Signal processing method of anti-interference acoustic phased array Doppler log

Abstract

The invention discloses an anti-interference acoustic phased array Doppler log signal processing method. The method comprises the following steps: constructing a standard echo signal matrix; dividing the constructed standard echo signal matrix into K non-overlapping frames; constructing a beam output sequential sequence; performing short-time Fourier transform on the beam output sequential sequence to obtain a time-frequency spectrum matrix; extracting J frequency shift parameters from the time-frequency spectrum matrix; clustering G frequency shift parameter clusters; constructing a speed observation vector for carrier speed estimation; inputting the speed observation vector into a pre-trained time sequence denoising network, and outputting an anti-interference speed sequence; performing time integration on the anti-interference speed sequence, and constructing an accumulated voyage of the carrier in a measurement time period; according to the method, the accumulated voyage obtained through time integration is not hopped or interrupted due to individual abnormal frequency shift any more, so that reliable voyage information can be continuously output in a complex underwater acoustic environment.

Description

Technical Field

[0001] This invention relates to the field of Doppler log echo signal processing, specifically to a signal processing method for an anti-interference acoustic phased array Doppler log. Background Technology

[0002] Currently, underwater acoustic Doppler logs generally employ phased arrays to receive echo signals from seabed or water body scatterers, and calculate the radial velocity of the carrier relative to the environment through beamforming and Doppler frequency shift analysis. A typical implementation involves: using a fixed delay to sum and form a receiving beam in a preset direction; performing a short-time Fourier transform on the beam output to obtain the time spectrum; finally, determining the Doppler frequency shift by detecting energy peaks in the spectrum; converting this to velocity using the Doppler formula; and finally, obtaining the cumulative range through time integration.

[0003] However, in typical marine environments, echo signals are often severely affected by multiple interference sources: strong reverberation leads to spectral broadening, nearby scatterers induce Doppler frequency shift aliasing, and platform vibrations introduce non-motion-related spurious frequency shifts. Traditional Doppler logs typically employ a fixed pointing beam and a single peak detection strategy, making it difficult to distinguish between target echoes and interference components. When the interference energy approaches or exceeds the target signal, frequency shift estimation is prone to jumps or even complete failure, leading to abrupt changes, drifts, or interruptions in the integrated range, severely limiting the reliability of long-duration underwater navigation. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a signal processing method for an anti-interference acoustic phased array Doppler log, which solves the technical problems mentioned in the background by constructing a cumulative range that can be integrated over time.

[0005] To achieve the above objectives, the present invention provides the following technical solution: The signal processing method for an anti-interference acoustic phased array Doppler log includes the following steps: S1. Construct the standard echo signal matrix of the Doppler log in the desired beam pointing direction; S2. Divide the standard echo signal matrix into K non-overlapping frames along the sampling time direction; S3. Construct the beam output sub-sequences corresponding to each of the K non-overlapping frames, and splice them into a complete beam output timing sequence; S4. Perform a short-time Fourier transform on the beam output time sequence to obtain the time-spectrum matrix; S5. Search for local energy maxima along the frequency axis of the time-frequency matrix and extract J frequency shift parameters; S6. Perform density clustering on the J frequency shift parameters to generate G frequency shift parameter clusters; S7. Based on the G frequency shift parameters, cluster them to construct a velocity observation vector for carrier velocity estimation; S8. Input the velocity observation vector into a pre-trained temporal denoising network and output an anti-interference velocity sequence; S9. Perform time integration on the anti-interference velocity sequence to construct the cumulative range of the carrier during the measurement period.

[0006] In some specific embodiments, constructing a standard echo signal matrix of the Doppler log in the desired beam pointing direction includes: S1-1. An acoustic phased array based on N hydrophones is used to synchronously acquire echo signals at M sampling times. S1-2. Arrange the echo signals collected by each hydrophone at M sampling times in chronological order to construct N original echo signal sequences; S1-3. Align the N original echo signal sequences according to the sampling time to form an original echo signal matrix; S1-4. Obtain the relative time delay offset between the signals of each channel in the original echo signal matrix; S1-5. Based on the time delay offset, perform time delay compensation on the signals of each channel in the original echo signal matrix to construct a standard echo signal matrix. In some specific embodiments, the splicing into a complete beamout timing sequence includes: S3-1. Calculate the covariance matrix of each non-overlapping frame; S3-2. Calculate the corresponding beam weight vector based on the covariance matrix of each non-overlapping frame; S3-3. Apply each beam weight vector to the corresponding non-overlapping frame to generate K beam output subsequences; S3-4. Based on the sampling time sequence, the K beam output sub-sequences are spliced ​​together to generate the beam output time sequence.

