

[0044] When the bearings of two frequency signals are exactly the same, their inner product is equal to 1; when the bearings of two frequency signals differ by 180 degrees, their inner product is equal to -1.

[0045] S4. Filter the frequency subset with high directional concentration. G is A symmetric matrix with all diagonal elements set to 1, corresponding to the inner product of the azimuth vector of each frequency point.

[0046] Assume that the signal frequency point dominated by the target radiation noise is screened, and the accumulated value in the azimuth is used to screen the frequency point signals closest to the azimuth. The specific process is as follows: G Sort each row in descending order to get , the first column elements are all 1, which are discarded in the subsequent summation; assuming that the screening direction is closest to the target direction k Frequency points ( ),right Each row starts from the second column k Sum the columns and the frequency point corresponding to the maximum azimuth cumulative value Calculated by the following formula: , where It is the accumulated value within the maximum direction;

[0047] Find at the same time The frequency point with the closest azimuth angle k The frequency point is i Row 2 to n +1 column element corresponding to the frequency point, the set is recorded as ;

[0048] The frequency subset with the most concentrated azimuth distribution is obtained by the above method ,Will The corresponding sound intensity in the frequency point is reused to calculate the azimuth estimation value formula, and the azimuth estimation curve after the azimuth accumulation method is obtained. T The signal is processed repeatedly in the above steps until the signal ends, and the time-azimuth history diagram of the entire signal time period can be obtained.

[0049] parameter: is the transpose operation, G is the azimuthal inner product matrix, for The new matrix obtained by sorting each row of in descending order is: k is the number of high directional concentration frequency points that you want to screen, M is the number of frequency points in the frequency band, is the frequency point corresponding to the maximum accumulated value in the azimuth, is the maximum value of the accumulated sum in the direction, and max is the maximum value operation. for Matrix i Rank n Column elements, For The nearest front k Frequency points, It is the subset of frequency points with the most concentrated azimuth distribution.

[0050] In this embodiment, a single vector hydrophone passive direction finding experiment was carried out in the northern part of a certain sea area in 2024. The hydrophone sampling frequency was 64000 Hz, and a ship was used as the target sound source. A total of 60 minutes of data was collected. The target distance gradually decreased from 8 km to 1.5 km and then increased to 8 km. The speed increased from the initial 8 knots to about 10 knots. The signal processing parameters were as follows: sampling frequency 4000 Hz, filter frequency band 150~302 Hz, FFT point number 16384, the corresponding filter frequency band contains 623 frequency points, Figure 4 It can be seen that the trajectory diverges severely in the first 10 minutes and the signal-to-noise ratio is very low;

[0051] Data division and preliminary processing: The filtered data is divided into 120 equal-length azimuth estimation time windows, each 30 seconds long. The length of each FFT time window is 6 seconds, and the overlap rate of each FFT time window is 50%. It is calculated that a single azimuth estimation time window contains 9 snapshots. In a single azimuth estimation time window, the three-channel time domain data is calculated using FFT to obtain its spectrum, and then the spectrum is calculated according to the formula and formula Calculate x 、 y Directional sound intensity spectrum and ; Divide the statistical interval according to the preset angle interval, formula and formula The angle calculation and statistics are performed using the formula to obtain the azimuth estimation curve of each time window. Finally, the statistical results of all time windows are combined to obtain the azimuth time history diagram of the target.

[0052] Azimuth inner product matrix construction: Calculate the direction vector matrix for all frequency points in a single azimuth estimate , through the formula The azimuth inner product matrix of 623 frequency points in a single azimuth estimation is calculated G .

[0053] Frequency point screening and result optimization: Determine the number of high-direction concentration frequency points you want to screen k is 35, yes G Sort in descending order along the rows , according to the formula Calculate the cumulative sum of each row from column 2 to column 36, and then find the row number corresponding to the maximum cumulative sum, which is the corresponding core frequency point ; The nearest front k The frequency points are the frequency points corresponding to the elements in the 2nd to 36th columns of the row, and finally the subset of frequency points with the most concentrated azimuth distribution is obtained. , and then use this subset to perform acoustic energy flow cross-spectrum histogram statistics again according to step 2.

[0054] The working principle and use process of the present invention:

[0055] First, the direction of each frequency point is estimated through the joint processing of sound pressure and vibration velocity. Then, the inner product matrix of the direction vector is constructed to measure the direction similarity between the frequency points. Finally, the subset of frequency points with the most concentrated direction distribution is selected to reduce the influence of noise and interference. This allows the direction finding results to focus on the real target and achieve accurate direction finding under low signal-to-noise ratio and strong interference.

[0056] like Figure 4 and Figure 5 As shown in the figure, the dotted line is the actual GPS trajectory. Comparative analysis shows that the conventional acoustic energy flow cross-spectrum weighted histogram statistical method has a low signal-to-noise ratio in the first 10 minutes, while the single-vector hydrophone weak broadband target direction finding method based on the azimuth accumulation meter has significant noise suppression capabilities, successfully improving the broadband target direction finding accuracy under low signal-to-noise ratio conditions.

