Target DOA estimation method based on instantaneous phase difference weighting of acoustic energy flow in frequency domain

Abstract

The invention provides an underwater target DOA estimation method based on the weighted instantaneous phase difference of frequency domain acoustic energy flow: 1. extract low-frequency information and perform discretization processing; perform FFT analysis on three-channel discrete signals, conjugate cross-spectra of sound pressure channels with two-dimensional vector velocity channels respectively, and extract the instantaneous phase of frequency domain acoustic energy flow; 2. repeat step 1 M times, update M received signals, and calculate the instantaneous phase difference of each frequency unit on X-axis and Y-axis components of frequency domain acoustic energy flow in sequence; 3. perform variance calculation on the instantaneous phase difference of all frequency units according to the calculation results; 4. perform average fitting and mean filtering on the variance of the instantaneous phase difference of X-axis and Y-axis acoustic energy flow components; 5. use the inverse of the calculation result as a weight, and perform weighted variance of the instantaneous phase difference of all frequency units under each component of frequency domain acoustic energy flow to obtain frequency domain acoustic energy flow; 6. extract the real part of the frequency domain acoustic energy flow component, calculate and count the acoustic energy flow at each frequency point, and obtain the target direction.

Description

Technical Field

[0001] The present invention belongs to the technical field of signal processing, and relates to a method for estimating the direction of an underwater target under long-range low signal-to-noise ratio conditions, and specifically to a method for estimating the DOA of an underwater target based on weighting of the variance of the instantaneous phase difference of frequency-domain acoustic energy flow of a vector hydrophone. Background Art

[0002] Underwater target azimuth estimation has always been a hot research topic in the field of hydroacoustics. Traditional pressure hydrophones can only measure the scalar information of the sound field, forming a certain spatial pointing and spatial anti-interference ability, which requires a larger array aperture. Vector hydrophone is a new type of hydroacoustic signal measurement device, which can synchronously and co-pointly measure the sound pressure and particle vibration velocity of the sound field in the underwater space, providing more comprehensive sound field information. Compared with pressure hydrophones, vector hydrophones have both frequency-independent dipole directivity and strong resistance to isotropic noise, which can achieve unambiguous azimuth estimation of the target in the entire space. Vector hydrophones can broaden the dimension of hydroacoustic signals and have great advantages in the field of hydroacoustics.

[0003] In the research on vector hydrophone azimuth estimation, Nehorai first proposed two simple algorithms for target azimuth estimation using a single vector hydrophone. One is a target azimuth estimation method based on acoustic energy flow, and proved that the target azimuth estimation performance of this method is close to the Cramer-Rao bound; the other is a method based on the velocity correlation matrix, which can give the target azimuth without any phase information by synthesizing the velocity components. Hochwald pointed out that a single vector hydrophone can distinguish two incompletely related targets in three-dimensional space. Professor Hui Junying studied the theoretical basis of the joint signal processing technology of sound pressure and velocity, and pointed out that the joint signal processing of sound pressure and velocity has good anti-interference ability in isotropic noise fields. Huang Wentao introduced the principle of the complex sound intensity algorithm in detail, and proved the effectiveness of complex sound intensity in target azimuth estimation and multi-target recognition through sea trials. With the rapid development of submarine stealth technology, the signal-to-noise ratio has decreased, and many improved algorithms have been applied to underwater target azimuth estimation. Zhang Xiaoyong proposed a sliding window to find the azimuth interval, and used the centroid method to obtain the energy center to calculate the azimuth, thereby improving the accuracy of azimuth estimation. Wang Chao tracked the target's azimuth, eliminated outliers, and improved the azimuth accuracy. However, the above methods did not fully utilize the spectrum characteristics of underwater target radiation noise, and the azimuth estimation accuracy of long-range low signal-to-noise ratio targets still had problems such as large errors and estimated value jumps.

