Underwater ultrasonic signal analysis method based on Doppler effect

Abstract

The invention provides an underwater ultrasonic signal analysis method based on the Doppler effect, and relates to the technical field of data processing, and the method comprises the steps: carrying out the segmentation processing of echo signal data according to a preset time window, carrying out the envelope demodulation and phase unwrapping operation of a signal in each time window, and carrying out the phase unwrapping operation of the signal in each time window; amplitude sequence, phase sequence and instantaneous frequency sequence data are extracted; extracting frequency change, amplitude change and phase change data, performing clustering processing, and distinguishing and marking different underwater target components; respectively calculating an amplitude stability index and a phase change synchronism index for each frequency track in all time windows, and screening a main frequency track according to the amplitude stability index and the phase change synchronism index; frequency shift values in the time windows are extracted, the speeds of the underwater targets in the time windows are calculated based on the Doppler effect, and the motion direction is judged in combination with frequency change data and phase change data; according to the invention, the autonomy and accuracy of underwater ultrasonic signal analysis are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, in particular to an underwater ultrasonic signal analysis method based on the Doppler effect. Background Art

[0002] Currently, existing technologies primarily use traditional Fourier transform methods to perform spectrum analysis on underwater ultrasonic signals. By analyzing the frequency offset of the received signal, the speed or distance of underwater targets can be estimated. This method, typically combined with underwater sonar equipment, first transmits an ultrasonic signal of a specific frequency, then receives the echo signal reflected by the underwater target. By calculating the frequency shift of the echo signal relative to the transmitted signal, the motion parameters of the underwater target can be inferred.

[0003] However, in practical applications, when underwater targets move at rapidly varying speeds or in multiple underwater target environments, traditional Fourier transform methods struggle to distinguish the Doppler shift components of different underwater targets. For example, when simultaneously monitoring multiple schools of fish in a marine ranch, multiple reflected signals may overlap, resulting in a decrease in frequency shift resolution. This can easily lead to misidentification or loss of tracking of underwater targets, compromising the accuracy and reliability of underwater monitoring. Summary of the Invention

[0004] The purpose of the present invention is to provide an underwater ultrasonic signal analysis method based on the Doppler effect, aiming to solve the problems mentioned in the background technology.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows: An underwater ultrasonic signal analysis method based on the Doppler effect, the method comprising: Acquire a transmitting frequency of an ultrasonic signal, transmit the ultrasonic signal to an underwater target, receive echo signals reflected by multiple underwater targets, and obtain echo signal data containing reflection information of the multiple underwater targets; The echo signal data is segmented according to the preset time window, and the envelope demodulation and phase unwrapping operations are performed on the signal in each time window to extract its amplitude sequence, phase sequence and instantaneous frequency sequence data to generate the corresponding time series signal feature data; Based on the time series signal feature data, frequency change, amplitude change and phase change data are extracted and clustered to distinguish and mark different underwater target components. The preliminary frequency shift signal sets of multiple underwater targets are obtained, where each preliminary frequency shift signal set contains one or more frequency tracks. Based on the preliminary frequency shift signal set, combined with the amplitude change and phase change data, the amplitude stability index and phase change synchronization index are calculated for each frequency trajectory in all time windows. The main frequency trajectory is then filtered based on this to form the final frequency shift signal set. According to the final frequency shift signal set, the frequency shift value in each time window is extracted, and the speed of each underwater target in each time window is calculated based on the Doppler effect. The direction of movement is determined by combining the frequency change data and phase change data, and the motion state analysis results of the underwater target are generated and output.

[0006] Preferably, the echo signal data is segmented according to a preset time window, envelope demodulation and phase unwrapping operations are performed on the signal in each time window, and its amplitude sequence, phase sequence and instantaneous frequency sequence data are extracted to generate corresponding time series signal feature data, including: Perform time axis framing on the echo signal data, dividing the echo signal into multiple frame segments according to a preset time window to obtain a segmented signal sequence; According to the segmented signal sequence, envelope demodulation processing is performed on the echo signal of each frame segment, and its amplitude change curve is extracted to form amplitude sequence data; According to the echo signal data, the phase unwrapping operation is performed to extract the continuous phase change curve and calculate the phase growth rate of each frame segment to obtain the phase sequence and instantaneous frequency sequence data; The amplitude sequence, phase sequence and instantaneous frequency sequence data are organized in time sequence to obtain time series signal feature data.

[0007] Preferably, frequency change, amplitude change and phase change data are extracted based on the time series signal characteristic data, clustering processing is performed, and different underwater target components are distinguished and marked to obtain a preliminary frequency shift signal set of multiple underwater targets, including: According to the time series signal characteristic data, the instantaneous frequency change, amplitude change and phase change data in each time window are extracted in sequence to form a frequency change sequence, an amplitude change sequence and a phase change sequence respectively; According to the frequency change sequence, the frequency changes of adjacent time windows are analyzed, and points with similar change trends are grouped into preliminary frequency change groups to obtain multiple frequency change candidate groups; For each frequency change candidate group, extract its amplitude change sequence, calculate its mean and standard deviation respectively, and screen out the frequency change candidate groups that meet the amplitude judgment criteria based on the conditions that the amplitude mean is higher than the preset valid signal judgment threshold and the standard deviation is lower than the preset amplitude stability judgment threshold; For each frequency change candidate group that meets the amplitude criterion, its phase change sequence is analyzed, and the correlation between the phase change trend and the frequency change trend within the group is calculated. The groups with correlation higher than the preset correlation threshold are determined as the preliminary frequency shift signal sets of the same underwater target. Each set contains one or more frequency trajectories.

[0008] Preferably, based on the preliminary frequency shift signal set, combined with the amplitude change and phase change data, the amplitude stability index and the phase change synchronization index are calculated for each frequency trajectory in all time windows, and the main frequency trajectory is screened accordingly to form the final frequency shift signal set, including: For each frequency trajectory, extract its amplitude change data in all time windows and calculate its standard deviation as the amplitude stability index; For each frequency trajectory, based on the frequency change data and phase change data in all time windows, the frequency change direction and phase change direction in each time window are compared to determine whether the signs are consistent. The proportion of windows with consistent signs in all time windows is counted to obtain the growth direction consistency score. For each frequency trajectory, the frequency change data and phase change data are first-order differencing, respectively, to obtain the frequency change rate sequence and phase change rate sequence. The two are then subjected to correlation analysis at the same window position. The proportion of windows with correlation higher than the preset synchronization judgment threshold is statistically calculated as the phase change synchronization indicator. For all frequency trajectories, combined with the amplitude stability index, growth direction consistency score and phase change synchronization index, a weighted scoring or threshold screening method is used to select the frequency trajectory with the highest comprehensive score as the main frequency trajectory, and all main frequency trajectories are collected to form the final frequency shift signal set.