[0057] like Figure 6 The figure shows a comparison of the azimuth corresponding to the maximum value in each time window extracted by the two methods and the true azimuth. From this figure, we can see that in the low signal-to-noise ratio period, the conventional method has a drastic trajectory jump and is basically ineffective, while this method can still maintain a small error range, which is in line with the changing trend of the target trajectory.

[0058] At the same time, a symmetrical azimuth interval of a certain width is set up with the true azimuth as the center, and the proportion of the statistical weights falling within the interval to all statistical weights within the time window is counted. This proportion is called the azimuth interval hit rate, which can quantify the direction finding capabilities of the two methods.

[0059] Measures the concentration of direction finding results around the true azimuth;

[0060] Since both methods have a large error with the true azimuth in the last 20 minutes, which is suspected to be a fixed error caused by the hydrophone itself, the following analysis only uses the first 40 minutes of data;

[0061] The half-width of the interval is , Figure 7 and Figure 8 The following are the azimuth interval hit rates of the two methods at different times in the first forty minutes and the average azimuth interval hit rates of the two methods in the first forty minutes. It can be seen that the proposed method can maintain high accuracy at most times, and is significantly better than the conventional method even under low signal-to-noise ratio conditions.

[0062] Therefore, this method has been verified by actual sea trial data, and the results show that this method can effectively improve the direction finding fluctuation problem under low signal-to-noise ratio conditions, minimize noise and retain target signals, and at the same time solve the shortcomings of traditional acoustic energy flow cross-spectrum weighted histogram statistics that are easily affected by strong line spectrum interference. It has significant advantages in situations where background noise and line spectrum interference are strong.

[0063] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A single vector hydrophone weak broadband target direction finding method based on azimuth internal accumulation meter is characterized by: The following steps are involved: S1. Use a single vector hydrophone to collect sound pressure and vibration velocity signals; S2, preprocessing the sound pressure and vibration velocity signals obtained in S1 to obtain a spectrum; Calculate the vibration velocity signal using the formula x Direction and y The average sound intensity in the direction is used to obtain the frequency point direction estimation curve ; By angle interval , using sound intensity as weight, using formula to calculate the histogram statistical results, repeatedly generating the time-azimuth history diagram of the sound energy flow cross-spectrum method; S3. Build m Frequency azimuth vector , calculate the frequency pair Azimuth inner product, assembling the orientation vector matrix V And construct the inner product matrix G ; S4, yes G Sort each row in descending order to get a new matrix , after discarding the first column, k Sum the columns and find the frequency point corresponding to the maximum cumulative value in the azimuth and the most concentrated frequency point subset in azimuth distribution , use the subset sound intensity to re-count, generate the filtered time-azimuth history diagram, and repeat the processing of the full-time signal, where, k <M,M is the total number of frequency points.

2. The single vector hydrophone weak broadband target direction finding method based on azimuth internal accumulation meter according to claim 1 is characterized in that: The vibration velocity signal is the sound pressure 、 x Directional vibration velocity 、 y Directional vibration speed signal ; The vibration speed signal preprocessing is to intercept the duration T The vibration velocity signal data is divided into N A quick shot, and the spectrum is obtained by Fourier transform.

3. The single vector hydrophone weak broadband target direction finding method based on azimuth internal accumulation meter according to claim 2 is characterized in that: In "Step S2", the average sound intensity in each direction is calculated using the formula to obtain the frequency point direction estimation curve , the formula is: and ,in, is the conjugate operation, To get the real part, For the n The sound pressure spectrum of a quick beat, For the n A quick shot x Directional vibration velocity spectrum, For the n A quick shot y Directional vibration velocity spectrum, for x The average sound intensity in the direction, for y The average sound intensity in a direction.

4. The single vector hydrophone weak broadband target direction finding method based on azimuth internal accumulation meter according to claim 3 is characterized in that: In "step S2", the frequency point direction estimation curve The calculation formula is: , It is the inverse tangent operation, and the angle corresponding to the tangent value is obtained.

5. The single vector hydrophone weak broadband target direction finding method based on azimuth internal accumulation meter according to claim 1 is characterized in that: In "step S2", the formula for the histogram statistics is: and ,in 、 is the lth interval and its corresponding statistical weight, 、 、 、 Respectively m Angle estimation value of each frequency point, x Directional sound intensity, y Directional sound intensity and total sound intensity.

6. The single vector hydrophone weak broadband target direction finding method based on azimuth internal accumulation meter according to claim 1 is characterized in that: In "step S3", the frequency point The calculation formula of azimuthal inner product is: , where 、 Respectively m 、 n The unit vector of the frequency point direction, For the m and n The azimuth inner product value of each frequency point; Assemble the orientation vector matrix V The formula is: , construct the azimuthal inner product matrix G The formula is: , for V Transpose of a matrix.

7. The single vector hydrophone weak broadband target direction finding method based on azimuth internal accumulation meter according to claim 1 is characterized in that: In step S4, the frequency point corresponding to the maximum azimuth cumulative value is found. and the most concentrated frequency point subset in azimuth distribution The formula is: ,in, for The new matrix obtained by sorting each row of in descending order is: for No. i Rank n Column element.

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