[0007] In some specific embodiments, the corresponding beam weight vector is calculated based on the covariance matrix of each non-overlapping frame, including: S3-2-1. Construct the corresponding steering vector according to the preset desired beam pointing direction; S3-2-2. Based on the covariance matrix and its inverse matrix, and combined with the steering vector, calculate the MVDR beam weight vector corresponding to the kth non-overlapping frame, until K MVDR beam weight vectors are obtained. S3-2-3. Normalize the K MVDR beam weight vectors to generate the beam weight vectors corresponding to each non-overlapping frame.

[0008] In some specific embodiments, local energy maxima are searched along the frequency axis of the time-spectrum matrix to extract J frequency shift parameters, including: S5-1. For each time frame in the time spectrum matrix, calculate its energy value at each frequency point; S5-2. Within each time frame, traverse all frequency points and identify the frequency points that satisfy the local maximum condition. S5-3. Use the frequency values ​​corresponding to all frequency points that satisfy the local maximum condition as candidate frequency shift parameters; S5-4. Summarize the candidate frequency shift parameters for all time frames and construct a set containing J frequency shift parameters.

[0009] In some specific embodiments, density clustering is performed on J frequency shift parameters to generate G frequency shift parameter clusters, including: S6-1. Mark the access status of J frequency shift parameters as unaccessed, and predefine the neighborhood radius and minimum number of neighborhood points; S6-2. For the J frequency shift parameters marked as unaccessed, determine the core frequency shift parameters and construct the core frequency shift parameter set; S6-3. Traverse each core frequency shift parameter in the core frequency shift parameter set; S6-4. If the access status of the core frequency shift parameter is not accessed, then create a new frequency shift parameter cluster. S6-5. Add the core frequency shift parameters to the newly created frequency shift parameter cluster and update its access status to "accessed". S6-6. Recursively search for all frequency shift parameters that are reachable from the core frequency shift parameter density, add all density-reachable frequency shift parameters to the newly created frequency shift parameter cluster, and update their access status to "accessed". Density reachability is defined as follows: if a frequency shift parameter can be connected to the core frequency shift parameter through a series of adjacent frequency shift parameters, and the frequency deviation between any two adjacent frequency shift parameters in the path does not exceed the neighborhood radius, then the frequency shift parameter is considered to be density reachable.

[0010] S6-7. After all core frequency shift parameters have been processed, a total of G frequency shift parameter clusters are output.

[0011] In some specific embodiments, core frequency shift parameter determination is performed on J frequency shift parameters marked as unaccessed to construct a core frequency shift parameter set, including: S6-2-1. Among the J frequency shift parameters, select any frequency shift parameter whose access status is unaccessed as the target frequency shift parameter; S6-2-2 Calculate the frequency deviation between the target frequency shift parameter and the remaining J−1 frequency shift parameters; S6-2-3. If the frequency deviation is not greater than the neighborhood radius, then the frequency shift parameter corresponding to the frequency deviation is marked as a neighborhood point; otherwise, it is marked as a non-neighborhood point. S6-2-4. Traverse J-1 frequency deviations until all neighborhood points of the target frequency shift parameter are marked; S6-2-5, Count the number of neighborhood points of the target frequency shift parameter; S6-2-6. If the number of neighborhood points is not less than the minimum number of neighborhood points, then the target frequency shift parameter is marked as the core frequency shift parameter. S6-2-7. Traverse the J frequency shift parameters until all core frequency shift parameters are marked, and obtain the core frequency shift parameter set.

[0012] In some specific embodiments, a velocity observation vector for carrier velocity estimation is constructed based on G frequency shift parameter clusters, including: S7-1. Calculate the center frequency of each cluster, which serves as the cluster center frequency shift parameter for the G candidate frequency shift parameters; S7-2. Calculate the effective radial velocity corresponding to each cluster center frequency shift parameter based on the cluster center frequency shift parameter; S7-3. The G effective radial velocities are spliced ​​together in time order to form a velocity observation vector.