[0009] Preferably, the frequency shift value in each time window is extracted based on the final frequency shift signal set, the speed of each underwater target in each time window is calculated based on the Doppler effect, and the direction of motion is determined by combining the frequency change data and the phase change data, and the motion state analysis result of the underwater target is generated and output, including: According to the main frequency trajectory, in accordance with the time window order, the instantaneous frequency value in each time window is extracted, and the instantaneous frequency value in each time window is differentiated from the transmitting frequency based on the transmitting frequency to obtain the frequency shift value of each time window; According to the frequency shift value in each time window of each main frequency trajectory, the Doppler effect calculation method is used to divide the frequency shift value by the transmission frequency and multiply it by the propagation speed of ultrasound in water to obtain the corresponding radial velocity sequence in each time window; For each main frequency trajectory, the frequency change data and phase change data in each time window are used to determine the signs of the frequency change direction and phase change direction in the same window. If the signs are consistent, the underwater target is determined to be close to the emission source. If the signs are opposite, the underwater target is determined to be far away from the emission source. This generates the motion direction criterion for each time window. The radial velocity sequence of each main frequency trajectory in all time windows is combined with the motion direction criterion to form the velocity and motion direction data of each underwater target in the entire time series; According to the speed and movement direction data of each underwater target in the entire time series, the motion state analysis results of the underwater target are generated and output.

[0010] Preferably, according to the segmented signal sequence, envelope demodulation processing is performed on the echo signal of each frame segment, and its amplitude change curve is extracted to form amplitude sequence data, including: For each frame signal in the segmented signal sequence, for each sampling point, the absolute value of the signal between it and multiple adjacent sampling points is counted, and the absolute value of the signal obtained by the statistics is averaged in the time direction to obtain the envelope amplitude corresponding to the sampling point; Arrange the envelope amplitudes of all sampling points in each frame segment in sequence according to the sampling time to form an amplitude change curve; According to the amplitude variation curve, the amplitude at each sampling moment is extracted point by point to form amplitude sequence data.

[0011] Preferably, a phase unwrapping operation is performed based on the echo signal data to extract a continuous phase change curve and calculate the phase growth rate of each frame segment to obtain phase sequence data and instantaneous frequency sequence data, including: According to the echo signal data, for each sampling point, the preliminary phase value of the sampling point is calculated using its sine and cosine components to obtain a set of preliminary phase sequence data; Perform continuity correction on the preliminary phase sequence data. When the phase difference between adjacent sampling points is found to be greater than half a cycle, eliminate the sudden change by adding or subtracting 2π, so that the phase change curve remains monotonically continuous in time, forming continuous phase sequence data. According to the continuous phase sequence data, the phase difference between each two adjacent sampling points is calculated, and the phase difference is divided by the time interval between the two sampling points to obtain the phase growth rate sequence of each sampling point; The phase growth rates of all sampling points are arranged in the order of sampling time to form instantaneous frequency sequence data.

[0012] Preferably, according to the frequency change sequence, the frequency changes in adjacent time windows are analyzed, and points with similar change trends are grouped into preliminary frequency change groups to obtain multiple frequency change candidate groups, including: According to the frequency change sequence, the frequency change in each two adjacent time windows is differentiated to obtain the frequency change increment corresponding to each time window; For the frequency change increments of all time windows, according to the principle of numerical proximity and consistent change trend, the frequency change increments are clustered using the similarity criterion to obtain multiple frequency change subsequences with similar change trends; The frequency change amounts of adjacent time windows in each frequency change subsequence are sequentially connected to form a preliminary frequency change grouping; A plurality of frequency change candidate groups are generated based on all preliminary frequency change groupings.

[0013] Preferably, for each frequency change candidate group that meets the amplitude criterion, its phase change sequence is analyzed, the correlation between the phase change trend and the frequency change trend within the group is calculated, and the group with a correlation higher than a preset correlation threshold is determined as a preliminary frequency shift signal set of the same underwater target, including: For each frequency change candidate group that meets the amplitude criterion, extract the phase change sequence within the corresponding time window to form the phase change data of the group; A first-order difference method is used to obtain a frequency change rate sequence and a phase change rate sequence of the frequency change candidate group respectively; Perform correlation analysis on the frequency change rate sequence and the phase change rate sequence in each corresponding time window, calculate the correlation coefficient, and obtain the correlation criterion of each group; For each frequency change candidate group, determine whether its correlation criterion is higher than the preset correlation threshold. The groups above the threshold are determined as the preliminary frequency shift signal sets of the same underwater target, and all preliminary frequency shift signal sets that meet the conditions are output.

[0014] Preferably, for each frequency trajectory, the frequency change data and phase change data are first-order differencing respectively to obtain a frequency change rate sequence and a phase change rate sequence, and a correlation analysis is performed on the two at the same window position, including: For each frequency trajectory, extract its frequency change data and phase change data in all time windows, and perform first-order difference processing on each time window to obtain the corresponding frequency change rate sequence and phase change rate sequence; According to the frequency change rate sequence and phase change rate sequence, the rate values ​​of the two are extracted at each time window position to construct a set of rate pairs in the same time window; For the rate pairs of all time windows, the correlation coefficient algorithm is used to perform correlation analysis to obtain the correlation score sequence of each time window.

[0015] The above solution of the present invention includes at least the following beneficial effects: First, this invention goes beyond the traditional Fourier transform's single spectral analysis perspective. Instead, by segmenting the echo signal into time windows and employing envelope demodulation and phase unwrapping, it can simultaneously extract the signal's amplitude, phase, and instantaneous frequency sequences within each time window. This multi-feature parallel extraction approach significantly expands the capture of the echo signal's physical properties, not only capturing target velocity but also depicting energy fluctuations and phase change trends during target motion in real time.

[0016] Secondly, this invention utilizes multidimensional feature data (frequency, amplitude, and phase variations) for clustering, effectively distinguishing and labeling the signal components of different underwater targets in complex, multi-target scenarios. Compared to existing techniques, which often suffer from overlapping signals and difficulty separating them accurately, this invention significantly improves the resolution of frequency-shifted components and target separation capabilities through multi-feature clustering. This reduces target misidentification and tracking loss, improving the underwater monitoring system's anti-interference capabilities and target detection accuracy.

[0017] Thirdly, when processing the initial frequency-shift signal set, the present invention not only considers the continuity of frequency changes, but also integrates the stability of amplitude changes and the synchronization of phase changes. This allows the system to automatically screen out real, physically significant main frequency trajectories, further eliminating abnormal trajectories caused by noise, clutter, or multipath effects. For example, in a marine ranch monitoring scenario with dense fish schools and intersecting movement paths, the system can automatically lock onto the main movement trajectory of the real fish school, effectively avoiding interference from clutter and false signals.

[0018] Finally, by calculating the velocity of the main frequency trajectory based on the Doppler effect and combining frequency and phase change data to determine the target's direction of motion, a continuous and complete motion state analysis result can be output for each underwater target. This not only enhances the dynamic tracking capability of multiple underwater targets, but also provides a solid data foundation for subsequent intelligent processing such as underwater target classification, behavior analysis, and path prediction.