[0004] In fact, the composition of noise in the marine environment is extremely complex, and the energy of these environmental noises fluctuates. These factors will cause very serious interference to the acoustic signals received by the vector hydrophone. At the same time, with the continuous development of stealth technology, the broadband continuous spectrum acoustic radiation in the frequency band above 100Hz has been significantly reduced, even lower than the ocean hydrodynamic noise level, which has brought great pressure to the traditional passive sonar detection system. Summary of the invention

[0005] In view of the phenomenon that the signal-to-noise ratio of the data received by the vector hydrophone continues to decrease, the purpose of the present invention is to provide a target azimuth estimation method based on the weighted instantaneous phase difference of the frequency domain acoustic energy flow of the vector hydrophone, which weights each frequency domain acoustic energy flow component to improve the signal-to-noise ratio of the received signal, thereby achieving the purpose of high-precision azimuth estimation.

[0006] The present invention adopts the following technical scheme:

[0007] The underwater target DOA estimation method based on the weighting of the instantaneous phase difference of the frequency domain acoustic energy flow comprises the following steps:

[0008] Step 1: Extract low-frequency information from the sound pressure information and two-dimensional vector velocity information collected by the vector hydrophone according to the channel, and then perform discretization processing; perform FFT analysis on the obtained three-channel discrete signals respectively, and then perform conjugate cross-spectrum on the sound pressure channel and the two-dimensional vector velocity channel respectively to extract the instantaneous phase of the frequency domain acoustic energy flow;

[0009] Step 2, repeat step 1 M times, update to obtain M received signals, and calculate the instantaneous phase difference of each frequency unit on the X-axis and Y-axis components of the frequency domain acoustic energy flow according to the signal update sequence;

[0010] Step 3: According to the calculation result of step 2, the variance of the instantaneous phase difference values ​​of all frequency units is calculated;

[0011] Step 4, performing average fitting and mean filtering on the variance of the instantaneous phase difference between the X-axis and Y-axis acoustic energy flow components;

[0012] Step 5, taking the inverse of the result calculated in step 4 as the weight, weighting the variance of the instantaneous phase difference of all frequency units under each component of the frequency domain acoustic energy flow, and obtaining the frequency domain acoustic energy flow;

[0013] Step 6: After the signals collected by the vector hydrophone are processed in steps 1 to 5, the real part of the frequency domain acoustic energy flow component is extracted, the acoustic energy flow at each frequency point is calculated and counted, and the target direction is obtained.

[0014] Preferably, in step 1, the frequency of the low-frequency information is 15 Hz to 200 Hz.

[0015] As a preferred embodiment, in step 1, the received signal is filtered and amplified to extract the 15Hz to 200Hz low-frequency information at a sampling rate of f s The sound pressure and velocity information collected by the vector hydrophone is sampled to obtain a discrete signal p(n'T s )、vx(n'T s )、vy(n'T s ), where T s =1 / f s , n'=1,2,…,N', N' is the data length;

[0016] p(n'T s )、vx(n'T s )、vy(n'T s ) to perform FFT analysis, and perform complex conjugate calculation of sound pressure and vibration velocity according to formula (10):

[0017]

[0018] In formula (10), 1≤k≤K, K is the number of frequency units obtained by each FFT analysis; ω s =2π / KT s is the frequency sampling interval in FFT analysis; P(kω s )、Vx(kω s )、Vy(kω s ) are the results obtained by Fourier transform of discrete signals of sound pressure and vibration velocity.

[0019] Preferably, in step 2, the received signal is updated, and step 1 is repeated M times to obtain M received signals. The X-axis and Y-axis components of the frequency domain acoustic energy flow are calculated according to formula (11), and each frequency unit on the frequency domain acoustic energy flow component obtains M instantaneous phase values, which are recorded as

[0020]

[0021] The instantaneous phase difference of all frequency units of the X-axis and Y-axis components of the frequency domain acoustic energy flow is calculated according to formula (12), which is expressed as

[0022]

[0023] In formula (12), 1≤n≤M-1, 1≤k≤K.

[0024] Preferably, in step 3, the instantaneous phase difference values ​​of all frequency units on each component of the frequency domain acoustic energy flow are calculated according to formula (13), which is expressed as

[0025]

[0026] In formula (13), It represents the average value of the instantaneous phase difference of all frequency units on each component of the frequency domain acoustic energy flow.