[0013] This invention provides a signal processing method for an anti-interference acoustic phased array Doppler log, which has the following beneficial effects: This invention employs adaptive directional filtering based on array-received signals, effectively suppressing reverberation and interference from non-target directions. Subsequently, by performing neighborhood density analysis on Doppler frequency shift candidate values, only stable components with similar frequencies at multiple time points are retained, while randomly occurring spurious frequency shifts are eliminated. Based on this, the resulting velocity sequence is fed into a pre-trained temporal neural network to further smooth instantaneous fluctuations caused by residual interference or measurement noise. Ultimately, the cumulative range obtained through time integration no longer jumps or is interrupted due to individual abnormal frequency shifts. Even in the presence of strong reverberation, multi-scatterer echoes, or carrier vibration, it maintains continuous and usable displacement output, thus enabling the continuous output of reliable range information in complex underwater acoustic environments. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating the signal processing method for the anti-interference acoustic phased array Doppler log of the present invention. Figure 2 This is a schematic diagram illustrating the construction process of the standard echo signal described in this invention; Figure 3 This is a schematic diagram of the beam output timing sequence generation process described in this invention; Figure 4This is a schematic diagram of the output process of the frequency shift parameter clustering cluster described in this invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Example 1: Please refer to Figure 1 This invention provides a signal processing method for an anti-interference acoustic phased array Doppler log, comprising the following steps: S1. Construct the standard echo signal matrix of the Doppler log in the desired beam pointing direction; S2. Divide the standard echo signal matrix into K non-overlapping frames along the sampling time direction; Each non-overlapping frame contains L consecutive sampling points, and each sampling point represents the complex baseband echo sampling value of N hydrophone channels at a discrete sampling time.

[0017] S3. Construct the beam output sub-sequences corresponding to each of the K non-overlapping frames, and splice them into a complete beam output timing sequence; Specifically, the beam output represents the target Doppler echo signal enhanced in the desired direction after spatial filtering, and its time sequence characterizes the Doppler modulation process caused by the radial motion of the carrier relative to the seabed or water scatterer.

[0018] S4. Perform a short-time Fourier transform on the beam output time sequence to obtain the time-spectrum matrix; Specifically, the short-time Fourier transform represents a processing method that segments a time-domain signal for Fourier analysis. It obtains a time-spectrum matrix that characterizes the evolution of Doppler frequency shift over time by windowing and framing the beam output time sequence and calculating the spectrum frame by frame.

[0019] S5. Search for local energy maxima along the frequency axis of the time-frequency matrix and extract J frequency shift parameters; S6. Perform density clustering on the J frequency shift parameters to generate G frequency shift parameter clusters; S7. Based on the G frequency shift parameters, cluster them to construct a velocity observation vector for carrier velocity estimation; S8. Input the velocity observation vector into a pre-trained temporal denoising network and output an anti-interference velocity sequence; Specifically, in this embodiment, the temporal denoising network is preferably a sequence denoising model based on a recurrent neural network; its specific network architecture can adopt a bidirectional long short-term memory network (BiLSTM). The pre-training step can adopt the general steps of supervised training models: in a controlled water tank or simulation environment, multiple sets of velocity observation vectors containing interference and their corresponding interference-free reference velocity sequences are collected as training samples; using the velocity observation vectors as input and the interference-free reference velocity sequences as labels, the weight parameters of the BiLSTM network are optimized by minimizing the mean squared error loss function.

[0020] S9. Perform time integration on the anti-interference velocity sequence to construct the cumulative range of the carrier during the measurement period.

[0021] Furthermore, the anti-interference velocity sequence is represented by time integration by multiplying each velocity value in the velocity sequence by its corresponding time sampling interval, and summing all the multiplications to obtain the cumulative range.

[0022] Cumulative voyage represents the relative displacement of the underwater vehicle when the inertial navigation system is interrupted or requires autonomous correction, relying on the Doppler log.

[0023] In this embodiment, channel phase alignment is achieved by constructing a standard echo signal matrix, providing spatial coherent input for beamforming. Based on this, frame-by-frame beamforming effectively enhances the echo in the target direction and suppresses spatial interference. Then, Doppler frequency shift is extracted through time-frequency analysis, and isolated spurious frequency shift points are eliminated using density clustering. Subsequently, the clustering results are converted into velocity observation vectors, and time-domain jitter is suppressed by a time-series denoising network. Finally, the cumulative range after anti-interference processing is output.