[0019] In summary, the present invention can significantly improve the signal resolution capability and motion analysis accuracy in complex underwater environments with multiple targets, solves the problems of poor frequency shift resolution, high misrecognition rate, and tracking loss in existing technologies in scenarios with multiple targets, rapid motion, and noise interference, and has higher engineering application value and promotion prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flowchart of an underwater ultrasonic signal analysis method based on the Doppler effect provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0022] like Figure 1 As shown, an embodiment of the present invention proposes an underwater ultrasonic signal analysis method based on the Doppler effect, the method comprising: Acquire a transmitting frequency of an ultrasonic signal, transmit the ultrasonic signal to an underwater target, receive echo signals reflected by multiple underwater targets, and obtain echo signal data containing reflection information of the multiple underwater targets; The echo signal data is segmented according to the preset time window, and the envelope demodulation and phase unwrapping operations are performed on the signal in each time window to extract its amplitude sequence, phase sequence and instantaneous frequency sequence data to generate the corresponding time series signal feature data; Based on the time series signal feature data, frequency change, amplitude change and phase change data are extracted and clustered to distinguish and mark different underwater target components. The preliminary frequency shift signal sets of multiple underwater targets are obtained, where each preliminary frequency shift signal set contains one or more frequency tracks. Based on the preliminary frequency shift signal set, combined with the amplitude change and phase change data, the amplitude stability index and phase change synchronization index are calculated for each frequency trajectory in all time windows. The main frequency trajectory is then filtered based on this to form the final frequency shift signal set. According to the final frequency shift signal set, the frequency shift value in each time window is extracted, and the speed of each underwater target in each time window is calculated based on the Doppler effect. The direction of movement is determined by combining the frequency change data and phase change data, and the motion state analysis results of the underwater target are generated and output.

[0023] In this embodiment of the present invention, by acquiring the transmission frequency of an ultrasonic signal, transmitting it to underwater targets, and collecting echo signals reflected from multiple targets, a raw data foundation covering the diversity and dynamics of the targets can be obtained. The echo signal data is segmented according to a preset time window, and through envelope demodulation and phase unwrapping operations, amplitude, phase, and instantaneous frequency series data are extracted, achieving comprehensive quantification of the original signal's multidimensional physical characteristics.

[0024] In subsequent processing, cluster analysis based on the three types of extracted change data (frequency, amplitude, and phase) can effectively distinguish the echo components of multiple underwater targets. Even in complex environments (such as multiple targets, overlaps, and noise fields), the signal trajectories of different targets can be separated to obtain a preliminary set of frequency-shifted signals.

[0025] Further screening based on amplitude stability and phase synchronization automatically eliminates abnormal trajectories, retaining only physically dominant frequency trajectories, significantly improving the accuracy and robustness of the dominant trajectories. For example, if a frequency trajectory exhibits large amplitude fluctuations and irregular phase changes, it is automatically identified as interference. Conversely, a stable trajectory can be accurately labeled as the target motion path.

[0026] Finally, by extracting frequency shift data from the main frequency trajectory, calculating the speed of each target based on the Doppler effect, and combining frequency and phase change data to determine the direction of movement, full dynamic tracking of speed and direction and high-resolution motion state analysis of all underwater targets were achieved.

[0027] For example: In an actual underwater sonar scenario, if there are two moving ships and a background obstacle at the same time, the method of the present invention can separate and mark their respective reflected signals, calculate the speed and approach or distance status of each ship, and eliminate the influence of background noise, providing complete and reliable analysis results for engineering applications such as underwater target recognition, navigation and obstacle avoidance.

[0028] Through the above series of processing steps, the present invention significantly improves the signal recognition accuracy, motion trajectory tracking continuity and practicality of terminal applications in a multi-target dynamic underwater environment.

[0029] In a preferred embodiment of the present invention, the echo signal data is segmented according to a preset time window, and envelope demodulation and phase unwrapping operations are performed on the signal in each time window to extract its amplitude sequence, phase sequence, and instantaneous frequency sequence data to generate corresponding time series signal feature data, including: Perform time axis framing on the echo signal data, dividing the echo signal into multiple frame segments according to a preset time window to obtain a segmented signal sequence; According to the segmented signal sequence, envelope demodulation processing is performed on the echo signal of each frame segment, and its amplitude change curve is extracted to form amplitude sequence data; According to the echo signal data, the phase unwrapping operation is performed to extract the continuous phase change curve and calculate the phase growth rate of each frame segment to obtain the phase sequence and instantaneous frequency sequence data; The amplitude sequence, phase sequence and instantaneous frequency sequence data are organized in time sequence to obtain time series signal feature data.

[0030] In embodiments of the present invention, by framing the echo signal data on a time axis, local changes in the echo signal can be captured with higher temporal resolution. For example, in ocean observations, if a target suddenly accelerates or decelerates, framing can record this process in detail, avoiding the loss of local dynamic features due to overall averaging.

[0031] Envelope demodulation is applied to each frame to effectively eliminate the influence of the high-frequency carrier wave, directly generating an amplitude curve reflecting the target's distance and volume. Subsequently, phase unwrapping is performed on the original echo signal to obtain continuous phase variation information, allowing calculation of the phase growth rate and instantaneous frequency for each frame. For example, the phase and frequency data for the same target within different frames can clearly reflect subtle changes in its motion direction and speed.

[0032] Finally, by organizing the amplitude, phase, and instantaneous frequency sequences in chronological order, a complete time series signal feature data set is generated. This not only enables continuous analysis of multiple targets and events, but also provides a stable, rich, and easy-to-process source of raw data for subsequent multi-dimensional feature clustering and fine separation of underwater targets. For example, in a complex port environment, the distinct echo characteristics of a moored ship and a distant vehicle can be distinguished.

[0033] Among them, the amplitude sequence, phase sequence and instantaneous frequency sequence data are organized in chronological order to obtain time series signal feature data, specifically including: First, after processing the echo signal of each frame segment, the amplitude sequence, phase sequence, and instantaneous frequency sequence arranged by sampling time are obtained. Each set of data is ordered by the start and end sampling points of the time window, ensuring that each eigenvalue corresponds to the sampling point at the same time.

[0034] Next, these three sets of data are synchronized and paired, with time as the primary axis. That is, at each sampling moment, there is a corresponding amplitude, phase, and instantaneous frequency characteristic value. This way, all characteristic points are arranged in chronological order.

[0035] Finally, the amplitude, phase and instantaneous frequency arranged in time sequence in each time window are combined in sequence to form a complete set of multi-dimensional signal feature data with time labels for subsequent target recognition and cluster analysis.

[0036] For example: Assuming the time window is 10 milliseconds and the interval between each sampling point is 1 millisecond, 10 groups of amplitude, phase and instantaneous frequency feature points will be arranged in sequence in each window, and the corresponding sampling time point will be clearly marked in each group of data.