[0027] As a preferred method, in step 4, the variance of each component is averaged according to formula (14), and then averaged and filtered to obtain

[0028]

[0029] Preferably, in step 5, the variance weighted statistics of the instantaneous phase difference of the frequency unit under each component of the frequency domain acoustic energy flow are performed to obtain the estimated values ​​of the spectrum and the signal line spectrum, such as formula (15):

[0030]

[0031] In the formula, 1≤m≤M.

[0032] The present invention has the following beneficial effects:

[0033] 1. The complex sound intensity azimuth estimation technology of the present invention is based on the vector hydrophone, which makes full use of the sound vector sensor to pick up more comprehensive sound field information, and uses the sound pressure and particle vibration velocity joint information processing technology to have a strong line spectrum detection capability, and can estimate the directions of multiple targets at the same time.

[0034] 2. Based on the complex sound intensity, the present invention utilizes the characteristics that the instantaneous phase difference corresponding to the target line spectrum frequency unit is relatively stable, and the instantaneous phase difference corresponding to the background noise frequency unit changes randomly, and weights the variance of the instantaneous phase difference of each component frequency unit of the frequency domain acoustic energy flow. This can further suppress the energy interference of the background noise, enhance the line spectrum signal-to-noise ratio gain, and achieve high-precision azimuth estimation of underwater targets.

[0035] 3. The present invention has no requirements on the bandwidth and frequency of the received signal and has good robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of the noise spectrum radiated by underwater targets.

[0037] Figure 2 It is a schematic diagram of underwater target DOA estimation based on the weighting of instantaneous phase difference of acoustic energy flow in frequency domain.

[0038] Figure 3 Schematic diagram of simulation results in the embodiment.

[0039] Figure 4 Schematic diagram of the deployment of the signal collection system in the embodiment.

[0040] FIG. 5( a ) and FIG. 5( b ) are respectively a conventional LOFAR diagram and a LOFAR diagram based on a weighted method of instantaneous phase difference of acoustic energy flow in the frequency domain in the embodiment.

[0041] FIG. 6( a ) and FIG. 6( b ) are respectively a time-azimuth history diagram of a conventional method and a time-azimuth history diagram of a weighted method based on instantaneous phase difference of acoustic energy flow in the frequency domain in the embodiment. DETAILED DESCRIPTION

[0042] The present invention will be described in further detail below with reference to the accompanying drawings.

[0043] The present invention is a technical solution for estimating the target DOA based on the weighted instantaneous phase difference of the frequency domain acoustic energy flow. It is based on the complex sound intensity multi-target direction of arrival (DOA) estimation method, makes full use of the acoustic vector sensor, and picks up more comprehensive sound field information. At the same time, the present invention uses the characteristics that the instantaneous phase difference corresponding to the target line spectrum frequency unit is relatively stable, and the instantaneous phase difference corresponding to the background noise frequency unit changes randomly, and performs the variance weighting of the instantaneous phase difference of each component frequency unit of the frequency domain acoustic energy flow, which can further enhance the line spectrum signal-to-noise ratio gain and suppress the energy interference of the background noise, so as to achieve high-precision estimation of the underwater target position.

[0044] 1. Data Model

[0045] During the navigation or operation of underwater targets such as ships, submarines or torpedoes, the vibration of various equipment such as propellers will radiate sound waves through the hull into the water, becoming the main noise source of ship target radiation. Among them, the sources of underwater target radiation noise field are mainly divided into three categories: mechanical noise, propeller noise and hydrodynamic noise. The noise radiated by underwater targets can be refined into the noise of each component, and the noise of each component can be divided into two basic types of representation: the first and most common is a broadband continuous spectrum, whose noise intensity is a continuous function of frequency, with a peak value between 100 and 1000 Hz. The other is single-frequency noise, whose spectrum is a line spectrum, mainly distributed in the low frequency band, and in some cases extends to around 1000 Hz. At the same time, the line spectrum has the characteristics of relatively stable frequency and concentrated energy. In fact, the noise radiated by underwater targets is mostly composed of multi-line spectra and continuous spectra of different frequencies, such as Figure 1 shown.

[0046] The simplified form of underwater target radiation signal can be expressed as:

[0047]

[0048] In the formula, A n (t) is the amplitude of the line spectrum signal, which is a random variable uniformly distributed on [0, 1]; f n is the line spectrum signal frequency, is the initial phase of the line spectrum signal, t is the target radiation signal moment, bs(t) is the bandwidth signal; N is the number of assumed independent components, and bs(t) are independent of each other, and It obeys the uniform distribution of [0~2π].