[0024] Example 2: See Figures 2 to 4 The technical solution of this embodiment 2 differs from that of embodiment 1 in that it discloses the specific application steps of each step in embodiment 1.

[0025] Specifically, in this embodiment, the construction step of step S1 is as follows: S1-1. An acoustic phased array based on N hydrophones is used to synchronously acquire echo signals at M sampling times. The echo signal is a complex baseband signal collected by a hydrophone and a Doppler echo signal formed by reflection from the seabed or water body scattering body.

[0026] S1-2. Arrange the echo signals collected by each hydrophone at M sampling times in chronological order to construct N original echo signal sequences; The original echo signal sequence corresponds to a hydrophone channel with a time length of M, which represents the Doppler echo timing received by the channel at continuous sampling times.

[0027] S1-3. Align the N original echo signal sequences according to the sampling time to form an original echo signal matrix; Specifically, each row of the raw echo signal matrix represents the raw echo signal sequence of a hydrophone channel, and each column represents a snapshot of all hydrophones at the same sampling time.

[0028] S1-4. Obtain the relative time delay offset between the signals of each channel in the original echo signal matrix; The relative time delay offset is calculated using the spatial coordinates of the hydrophone and the desired beam pointing direction; for example, the specific calculation process is as follows: ① Take one of the hydrophones as the reference hydrophone and set its spatial position as the origin of the coordinate system; the spatial position of each of the other hydrophones is determined by its horizontal coordinate, vertical coordinate and depth coordinate in three-dimensional space; ② Based on the preset desired beam pointing direction, determine the elevation angle and azimuth angle of that direction; where the elevation angle represents the angle between the beam direction and the vertical downward direction, and the azimuth angle represents the pointing angle of the beam in the horizontal plane; ③ For each non-reference hydrophone, perform trigonometric function operations on its x-coordinate, y-coordinate, and depth coordinates with the elevation and azimuth angles of the desired beam pointing direction to calculate the sound path difference of the hydrophone relative to the reference hydrophone. ④ Divide the obtained sound path difference by the speed of sound in water to obtain the relative time delay offset of the hydrophone channel relative to the reference channel.

[0029] S1-5. Based on the time delay offset, perform time delay compensation on the signals of each channel in the original echo signal matrix to construct a standard echo signal matrix. Specifically, time delay compensation involves interpolating and resampling the original echo signal sequences of each channel to achieve phase alignment in the desired beam pointing direction, thereby constructing a spatially coherent standard echo signal matrix.

[0030] In this embodiment, the constructed standard echo signal matrix enables phase alignment of the echo signals of each hydrophone channel in the desired beam pointing direction, effectively eliminating the time delay differences between channels caused by array geometry and beam pointing, thereby forming a spatially coherent multi-channel standard echo signal matrix.

[0031] Specifically, in this embodiment, the generation step of step S3 is as follows: S3-1. Calculate the covariance matrix of each non-overlapping frame; Specifically, the expression for the covariance matrix is: ; in, This represents the spatiotemporal covariance matrix corresponding to the k-th non-overlapping frame. Let H represent the standard echo signal submatrix of the k-th non-overlapping frame. Its dimension is N×L, where N is the number of hydrophone channels, L is the number of sampling points in each frame, and H represents the conjugate transpose operation of the standard echo signal submatrix.

[0032] Furthermore, constructing the covariance between each hydrophone channel into a covariance matrix can completely preserve the spatial structure information of the signal, making it easier to enhance the target echo signal in the desired direction through adaptive beamforming while suppressing interference.

[0033] S3-2. Calculate the corresponding beam weight vector based on the covariance matrix of each non-overlapping frame; S3-3. Apply each beam weight vector to the corresponding non-overlapping frame to generate K beam output subsequences; Specifically, the k-th beam weight vector is conjugate weighted and summed with the standard echo signal submatrix of the k-th non-overlapping frame to obtain the single-channel beam output timing sequence corresponding to that frame, i.e., the beam output subsequence.

[0034] S3-4. Based on the sampling time sequence, the K beam output sub-sequences are spliced ​​together to generate the beam output time sequence.