[0037] In a preferred embodiment of the present invention, frequency change, amplitude change, and phase change data are extracted based on the time series signal feature data, clustering is performed, and different underwater target components are distinguished and marked to obtain a preliminary frequency shift signal set of multiple underwater targets, including: According to the time series signal characteristic data, the instantaneous frequency change, amplitude change and phase change data in each time window are extracted in sequence to form a frequency change sequence, an amplitude change sequence and a phase change sequence respectively; According to the frequency change sequence, the frequency changes of adjacent time windows are analyzed, and points with similar change trends are grouped into preliminary frequency change groups to obtain multiple frequency change candidate groups; For each frequency change candidate group, extract its amplitude change sequence, calculate its mean and standard deviation respectively, and screen out the frequency change candidate groups that meet the amplitude judgment criteria based on the conditions that the amplitude mean is higher than the preset valid signal judgment threshold and the standard deviation is lower than the preset amplitude stability judgment threshold; For each frequency change candidate group that meets the amplitude criterion, its phase change sequence is analyzed, and the correlation between the phase change trend and the frequency change trend within the group is calculated. The groups with correlation higher than the preset correlation threshold are determined as the preliminary frequency shift signal sets of the same underwater target. Each set contains one or more frequency trajectories.

[0038] In this embodiment of the present invention, the instantaneous frequency, amplitude, and phase changes within each time window are sequentially extracted from the time series signal feature data, breaking down the original complex signal into a multidimensional data sequence that is easier to analyze. For example, if a target (such as a submarine) passes through a surveillance area at a constant speed, its frequency variation trend, amplitude stability, and phase synchronization can be accurately tracked to distinguish it from other small clutter or interfering targets.

[0039] Subsequently, frequency change trend analysis is used to group data points with similar trends into preliminary frequency change groups, helping to initially identify target segments with consistent motion. Furthermore, for each candidate frequency change group, the mean and standard deviation of the amplitude change sequence are calculated to identify signal groups with high amplitudes and low fluctuations. For example, the echo amplitudes of large marine organisms or underwater robots in motion are often large and stable, effectively eliminating low-amplitude noise.

[0040] Finally, for candidate groups that meet the amplitude criteria, their phase change sequences are further analyzed and correlated with frequency change trends. Only those groups with synchronized phase and frequency changes are retained as valid targets. For example, when an underwater target approaches the sonar array in a straight line, its frequency and phase change trends are highly synchronized, allowing it to be accurately identified and tracked. Through this series of layer-by-layer refinement and gradual elimination of anomalies, the final preliminary frequency shift signal set obtained will only contain valid underwater target trajectories with obvious physical characteristics and clear motion states, providing high-quality input for subsequent high-resolution estimation of speed and direction.

[0041] Among them, according to the time series signal characteristic data, the instantaneous frequency change, amplitude change and phase change data in each time window are extracted in sequence to form a frequency change sequence, an amplitude change sequence and a phase change sequence respectively, which specifically include: First, the amplitude, phase and instantaneous frequency feature points of the same target are extracted from the time series signal feature data in each time window and arranged into their respective initial sequences in chronological order.

[0042] For the frequency change sequence, the instantaneous frequency eigenvalues ​​in two adjacent time windows are subtracted in sequence to obtain the frequency change in each window, and these changes are arranged in sequence to obtain the frequency change sequence.

[0043] For the amplitude change sequence, similarly, the amplitude eigenvalues ​​of adjacent time windows are subtracted to obtain the amplitude changes, which are then arranged in sequence to form an amplitude change sequence.

[0044] For the phase change sequence, the phase eigenvalue changes between adjacent windows are calculated in sequence and arranged in order to obtain the phase change sequence.

[0045] This differential extraction method can accurately reflect the dynamic change patterns of the target within each time window, providing a rich data basis for subsequent clustering, target identification and abnormal trajectory screening.

[0046] For example, if the instantaneous frequency characteristics of three consecutive time windows are 10kHz, 10.5kHz, and 11kHz, then the frequency change sequence is 0.5kHz and 0.5kHz. Similarly, the amplitude and phase change sequences are generated in exactly the same way.

[0047] The method for determining the preset effective signal determination threshold and the preset amplitude stability determination threshold specifically includes: Valid signal judgment threshold: This is typically done based on the statistical characteristics of the echo signal's amplitude. Specifically, a set of echo signals under representative underwater background noise conditions can be selected, their amplitude distribution statistically analyzed, and a lower amplitude limit set above the background noise level can be established. For example, the mean amplitude under background noise conditions plus a multiple of the standard deviation can be used as the minimum amplitude criterion for determining an echo signal as a "valid target reflection signal."

[0048] During the implementation process, the maximum amplitudes in multiple target-free environments can be counted through actual measurement of historical signals, and a higher judgment threshold can be set based on this, so that only signals with amplitudes exceeding the threshold are considered valid target signals, filtering out environmental noise and clutter.

[0049] Amplitude stability judgment threshold: This method is primarily used to determine the stability of signal amplitude within a target motion cycle. Specifically, the standard deviation of the amplitude sequence for each target candidate group is calculated. A smaller standard deviation indicates a more stable signal amplitude. By collecting a large number of echo signals from different targets and in different motion states, the standard deviation distribution of each amplitude sequence is statistically analyzed.

[0050] Based on actual application requirements, a maximum allowable standard deviation threshold is preset. For example, in previous fish monitoring tasks, if the amplitude standard deviation exceeds the set value, the group is likely to be subject to sudden interference or signal obstruction, making it impossible to serve as a stable target trajectory. Typically, empirical methods (such as using the standard deviation of target amplitude stability above 95% as a reference) or adaptive optimization combined with machine learning methods are used to continuously adjust the threshold to adapt to varying environmental noise levels and signal quality requirements.

[0051] For example, if the background noise level is low, the amplitude standard deviation is 0.5, and the target effective signal amplitude is mostly higher than 1.0, in actual application, the effective signal judgment threshold can be set to 1.2, and the amplitude stability judgment threshold can be set to 0.8 to ensure the accuracy and robustness of target recognition.

[0052] In a preferred embodiment of the present invention, based on the preliminary frequency shift signal set, combined with the amplitude change and phase change data, the amplitude stability index and the phase change synchronization index are calculated for each frequency trajectory in all time windows, and the main frequency trajectory is filtered based on the results to form a final frequency shift signal set, including: For each frequency trajectory, extract its amplitude change data in all time windows and calculate its standard deviation as the amplitude stability index; For each frequency trajectory, based on the frequency change data and phase change data in all time windows, the frequency change direction and phase change direction in each time window are compared to determine whether the signs are consistent. The proportion of windows with consistent signs in all time windows is counted to obtain the growth direction consistency score. For each frequency trajectory, the frequency change data and phase change data are first-order differencing, respectively, to obtain the frequency change rate sequence and phase change rate sequence. The two are then subjected to correlation analysis at the same window position. The proportion of windows with correlation higher than the preset synchronization judgment threshold is statistically calculated as the phase change synchronization indicator. For all frequency trajectories, combined with the amplitude stability index, growth direction consistency score and phase change synchronization index, a weighted scoring or threshold screening method is used to select the frequency trajectory with the highest comprehensive score as the main frequency trajectory, and all main frequency trajectories are collected to form the final frequency shift signal set.