[0049] Among them, the ratio of the nth line spectrum signal component to the average spectrum level of the continuous spectrum is for:

[0050]

[0051] Where B is the bandwidth of the bandwidth signal bs(t), σ bs 2 is the variance of the bandwidth signal bs(t).

[0052] From formula (2), we can see that the line spectrum signal level of the target radiation signal is much higher than the average spectrum level of the broadband signal. If we want to detect the target direction, we can do so by detecting the line spectrum signal.

[0053] 2. Principle of Direction Estimation

[0054] The two-dimensional vector hydrophone consists of a sound pressure sensor and two particle velocity sensors located in the X-axis direction and the Y-axis direction respectively. Through these three channels, the two-dimensional vector velocity information and sound pressure information of the sound field can be obtained synchronously at the same point. The information model picked up by the vector hydrophone can be expressed as follows:

[0055]

[0056] Where p(t) is the sound pressure signal picked up by the vector hydrophone, v x (t) is the velocity signal in the X-axis direction picked up by the vector hydrophone, v y (t) is the Y-axis velocity signal picked up by the vector hydrophone, α is the azimuth angle of the target in the horizontal direction, n p (t), n vx (t), n vy (t) are the interference noise of sound pressure and vibration velocity in the X-axis and Y-axis directions respectively.

[0057] The complex sound intensity method first performs Fourier transform on the output of the vector hydrophone to obtain P(f), V x (f) V y (f) Then the sound pressure is conjugate-multiplied with the two velocity components to obtain two cross spectra:

[0058]

[0059] In the ocean channel, the sound pressure and the vibration velocity are in phase. According to the basic characteristics of Fourier transform, the energy of the in-phase input is concentrated in the real part of the cross spectrum. Therefore, taking the real part of the result of equation (4) will give the sound energy flow component I of each frequency. x (f) and I y (f), thus obtaining the modulus R(f) of the acoustic energy flow at each frequency point:

[0060]

[0061] The estimated value of the sound source direction at each frequency point is:

[0062]

[0063] The azimuth corresponding to each frequency point is calculated by formula (6). When the frequency range of the target radiation noise is unknown, the target azimuth cannot be directly obtained by formula (6). Therefore, it is necessary to use the histogram statistics method to count the azimuths corresponding to each frequency point. When there is a broadband target signal, the azimuths corresponding to each frequency point of the signal fall within a certain interval, while the azimuths corresponding to each frequency point of the noise band are randomly distributed. Divide [0°, 360°] into several intervals Δθ, then the total number of intervals N allowed for the spatial angle is:

[0064]

[0065] From the above formula, we can get the interval range of the horizontal azimuth angle: 0~Δθ, Δθ~2Δθ, 2Δθ~3Δθ, ..., (N-1)Δθ~NΔθ. Scan the entire frequency band to get the corresponding azimuth angle. Assume that the number of estimated azimuths of each frequency point falling into each statistical angle interval is m1, m2, m3, ..., m N , then calculate the following statistic S(θ):

[0066]

[0067] In the formula, S s (f) is the modulus of the acoustic energy flow that conforms to the angular range of the statistical interval. The distribution of the acoustic energy flow in each statistical interval is obtained by formula (8).

[0068] 3. Principle of weighted processing of line spectrum instantaneous phase difference stability

[0069] First, the collected data is divided into blocks for FFT analysis, and the frequency domain sound energy flow obtained by complex conjugation operation of the sound pressure and the two vibration velocity channels is recorded as X(m,f k ), m=1,2,…,M,k=1,2,…,K; where M is the number of blocks of collected data, and K is the number of frequency units obtained in each block.

[0070] In the filter band f1~f K When the target radiation signal occupies only one or several frequency units f l , l=1,2,…,L, the rest are noise frequency units f s , s=1,2,…,S,S+L=K. If the acoustic energy flow is directly counted by histogram, the acoustic energy flow components corresponding to all frequency units will be equivalently weighted into the final histogram result. Since the acoustic energy flow of background noise fluctuates greatly, the final acoustic energy flow is greatly affected by the noise, which is not convenient for the final target direction estimation.