[0035] In this embodiment, the constructed beam output timing sequence spatially filters and enhances the target Doppler echo in the desired beam pointing direction, while suppressing interference from non-desired directions.

[0036] Furthermore, the steps for calculating the corresponding beam weight vector include: S3-2-1. Construct the corresponding steering vector according to the preset desired beam pointing direction; Specifically, the steering vector is an N-dimensional complex vector, the nth element of which is determined by the phase delay of the nth hydrophone relative to the reference hydrophone in the desired beam pointing direction.

[0037] S3-2-2. Based on the covariance matrix and its inverse matrix, and combined with the steering vector, calculate the MVDR beam weight vector corresponding to the kth non-overlapping frame, until K MVDR beam weight vectors are obtained. Specifically, the formula for calculating the MVDR beam weight vector is: ; in, This represents the beam weight vector of the k-th frame. Denotes the inverse of the covariance matrix. This represents the guide vector.

[0038] It should be noted that the MVDR beam weight vector is a complex vector calculated according to the formula based on the covariance matrix and its inverse matrix of the k-th non-overlapping frame and the steering vector, which satisfies that the response gain in the desired beam pointing direction is a unit value.

[0039] S3-2-3. Normalize the K MVDR beam weight vectors to generate the beam weight vectors corresponding to each non-overlapping frame.

[0040] In this embodiment, the calculated beam weight vector is based on the covariance statistical characteristics of each non-overlapping frame and the desired beam pointing direction, and is used to perform spatial weighted synthesis of each non-overlapping frame.

[0041] Specifically, in this embodiment, step S5 is used to extract J frequency shift parameters, and the extraction steps include: S5-1. For each time frame in the time spectrum matrix, calculate its energy value at each frequency point; Specifically, the energy value is the square of the modulus of the complex value at that frequency point; S5-2. Within each time frame, traverse all frequency points and identify the frequency points that satisfy the local maximum condition. The local maximum condition is that the energy value of the frequency point is greater than the energy value of its adjacent frequency points. S5-3. Use the frequency values ​​corresponding to all frequency points that satisfy the local maximum condition as candidate frequency shift parameters; S5-4. Summarize the candidate frequency shift parameters for all time frames and construct a set containing J frequency shift parameters.

[0042] In this embodiment, by performing energy calculation and local maximum detection on each frame of the time spectrum, J frequency shift parameters are extracted as a candidate set to characterize the carrier Doppler frequency shift.

[0043] Specifically, in this embodiment, step S6 is used to generate G frequency shift parameter clusters, including: S6-1. Mark the access status of J frequency shift parameters as unaccessed, and predefine the neighborhood radius and minimum number of neighborhood points; S6-2. For the J frequency shift parameters marked as unaccessed, determine the core frequency shift parameters and construct the core frequency shift parameter set; S6-3. Traverse each core frequency shift parameter in the core frequency shift parameter set; S6-4. If the access status of the core frequency shift parameter is not accessed, then create a new frequency shift parameter cluster. S6-5. Add the core frequency shift parameters to the newly created frequency shift parameter cluster and update its access status to "accessed". S6-6. Recursively search for all frequency shift parameters that are reachable from the core frequency shift parameter density, add all density-reachable frequency shift parameters to the newly created frequency shift parameter cluster, and update their access status to "accessed". Density reachability is defined as follows: if a frequency shift parameter can be connected to the core frequency shift parameter through a series of adjacent frequency shift parameters, and the frequency deviation between any two adjacent frequency shift parameters in the path does not exceed the neighborhood radius, then the frequency shift parameter is considered to be density reachable.

[0044] S6-7. After all core frequency shift parameters have been processed, a total of G frequency shift parameter clusters are output.

[0045] In this embodiment, density clustering is used to group points in the frequency shift parameters that are adjacent in the frequency dimension and repeat in multiple time frames into the same cluster, thereby separating out a continuous set of effective frequency shifts and eliminating isolated false frequency shifts.