[0053] In an embodiment of the present invention, by calculating the amplitude stability index and phase change synchronization index for each frequency trajectory in the preliminary frequency shift signal set in all time windows, the main frequency trajectory with the strongest physical significance and the best signal quality can be screened out.

[0054] For example, suppose an underwater sonar monitors three reflective targets (e.g., A, B, and C). The initial frequency trajectory of each target may fluctuate due to noise or obstruction. By calculating the standard deviation of the amplitude variation data for each trajectory, interfering trajectories with large amplitude fluctuations and poor stability can be eliminated. For example, if target B experiences sudden amplitude fluctuations due to localized bubble interference, and its standard deviation is significantly higher than that of targets A and C, it will be identified as a non-primary trajectory.

[0055] Furthermore, we perform a synchronization analysis on the phase change data of the frequency trajectory, calculating the consistency of the frequency and phase change directions within each time window and the correlation of the rate changes. This ensures that the frequency and phase change trends of the selected main frequency trajectory remain coordinated throughout the entire time dimension. For example, if target A approaches in a straight line at a constant speed, while target C accelerates away, the frequency and phase changes of both are highly synchronized. Therefore, the synchronization index can ensure that the main frequency trajectory reflects the actual target movement.

[0056] This technology can greatly improve the physical accuracy and anti-interference ability of the final frequency-shifted signal set, effectively avoid the introduction of false trajectories, and provide a strong data foundation for subsequent high-resolution analysis of speed and direction.

[0057] Among them, for all frequency trajectories, combined with the amplitude stability index, growth direction consistency score and phase change synchronization index, a weighted scoring or threshold screening method is used to select the frequency trajectory with the highest comprehensive score as the main frequency trajectory. All main frequency trajectories are collected to form the final frequency shift signal set, which specifically includes: Feature extraction: For each frequency trajectory, we first extract the amplitude change data in all time windows, and calculate the standard deviation of the amplitude change based on this data as the "amplitude stability index" of the trajectory; secondly, we count the sign consistency of the frequency change direction and the phase change direction in each time window, and get the proportion of windows with consistent signs, which is used as the "growth direction consistency score"; thirdly, we perform first-order differences on the frequency change data and phase change data respectively to obtain their respective rate sequences, and then perform correlation analysis on the rates of the corresponding time windows, and count the proportion of windows with correlations higher than the set threshold as the "phase change synchronization index."

[0058] Normalization of feature quantities: The above three indicators are normalized separately (for example, they are uniformly adjusted to the interval between 0 and 1) to eliminate the impact of different dimensions and ranges on subsequent weighted scores.

[0059] Weighted score calculation: Set the weight coefficients of the three indicators of amplitude stability, growth direction consistency and phase change synchronization (such as 0.4, 0.3, and 0.3 respectively, which can be adjusted according to historical data, experimental results or actual scenarios), then multiply the normalized score of each indicator by the corresponding weight coefficient, and add the three results to obtain the "comprehensive score" of the frequency trajectory.

[0060] Example description: If the normalized scores of track A are 0.8, 0.7, and 0.9, respectively, the final weighted score = 0.8 × 0.4 + 0.7 × 0.3 + 0.9 × 0.3.

[0061] Main track screening and collection: After calculating the weighted scores for all frequency traces, the trace with the highest overall score within each preliminary frequency-shifted signal set is selected as the primary frequency trace. If threshold filtering is used, a minimum score threshold can be set, and only traces with scores above the threshold are retained as primary traces.

[0062] Forming the final set: The main frequency trajectories screened out from all preliminary frequency shift signal sets are collected and uniformly output as the "final frequency shift signal set" for subsequent motion parameter analysis and output.

[0063] In a preferred embodiment of the present invention, the frequency shift values ​​within each time window are extracted based on the final frequency shift signal set, the speed of each underwater target within each time window is calculated based on the Doppler effect, and the motion direction is determined by combining the frequency change data and the phase change data. The motion state analysis results of the underwater targets are generated and output, including: According to the main frequency trajectory, in accordance with the time window order, the instantaneous frequency value in each time window is extracted, and the instantaneous frequency value in each time window is differentiated from the transmitting frequency based on the transmitting frequency to obtain the frequency shift value of each time window; According to the frequency shift value in each time window of each main frequency trajectory, the Doppler effect calculation method is used to divide the frequency shift value by the transmission frequency and multiply it by the propagation speed of ultrasound in water to obtain the corresponding radial velocity sequence in each time window; For each main frequency trajectory, the frequency change data and phase change data in each time window are used to determine the signs of the frequency change direction and phase change direction in the same window. If the signs are consistent, the underwater target is determined to be close to the emission source. If the signs are opposite, the underwater target is determined to be far away from the emission source. This generates the motion direction criterion for each time window. The radial velocity sequence of each main frequency trajectory in all time windows is combined with the motion direction criterion to form the velocity and motion direction data of each underwater target in the entire time series; According to the speed and movement direction data of each underwater target in the entire time series, the motion state analysis results of the underwater target are generated and output.

[0064] In this embodiment of the present invention, the frequency shift values ​​within each time window are extracted using the final frequency-shift signal set, and the velocity is calculated based on the Doppler effect. This allows for precise quantification of the radial velocity of each underwater target over different time periods. For example, assuming that target A is actively approaching and target B is slowly retreating, the instantaneous frequency is extracted in each time window using their respective dominant frequency trajectories. After subtracting this frequency from the transmitted frequency, the corresponding velocity sequence is calculated using the Doppler formula. In actual testing, even subtle changes in target velocity are accurately and dynamically reflected in real time.

[0065] Furthermore, by combining the frequency and phase change data for each main frequency trajectory, we determine whether the target is approaching or receding within each window (sign judgment), thereby obtaining directional velocity information. For example, if both frequency and phase increase within a window, it indicates that the target is rapidly approaching; otherwise, it is receding. Finally, by combining the velocity and direction criteria for each target across all windows, we can fully output a dynamic state curve of the underwater target over time.

[0066] This step significantly improves the accuracy and real-time performance of target motion state estimation, meeting the needs of continuous monitoring, situational awareness, and behavior prediction in multi-target, highly dynamic scenarios. For example, in complex fishing grounds or ports, it can distinguish the motion trajectories and speed characteristics of fishing vessels, submersibles, and naturally floating objects, assisting in automated early warning and intelligent underwater management.

[0067] The radial velocity sequence of each main frequency trajectory in all time windows is combined with the motion direction criterion to form the velocity and motion direction data of each underwater target in the entire time series, specifically including: Main frequency trajectory velocity sequence extraction: For each main frequency trajectory, the instantaneous frequency value in each window is extracted in sequence according to the time window order, and the "frequency shift value" is obtained after subtracting the transmission frequency. The ratio of the frequency shift value to the transmission frequency is then multiplied by the ultrasonic propagation speed in water to obtain the "radial velocity" data in each window.