[0071] Therefore, the instantaneous phase of all frequency units in the complex conjugate result of each block of data is extracted, which is recorded as Since the sampled data is processed in equal-length blocks, the number of frequency units obtained by complex conjugate operation in each block is a fixed value, and it can be obtained that the phase difference of the frequency unit is relatively stable at different times. That is, it satisfies the following expression:

[0072]

[0073] Among them, due to the strong stability of the line spectrum, its f l It is relatively fixed. Stable and with relatively small variance. The noise f s is a random change. It is not stable and its variance is much larger than the line spectrum.

[0074] In this regard, by using the difference in the instantaneous phase difference between the line spectrum frequency unit and the noise frequency unit in the frequency domain acoustic energy flow, the variance of the instantaneous phase difference of each component frequency unit of the frequency domain acoustic energy flow is weighted and summed, which can weaken the influence of noise on the final acoustic energy flow, enhance the signal-to-noise ratio gain of the line spectrum detection, and suppress the interference of background noise energy. The above content theoretically discusses the effectiveness of this method.

[0075] 4. Target direction estimation method based on the weighted instantaneous phase difference of acoustic energy flow in frequency domain

[0076] The energy fluctuation of the noise signal received by the vector hydrophone is relatively large and the distribution is uneven and random, which seriously interferes with the accuracy of target azimuth estimation. According to the stability of the instantaneous phase difference of the line spectrum, the line spectrum of each component of the frequency domain acoustic energy streamline of the vector hydrophone is enhanced, and the interference noise and continuous spectrum are suppressed at the same time, so that high-precision DOA estimation of underwater targets can be achieved.

[0077] The steps of the method of the present invention are described in detail below:

[0078] Step 1: Figure 2As shown in the figure, taking the sound pressure and vibration velocity signals received by the two-dimensional vector hydrophone as an example, the received signal is first filtered and amplified to extract its 15Hz~200Hz low-frequency information, and then sampled through the A / D circuit with a sampling rate of f s , with sampling rate f s The sound pressure and velocity information collected by the vector hydrophone is sampled to obtain a discrete signal p(n'T s )、vx(n'T s )、vy(n'T s ), where T s =1 / f s , n'=1,2,…,N', N' is the data length;

[0079] p(n'T s )、vx(n'T s )、vy(n'T s ) to perform FFT analysis, and perform complex conjugate calculation of sound pressure and vibration velocity according to formula (10):

[0080]

[0081] In formula (10), 1≤k≤K, K is the number of frequency units obtained by each FFT analysis; ω s =2π / KT s is the frequency sampling interval in FFT analysis; P(kω s )、Vx(kω s )、Vy(kω s ) are the results obtained by Fourier transform of discrete signals of sound pressure and vibration velocity.

[0082] Step 2: Update the received signal. Repeat step 1 M times to obtain M received signals. Take the X-axis and Y-axis components of the frequency domain acoustic energy flow as an example and calculate according to formula (11). Then, each frequency unit on the frequency domain acoustic energy flow component obtains M instantaneous phase values, which are recorded as

[0083]

[0084] The instantaneous phase difference of all frequency units of the X-axis and Y-axis components of the frequency domain acoustic energy flow is calculated according to formula (12), which is expressed as

[0085]

[0086] In formula (12), 1≤n≤M-1, 1≤k≤K.

[0087] Step 3: Calculate the variance of the instantaneous phase difference of all frequency units on each component of the frequency domain acoustic energy flow according to formula (13), which is expressed as

[0088]

[0089] In formula (13), It represents the average value of the instantaneous phase difference of all frequency units on each component of the frequency domain acoustic energy flow.

[0090] Step 4: Process the sound pressure and velocity information in the X-axis and Y-axis directions received by the two-dimensional vector hydrophone through steps 1 to 3 to obtain the variance of the instantaneous phase difference under the corresponding frequency domain acoustic energy flow, and then perform an average fitting on the variance of each component according to formula (14), and perform mean filtering on it to obtain

[0091]

[0092] Step 5: According to the calculation results of step 4, the variance weighted statistics of the instantaneous phase difference of the frequency unit under each component of the frequency domain acoustic energy flow are performed to obtain the final spectrum and signal line spectrum estimation value, as shown in formula (15):

[0093]

[0094] In the formula, 1≤m≤M, when the fitting value When it is smaller, the corresponding acoustic energy flow component has a larger value, and the line spectrum is enhanced, which is convenient for azimuth estimation.