[0046] Furthermore, step S6-2 is used to construct the core frequency shift parameter set, including: S6-2-1. Among the J frequency shift parameters, select any frequency shift parameter whose access status is unaccessed as the target frequency shift parameter; S6-2-2 Calculate the frequency deviation between the target frequency shift parameter and the remaining J−1 frequency shift parameters; S6-2-3. If the frequency deviation is not greater than the neighborhood radius, then the frequency shift parameter corresponding to the frequency deviation is marked as a neighborhood point; otherwise, it is marked as a non-neighborhood point. S6-2-4. Traverse J-1 frequency deviations until all neighborhood points of the target frequency shift parameter are marked; S6-2-5, Count the number of neighborhood points of the target frequency shift parameter; S6-2-6. If the number of neighborhood points is not less than the minimum number of neighborhood points, then the target frequency shift parameter is marked as the core frequency shift parameter. S6-2-7. Traverse the J frequency shift parameters until all core frequency shift parameters are marked, and obtain the core frequency shift parameter set.

[0047] In this embodiment, the constructed core frequency shift parameter set consists of frequency shift parameters that are densely distributed in the local frequency neighborhood, and is used to identify potential effective Doppler frequency shift regions.

[0048] Specifically, in this embodiment, step S7 further includes: S7-1. Calculate the center frequency of each cluster, which serves as the cluster center frequency shift parameter for the G candidate frequency shift parameters; Specifically, the center frequency represents the arithmetic mean of all frequency shift parameters within the cluster.

[0049] S7-2. Calculate the effective radial velocity corresponding to each cluster center frequency shift parameter based on the cluster center frequency shift parameter; The formula for calculating the radial velocity is: ; in, Indicates the effective radial velocity. This represents the speed of sound in water. This represents the cluster center frequency shift parameter. This indicates the frequency of the Doppler log's transmitted signal. This indicates the angle between the desired beam pointing direction and the vertical direction.

[0050] S7-3. Concatenate the G effective radial velocities in time order to form a velocity observation vector; Furthermore, the velocity observation vector represents the sequence of radial motion velocities of the carrier relative to the seabed or water scatterer at continuous measurement times.

[0051] In this embodiment, by converting the frequency-shift clusters purified by density clustering into radial velocities that conform to the physical laws of Doppler, and organizing them into velocity observation vectors according to the time series, a radial velocity sequence suitable for time-series denoising can be finally formed.

[0052] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means.

[0053] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0054] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0055] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A signal processing method for an anti-interference acoustic phased array Doppler log, characterized in that, include: S1. Construct the standard echo signal matrix of the Doppler log in the desired beam pointing direction; S2. Divide the standard echo signal matrix into K non-overlapping frames along the sampling time direction; S3. Construct the beam output sub-sequences corresponding to each of the K non-overlapping frames, and splice them into a complete beam output timing sequence; S4. Perform a short-time Fourier transform on the beam output time sequence to obtain the time-spectrum matrix; S5. Search for local energy maxima along the frequency axis of the time-frequency matrix and extract J frequency shift parameters; S6. Perform density clustering on the J frequency shift parameters to generate G frequency shift parameter clusters; S7. Based on the G frequency shift parameters, cluster them to construct a velocity observation vector for carrier velocity estimation; S8. Input the velocity observation vector into a pre-trained temporal denoising network and output an anti-interference velocity sequence; S9. Perform time integration on the anti-interference velocity sequence to construct the cumulative range of the carrier during the measurement period.

2. The signal processing method for an anti-interference acoustic phased array Doppler log as described in claim 1, characterized in that, Constructing the standard echo signal matrix of the Doppler log in the desired beam pointing direction includes: S1-1. An acoustic phased array based on N hydrophones is used to synchronously acquire echo signals at M sampling times. S1-2. Arrange the echo signals collected by each hydrophone at M sampling times in chronological order to construct N original echo signal sequences; S1-3. Align the N original echo signal sequences according to the sampling time to form an original echo signal matrix; S1-4. Obtain the relative time delay offset between the signals of each channel in the original echo signal matrix; S1-5. Based on the time delay offset, perform time delay compensation on the signals of each channel in the original echo signal matrix to construct a standard echo signal matrix.

3. The signal processing method for an anti-interference acoustic phased array Doppler log as described in claim 1, characterized in that, The splicing is a complete beamout timing sequence, including: S3-1. Calculate the covariance matrix of each non-overlapping frame; S3-2. Calculate the corresponding beam weight vector based on the covariance matrix of each non-overlapping frame; S3-3. Apply each beam weight vector to the corresponding non-overlapping frame to generate K beam output subsequences; S3-4. Based on the sampling time sequence, the K beam output sub-sequences are spliced ​​together to generate the beam output time sequence.