[0068] For example, if the transmission frequency is 50 kHz and the instantaneous frequency of the main trajectory in a certain time window is 51 kHz, then the frequency shift value is 1 kHz and the radial velocity is (1 kHz / 50 kHz) × 1500 m / s = 30 m / s.

[0069] Determination of motion direction criteria: For each time window of each main frequency trajectory, analyze the sign relationship between the frequency change direction and the phase change direction within the window: When the signs are consistent (e.g., both are positive or both are negative), the underwater target's movement direction in the time window is determined to be "approaching" the emission source; When the signs are opposite, it is judged as "far away" from the emission source.

[0070] This criterion is coded as "1" or "-1" or recorded directly as approaching / moving away from the tag.

[0071] Speed ​​and direction data combination: The radial velocity sequence of the main frequency trajectory is combined with the corresponding motion direction criterion in a one-to-one correspondence to form a complete "speed + direction" feature point at each moment (time window).

[0072] Full sequence target motion state generation: The speed and direction data for all time windows under each main trajectory are organized in chronological order to form the complete motion state time series data of the underwater target during the monitoring period. If there are multiple main frequency trajectories, the speed and direction time series data for the corresponding underwater target are generated separately.

[0073] Result output: The final output is the radial velocity and direction of motion of each underwater target (main frequency trajectory) in each time window, which is convenient for subsequent application scenarios such as target tracking, classification, risk warning or underwater behavior analysis.

[0074] In a preferred embodiment of the present invention, according to the segmented signal sequence, envelope demodulation processing is performed on the echo signal of each frame segment, and its amplitude change curve is extracted to form amplitude sequence data, including: For each frame signal in the segmented signal sequence, for each sampling point, the absolute value of the signal between it and multiple adjacent sampling points is counted, and the absolute value of the signal obtained by the statistics is averaged in the time direction to obtain the envelope amplitude corresponding to the sampling point; Arrange the envelope amplitudes of all sampling points in each frame segment in sequence according to the sampling time to form an amplitude change curve; According to the amplitude variation curve, the amplitude at each sampling moment is extracted point by point to form amplitude sequence data.

[0075] In this embodiment of the present invention, by performing envelope demodulation on each frame of the echo signal through a segmented signal sequence, it is possible to effectively eliminate the high-frequency carrier wave's masking of amplitude characteristics, thereby extracting an amplitude variation curve that truly reflects the target volume, distance, and signal propagation loss. For example, in an underwater environment, if a reflective target (such as a school of fish) experiences a sudden change in volume or a disturbance in the signal propagation path, the envelope curve will respond quickly and sensitively, displaying local peaks or troughs in the amplitude.

[0076] Furthermore, by calculating the absolute value of the signal at all sampling points within each frame and comparing it with adjacent sampling points and performing a temporal averaging operation, we can effectively smooth local high-frequency noise and improve the continuity of the amplitude curve. For example, if there is weak background clutter or high-frequency mechanical interference underwater, the above averaging process will significantly reduce its impact on the amplitude sequence.

[0077] Finally, by extracting the amplitude values ​​at each sampling moment point by point and arranging them in chronological order, a complete and detailed amplitude sequence data set can be generated. This data can not only be used for subsequent cluster analysis and target identification, but also facilitates a visual display of the signal intensity evolution of each target during sonar monitoring, providing intuitive and continuous data support for real-time monitoring and historical analysis. For example, when responding to sudden underwater events (such as explosions or large-scale fish migrations), rapid abnormal changes in the amplitude sequence can enable timely detection and response.

[0078] For each frame signal in the segmented signal sequence, for each sampling point, the absolute value of the signal with multiple adjacent sampling points is counted, and the absolute value of the signal obtained is averaged in the time direction to obtain the envelope amplitude corresponding to the sampling point, specifically including: Sampling point and neighborhood selection: For each frame, we traverse each sampling point from the beginning to the end. For the current sampling point, we select several adjacent sampling points (e.g., 2 to 5 each) before and after it to form a local neighborhood sampling window. The size of the neighborhood window can be set based on the actual sampling rate and signal characteristics.

[0079] Absolute value statistical processing: For the current sampling point and all sampling points in its neighborhood, take the absolute value of their amplitudes (i.e., remove the positive and negative signs and only retain the signal strength value).

[0080] Time mean calculation: The absolute value amplitudes of all sampling points in the above neighborhood window are summed and averaged by the number of sampling points to obtain the envelope amplitude of the current sampling point.

[0081] For example, if the absolute values ​​of a sampling point and its four neighboring sampling points are {3, 4, 5, 6, 7}, then the envelope amplitude is (3+4+5+6+7) / 5=5.

[0082] Full frame envelope extraction: Repeat the above steps for all sampling points within each frame segment, sequentially generating the envelope amplitude sequence for all sampling points within that frame segment, thus completing the envelope demodulation process for a frame of signal. This operation can be expanded frame by frame across the entire segmented signal sequence, ultimately forming a continuous amplitude change curve and amplitude sequence data, providing the input basis for subsequent analysis.

[0083] In a preferred embodiment of the present invention, a phase unwrapping operation is performed based on the echo signal data to extract a continuous phase change curve and calculate the phase growth rate of each frame segment to obtain phase sequence data and instantaneous frequency sequence data, including: According to the echo signal data, for each sampling point, the preliminary phase value of the sampling point is calculated using its sine and cosine components to obtain a set of preliminary phase sequence data; Perform continuity correction on the preliminary phase sequence data. When the phase difference between adjacent sampling points is found to be greater than half a cycle, eliminate the sudden change by adding or subtracting 2π, so that the phase change curve remains monotonically continuous in time, forming continuous phase sequence data. According to the continuous phase sequence data, the phase difference between each two adjacent sampling points is calculated, and the phase difference is divided by the time interval between the two sampling points to obtain the phase growth rate sequence of each sampling point; The phase growth rates of all sampling points are arranged in the order of sampling time to form instantaneous frequency sequence data.

[0084] In an embodiment of the present invention, by performing a phase unwrapping operation on the echo signal data, extracting a continuous phase change curve, and calculating the phase growth rate of each frame segment, high-precision interpretation and quantification of the echo signal phase information can be achieved.

[0085] For example, for target object A, the phase of the reflected signal will continuously change due to its changing distance from the emission source. By estimating the initial phase of the sine and cosine components at each sampling point and combining it with continuity correction (such as adding or subtracting 2π to eliminate sudden changes), we can effectively avoid phase jumps and unwrapping discontinuities, ensuring that the phase curve within each time window accurately and coherently reflects the target's motion.

[0086] Furthermore, the phase difference between adjacent sampling points is calculated using continuous phase sequence data, and the phase growth rate is converted into the time interval, thereby obtaining an instantaneous frequency sequence that reflects the slight dynamic changes of the signal.