[0095] Step 6: After the frequency domain acoustic energy flow is weighted by the instantaneous phase difference, the real part of each acoustic energy flow component is taken, and the azimuth of each frequency point is calculated using the inverse trigonometric function. The frequency point acoustic energy flow is statistically analyzed (specifically, as shown in formulas (5), (6), and (7)) to obtain the final target azimuth.

[0096] V. Performance Analysis

[0097] In order to verify the performance of the weighted processing of the instantaneous phase difference stability of acoustic energy flow in the frequency domain, the broadband noise obtained by exciting the FIR linear filter with Gaussian white noise is used as a continuous spectrum signal, and a line spectrum is superimposed on the target signal. The line spectrum intensity is 10 to 25 dB higher than the nearby continuous spectrum.

[0098] The root mean square error (RMSE) of the method of the present invention is compared with that of the conventional complex sound intensity filter method at a signal-to-noise ratio of -20 to 30 dB, and each spectrum level is independently statistically analyzed 10,000 times. The sampling length of each time is T=3 min, and the sampled data is divided into 36 segments, and FFT analysis is performed on each segment for 5 seconds. Among them, the frequency of the target line spectrum of each sampling is uniformly distributed within 15 Hz to 200 Hz, the horizontal incident angle is uniformly distributed within 0° to 360°, and it is in a background noise environment composed of Gaussian white noise and noise of different directions and intensities. The simulation results are shown in Figure 2. Figure 3 shown.

[0099] In order to verify the effectiveness of the present invention, relevant lake test data were processed. The data were collected and stored in the Fuchun River in October 2018. At that time, the water depth was about 20m and the river bottom was flat. The target ship and receiving equipment used in the test are shown in the figure below. Figure 4 As shown in the figure, the vector hydrophone connected to the buoy is deployed at a depth of about 18m. The target low-frequency sound source moves about 1km behind the buoy. The sound source is 5m away from the water surface, with a frequency of 62Hz and an intensity about 10dB higher than that of the nearby waters. The sampling rate of the acquisition equipment connected to the hydrophone is 512Hz. The acquisition length is about 3min. The acquired data is divided into 36 segments for FFT analysis. Then, the final LOFAR map and time-azimuth history map are obtained by conventional methods and instantaneous phase difference weighting methods. The LOFAR comparison diagram is shown in Figure 5(a) and Figure 5(b), and the time-azimuth comparison diagram is shown in Figure 6(a) and Figure 6(b).

[0100] As shown in Figure 5(a), the line spectrum level obtained by the conventional complex sound intensity method is not much different from the background noise level. Due to the interference of the background noise energy fluctuation, the target acoustic energy flow in some time periods in Figure 6(a) is covered by noise, resulting in a large direction finding deviation and making it difficult to identify the target. Comparing Figure 5(a) with Figure 5(b), the direction finding algorithm based on the weighted instantaneous phase difference of the frequency domain acoustic energy flow can effectively detect the 62Hz line spectrum frequency unit signal, and the enhanced line spectrum intensity is much higher than the background noise. At the same time, by comparing Figure 6(a) with Figure 6(b), it can be seen that in each time period, the suppression effect of the method of the present invention on the 200° azimuth interference noise is better than the conventional complex sound intensity method in each time period. As shown in Figure 6(b), the time azimuth history diagram obtained by the method of the present invention is clearer, and the target azimuth can be clearly observed.