4. The signal processing method for an anti-interference acoustic phased array Doppler log as described in claim 3, characterized in that, Based on the covariance matrix of each non-overlapping frame, the corresponding beam weight vector is calculated, including: S3-2-1. Construct the corresponding steering vector according to the preset desired beam pointing direction; S3-2-2. Based on the covariance matrix and its inverse matrix, and combined with the steering vector, calculate the MVDR beam weight vector corresponding to the kth non-overlapping frame, until K MVDR beam weight vectors are obtained. S3-2-3. Normalize the K MVDR beam weight vectors to generate the beam weight vectors corresponding to each non-overlapping frame.

5. The signal processing method for an anti-interference acoustic phased array Doppler log as described in claim 4, characterized in that, Search for local energy maxima along the frequency axis of the time-spectrum matrix to extract J frequency shift parameters, including: S5-1. For each time frame in the time spectrum matrix, calculate its energy value at each frequency point; S5-2. Within each time frame, traverse all frequency points and identify the frequency points that satisfy the local maximum condition. S5-3. Use the frequency values ​​corresponding to all frequency points that satisfy the local maximum condition as candidate frequency shift parameters; S5-4. Summarize the candidate frequency shift parameters for all time frames and construct a set containing J frequency shift parameters.

6. The signal processing method for an anti-interference acoustic phased array Doppler log as described in claim 5, characterized in that, Density clustering is performed on the J frequency shift parameters to generate G frequency shift parameter clusters, including: S6-1. Mark the access status of J frequency shift parameters as unaccessed, and predefine the neighborhood radius and minimum number of neighborhood points; S6-2. For the J frequency shift parameters marked as unaccessed, determine the core frequency shift parameters and construct the core frequency shift parameter set; S6-3. Traverse each core frequency shift parameter in the core frequency shift parameter set; S6-4. If the access status of the core frequency shift parameter is not accessed, then create a new frequency shift parameter cluster. S6-5. Add the core frequency shift parameters to the newly created frequency shift parameter cluster and update its access status to "accessed". S6-6. Recursively search for all frequency shift parameters that are reachable from the core frequency shift parameter density, add all density-reachable frequency shift parameters to the newly created frequency shift parameter cluster, and update their access status to "accessed". Density reachability is defined as follows: if a certain frequency shift parameter can be connected to the core frequency shift parameter through a series of adjacent frequency shift parameters, and the frequency deviation between any two adjacent frequency shift parameters in the path does not exceed the neighborhood radius, then the frequency shift parameter is considered to be density reachable. S6-7. After all core frequency shift parameters have been processed, a total of G frequency shift parameter clusters are output.

7. The signal processing method for an anti-interference acoustic phased array Doppler log as described in claim 6, characterized in that, For J frequency shift parameters marked as unaccessed, perform core frequency shift parameter determination and construct a core frequency shift parameter set, including: S6-2-1. Among the J frequency shift parameters, select any frequency shift parameter whose access status is unaccessed as the target frequency shift parameter; S6-2-2 Calculate the frequency deviation between the target frequency shift parameter and the remaining J−1 frequency shift parameters; S6-2-3. If the frequency deviation is not greater than the neighborhood radius, then the frequency shift parameter corresponding to the frequency deviation is marked as a neighborhood point; otherwise, it is marked as a non-neighborhood point. S6-2-4. Traverse J−1 frequency deviations until all neighborhood points of the target frequency shift parameter are marked; S6-2-5, Count the number of neighborhood points of the target frequency shift parameter; S6-2-6. If the number of neighborhood points is not less than the minimum number of neighborhood points, then the target frequency shift parameter is marked as the core frequency shift parameter. S6-2-7. Traverse the J frequency shift parameters until all core frequency shift parameters are marked, and obtain the core frequency shift parameter set.

8. The signal processing method for an anti-interference acoustic phased array Doppler log as described in claim 1, characterized in that, Based on the G frequency shift parameters clusters, a velocity observation vector for carrier velocity estimation is constructed, including: S7-1. Calculate the center frequency of each cluster, which serves as the cluster center frequency shift parameter for the G candidate frequency shift parameters; S7-2. Calculate the effective radial velocity corresponding to each cluster center frequency shift parameter based on the cluster center frequency shift parameter; S7-3. The G effective radial velocities are spliced ​​together in time order to form a velocity observation vector.

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