[0087] For example, when a submarine moves at high speed underwater, even if the speed and motion trajectory change slightly, its instantaneous frequency curve can be reflected synchronously, achieving high-resolution dynamic tracking.

[0088] Through the above method, the tracking accuracy and robustness of complex underwater moving targets are greatly improved, providing a solid signal foundation for subsequent speed and movement direction judgment based on the Doppler effect.

[0089] According to the echo signal data, for each sampling point, the preliminary phase value of the sampling point is calculated using its sine and cosine components to obtain a set of preliminary phase sequence data, which specifically includes: Raw data preparation: For the original echo signal in each frame segment, each sampling point is traversed in turn, and the amplitude of the sampling point in the original signal is extracted respectively.

[0090] Sine and cosine components are obtained: For each sampling point, the sine component (i.e., real part) and cosine component (i.e., imaginary part) of the sampling point are calculated according to the common principles of digital signal processing. In practical applications, the Hilbert transform method can be used to obtain its analytical signal, thereby directly extracting the real and imaginary data of each sampling point.

[0091] Preliminary phase value calculation: For each sampling point, the cosine component is used as the horizontal coordinate and the sine component as the vertical coordinate. Based on the inverse tangent relationship of trigonometric functions, the preliminary phase value of the sampling point is calculated using the "inverse tangent" calculation method (such as arctan or atan2 function).

[0092] To put it simply, the real and imaginary positions of each sampling point on the two-dimensional plane are converted into a unique phase angle through polar coordinate transformation.

[0093] Preliminary phase sequence generation: The preliminary phase values ​​calculated at each sampling point are arranged in chronological order to obtain a complete preliminary phase sequence data for that frame segment. This data sequence provides the basis for subsequent phase unwrapping processing (eliminating 2π jumps and ensuring continuity) and further instantaneous frequency calculation.

[0094] In a preferred embodiment of the present invention, the frequency changes in adjacent time windows are analyzed according to the frequency change sequence, and points with similar change trends are grouped into preliminary frequency change groups to obtain multiple frequency change candidate groups, including: According to the frequency change sequence, the frequency change in each two adjacent time windows is differentiated to obtain the frequency change increment corresponding to each time window; For the frequency change increments of all time windows, according to the principle of numerical proximity and consistent change trend, the frequency change increments are clustered using the similarity criterion to obtain multiple frequency change subsequences with similar change trends; The frequency change amounts of adjacent time windows in each frequency change subsequence are sequentially connected to form a preliminary frequency change grouping; A plurality of frequency change candidate groups are generated based on all preliminary frequency change groupings.

[0095] In an embodiment of the present invention, the frequency changes of adjacent time windows are analyzed according to the frequency change sequence, and points with similar change trends are grouped into preliminary frequency change groups, which can achieve intelligent preliminary screening and clustering of frequency components in multi-target scenarios.

[0096] For example, when there are multiple moving targets within the sonar detection range, the frequency variation characteristics of the reflected signals from each target vary due to their relative speed. By clustering the frequency variation increments within all time windows based on numerical proximity and consistent trends, the frequency components corresponding to multiple targets can be automatically grouped.

[0097] For example, if targets A, B, and C are in the states of uniform approach, distance, and accelerated approach, respectively, after clustering, the frequency change subsequence related to target A, the grouping of target B, and the grouping of target C can be distinguished, thus laying the foundation for subsequent detailed analysis and classification labeling.

[0098] Through this intelligent grouping, data dimensions can be effectively reduced, human intervention can be reduced, and target recognition efficiency and automation can be improved. It is particularly suitable for complex underwater environments with high noise and multiple interferences.

[0099] Among them, for the frequency change increments of all time windows, according to the principle of numerical proximity and consistent change trend, the frequency change increments are clustered using the similarity criterion to obtain multiple frequency change subsequences with similar change trends, including: Frequency change increment calculation and arrangement: First, for the frequency change data of all time windows, the frequency change increments of every two adjacent time windows are calculated in sequence, all incremental results are sorted into a sequence, and numbered in chronological order.

[0100] Similarity criterion design: For the frequency change increment, two main similarity criteria are set: The first is “numerical proximity”, that is, if the difference between two frequency change increments is within a certain range (such as the threshold range set by empirical values), they are considered to be numerically close.

[0101] The second is “consistency of change trends”, that is, if the signs of the increments of two frequency changes are consistent (for example, both increase or both decrease), then their change trends are considered to be consistent.

[0102] Clustering processing: Using a step-by-step traversal and recursive clustering approach, starting from the first time window, the subsequent increments are determined to see if they simultaneously meet the two conditions of "numeric proximity" and "change trend consistency." If so, these frequency change increments are merged into the same group, forming a "frequency change subsequence." When an increment that does not meet these conditions is encountered, a new grouping is initiated.

[0103] Output clustering results: Through this approach, all frequency change increments are ultimately divided into several different frequency change subsequences with similar change trends. Each subsequence corresponds to a target frequency change component with obvious consistency characteristics, laying the foundation for subsequent amplitude judgment and target clustering.

[0104] In a preferred embodiment of the present invention, for each frequency change candidate group that meets the amplitude criterion, its phase change sequence is analyzed, the correlation between the phase change trend and the frequency change trend within the group is calculated, and the group with a correlation higher than a preset correlation threshold is determined as a preliminary frequency shift signal set of the same underwater target, including: For each frequency change candidate group that meets the amplitude criterion, extract the phase change sequence within the corresponding time window to form the phase change data of the group; A first-order difference method is used to obtain a frequency change rate sequence and a phase change rate sequence of the frequency change candidate group respectively; Perform correlation analysis on the frequency change rate sequence and the phase change rate sequence in each corresponding time window, calculate the correlation coefficient, and obtain the correlation criterion of each group; For each frequency change candidate group, determine whether its correlation criterion is higher than the preset correlation threshold. The groups above the threshold are determined as the preliminary frequency shift signal sets of the same underwater target, and all preliminary frequency shift signal sets that meet the conditions are output.

[0105] In an embodiment of the present invention, for each frequency change candidate group that meets the amplitude criterion, its phase change sequence is analyzed, and the correlation between the phase change trend and the frequency change trend within the group is calculated. The groups with correlation higher than the preset correlation threshold are determined as the preliminary frequency shift signal set of the same underwater target, which can further improve the accuracy of target separation and the physical reliability of clustering.

[0106] For example, among multiple frequency change candidate groups selected based on amplitude stability, some groups, while stable in amplitude, lack physical synchronization between frequency and phase changes (e.g., abnormal groups caused by reflected noise or multipath aliasing). In this case, by performing correlation coefficient analysis on the frequency and phase change rate sequences within each group, physically irrelevant or noise-dominated groups are eliminated, retaining only those with high correlation and strong synchronization as the target preliminary frequency shift signal set.

[0107] For example, if target A and target B are in the same direction but at different speeds, if their phase and frequency changes are highly synchronized, they can be correctly classified as two independent, real targets to prevent missed detection or false detection.