[0101] The present invention discloses a method for estimating the DOA of underwater targets based on the weighted instantaneous phase difference of frequency domain acoustic energy flow. The method of the present invention utilizes the characteristics that the line spectrum intensity of underwater targets is 10 to 25 dB higher than that of nearby continuous spectra, and that the instantaneous phase difference corresponding to the frequency unit is relatively stable, while the instantaneous phase difference corresponding to the background noise frequency unit changes randomly. The variance of the instantaneous phase difference of the frequency unit of each frequency domain acoustic energy flow component is weighted to further suppress the energy interference of the background noise, enhance the line spectrum signal-to-noise ratio gain, and achieve high-precision azimuth estimation of underwater targets. In the above manner, the present invention utilizes the weighted processing principle of the stability of the line spectrum instantaneous phase difference to enhance the line spectrum energy in the frequency domain acoustic energy flow component of the vector hydrophone, and further improves the accuracy of target azimuth estimation by combining the complex sound intensity. The simulation analysis and experimental data processing results prove the effectiveness and good engineering practicality of the processing method of the present invention.

[0102] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. A person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the scope of the present application should be based on the protection scope of the claims.

Claims

1. Underwater target DOA estimation method based on frequency domain acoustic energy flow instantaneous phase difference weighting, characterized by The following steps are involved: Step 1: extract low-frequency information from the sound pressure information and two-dimensional vector velocity information collected by the vector hydrophone according to the channel, and perform discretization processing; The obtained three-channel discrete signals are analyzed by FFT respectively, and then the sound pressure channel is conjugated with the two-dimensional vector velocity channel to extract the instantaneous phase of the frequency domain acoustic energy flow. Step 2, repeat step 1 M times, update to obtain M received signals, and calculate the instantaneous phase difference of each frequency unit on the X-axis and Y-axis components of the frequency domain acoustic energy flow according to the signal update sequence; Step 3: According to the calculation result of step 2, the variance of the instantaneous phase difference values ​​of all frequency units is calculated; Step 4, performing average fitting and mean filtering on the variance of the instantaneous phase difference between the X-axis and Y-axis acoustic energy flow components; Step 5, taking the inverse of the result calculated in step 4 as the weight, weighting the variance of the instantaneous phase difference of all frequency units under each component of the frequency domain acoustic energy flow, and obtaining the frequency domain acoustic energy flow; Step 6: extract the real part of the frequency domain acoustic energy flow in step 5, calculate the acoustic energy flow at each frequency point and perform statistics to obtain the target direction; In step 1, the received signal is filtered and amplified to extract the low-frequency information of 15Hz to 200Hz. s The sound pressure and velocity information collected by the vector hydrophone is sampled to obtain a discrete signal p(n'T s )、vx(n'T s )、vy(n'T s ), where T s =1 / f s , n'=1,2,…,N', N' is the data length; p(n'T s )、vx(n'T s )、vy(n'T s ) to perform FFT analysis, and perform complex conjugate calculation of sound pressure and vibration velocity according to formula (10): In formula (10), 1≤k≤K, K is the number of frequency units obtained by each FFT analysis; ω s =2π / KT s is the frequency sampling interval in FFT analysis; P(kω s )、Vx(kω s )、Vy(kω s ) are the results obtained by Fourier transform of discrete signals of sound pressure and vibration velocity; In step 2, the received signal is updated, and step 1 is repeated M times. After M received signals are obtained, the X-axis and Y-axis components of the frequency domain acoustic energy flow are calculated according to formula (11). Then, each frequency unit on the frequency domain acoustic energy flow component obtains M instantaneous phase values, which are recorded as The instantaneous phase difference of all frequency units of the X-axis and Y-axis components of the frequency domain acoustic energy flow is calculated according to formula (12), which is expressed as In formula (12), 1≤n≤M-1, 1≤k≤K; In step 3, the variance of the instantaneous phase difference of all frequency units on each component of the frequency domain acoustic energy flow is calculated according to formula (13), which is recorded as In formula (13), It represents the average value of the instantaneous phase difference of all frequency units on each component of the frequency domain acoustic energy flow; In step 4, the variance of each component is averaged according to formula (14), and then averaged and filtered to obtain In step 5, the variance weighted statistics of the instantaneous phase difference of the frequency unit under each component of the frequency domain acoustic energy flow are performed to obtain the estimated values ​​of the spectrum and signal line spectrum, as shown in formula (15): In the formula, 1≤m≤M.

2. The underwater target DOA estimation method based on the weighted instantaneous phase difference of frequency domain acoustic energy flow as claimed in claim 1, characterized in that: In step 1, the extracted low-frequency information has a frequency of 15 Hz to 200 Hz.

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