[0108] Through this step, the physical rationality and practical effectiveness of target differentiation and clustering in a multi-target environment can be greatly improved, providing a strong foundation for subsequent main frequency trajectory screening and motion state analysis.

[0109] The frequency change rate sequence and the phase change rate sequence are subjected to correlation analysis in each corresponding time window, and the correlation coefficient is calculated to obtain the correlation criterion of each group, which specifically includes: Sequence preparation: For each candidate frequency change group, first obtain its frequency change rate sequence and phase change rate sequence in each time window. The rate sequences here are all sequence data obtained in the previous step (such as first-order difference), which are arranged in the order of time windows.

[0110] Corresponding window pairing: For the same time window, the frequency change rate and the phase change rate of the group are paired one-to-one to form a rate pair under the same window.

[0111] Correlation coefficient analysis method: For the above rate pairs, a sliding window method is used to select a time range (such as 5 to 10 consecutive time windows) and count the degree of coordination of all rate pairs within this period.

[0112] In specific processing, a standard correlation coefficient statistical method (such as the Pearson correlation coefficient principle) is used, that is, the degree of synchronization between the change direction and change amplitude of the frequency change rate and the phase change rate is compared one by one.

[0113] If the two trends of increasing or decreasing over time are consistent, the correlation coefficient value is close to 1; if the change trends are opposite, the correlation coefficient is negative or close to -1; if there is no obvious relationship, the correlation coefficient is close to 0.

[0114] Correlation criterion output: For all time windows, the correlation scores of each frequency-phase rate pair are calculated and summarized to form a correlation criterion sequence. Based on the statistical results, the correlation of time windows exceeding the set judgment threshold is determined to determine whether the group is a valid frequency shift signal set of the same underwater target.

[0115] The method for determining the preset correlation threshold specifically includes: Statistical analysis of historical data: By analyzing a large number of historical underwater ultrasonic signal samples, the frequency change rate and phase change rate sequences of the same target are selected, and their correlation coefficient distribution ranges are calculated respectively.

[0116] Distribution characteristics determination: Count the correlation coefficients of all samples and observe their distribution characteristics (such as mean, standard deviation, and distribution range). Generally, the correlation coefficients of effective frequency-phase change rate pairs belonging to the same target are generally higher than a certain value (such as 0.7-0.8), while random combinations of different targets tend to be concentrated around 0.

[0117] Experience threshold setting and dynamic adjustment: Based on the statistical distribution, the judgment threshold is set at the dividing point between valid groups and invalid groups. The "minimum value of the positive correlation group" or "the overall average minus half the standard deviation" is often taken as the empirical threshold. For scenarios with high noise environments or low signal-to-noise ratios, a dynamic adjustment method can also be used to fine-tune the judgment threshold in real time based on the correlation distribution of the actual collected signals.

[0118] In a preferred embodiment of the present invention, for each frequency trajectory, the frequency change data and phase change data are first-order differencing performed to obtain a frequency change rate sequence and a phase change rate sequence, and a correlation analysis is performed on the two at the same window position, including: For each frequency trajectory, extract its frequency change data and phase change data in all time windows, and perform first-order difference processing on each time window to obtain the corresponding frequency change rate sequence and phase change rate sequence; According to the frequency change rate sequence and phase change rate sequence, the rate values ​​of the two are extracted at each time window position to construct a set of rate pairs in the same time window; For the rate pairs of all time windows, the correlation coefficient algorithm is used to perform correlation analysis to obtain the correlation score sequence of each time window.

[0119] In an embodiment of the present invention, for each frequency trajectory, the frequency change data and phase change data are respectively subjected to first-order difference to obtain a frequency change rate sequence and a phase change rate sequence, and a correlation analysis is performed on the two at the same window position, which can realize multi-dimensional dynamic consistency detection of target motion features and quantitative elimination of abnormal trajectories.

[0120] The specific effects are as follows: In underwater multi-target detection environments, the motion of different targets often manifests as synchronized or coordinated changes in frequency and phase. First-order difference processing can capture subtle dynamics in the rate of signal change, enabling precise quantification of characteristics such as acceleration, deceleration, and sudden changes in moving targets.

[0121] Furthermore, in each time window, the frequency change rate and the phase change rate are paired to form a rate pair, and a window sliding correlation algorithm is used to calculate the Pearson correlation coefficient or other correlation measurement of the two sets of data window by window.

[0122] For example: For physically consistent main frequency trajectories, the correlation coefficient will be significantly higher than the background noise in most windows (e.g., correlation > 0.8), reflecting the synchronization of the changes of the two. Abnormal trajectories (such as those caused by noise or false targets) are characterized by asynchronous frequency and phase changes or uncorrelated trends (such as correlation < 0.3), making them easy to eliminate directly.

[0123] Through this multi-window correlation quantification process, synchronization metrics (such as the proportion of highly correlated windows) are automatically calculated for each frequency trajectory, effectively distinguishing the true target's main trajectory from noise tracks. Ultimately, this improves the overall recognition accuracy and robustness of underwater moving targets, significantly reduces false positives and missed negatives, and enhances the algorithm's engineering applicability and intelligence.

[0124] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, multiple improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An underwater ultrasonic signal analysis method based on the Doppler effect, characterized in that: The method comprises: Acquire a transmitting frequency of an ultrasonic signal, transmit the ultrasonic signal to an underwater target, receive echo signals reflected by multiple underwater targets, and obtain echo signal data containing reflection information of the multiple underwater targets; The echo signal data is segmented according to the preset time window, and the envelope demodulation and phase unwrapping operations are performed on the signal in each time window to extract its amplitude sequence, phase sequence and instantaneous frequency sequence data to generate the corresponding time series signal feature data; Based on the time series signal feature data, frequency change, amplitude change and phase change data are extracted and clustered to distinguish and mark different underwater target components. The preliminary frequency shift signal sets of multiple underwater targets are obtained, where each preliminary frequency shift signal set contains one or more frequency tracks. Based on the preliminary frequency shift signal set, combined with the amplitude change and phase change data, the amplitude stability index and phase change synchronization index are calculated for each frequency trajectory in all time windows. The main frequency trajectory is then filtered based on this to form the final frequency shift signal set. According to the final frequency shift signal set, the frequency shift value in each time window is extracted, and the speed of each underwater target in each time window is calculated based on the Doppler effect. The direction of movement is determined by combining the frequency change data and phase change data, and the motion state analysis results of the underwater target are generated and output.

2. The underwater ultrasonic signal analysis method based on the Doppler effect according to claim 1, characterized in that: The echo signal data is segmented according to the preset time window, and the envelope demodulation and phase unwrapping operations are performed on the signal in each time window. The amplitude sequence, phase sequence and instantaneous frequency sequence data are extracted to generate the corresponding time series signal feature data, including: Perform time axis framing on the echo signal data, dividing the echo signal into multiple frame segments according to a preset time window to obtain a segmented signal sequence; According to the segmented signal sequence, envelope demodulation processing is performed on the echo signal of each frame segment, and its amplitude change curve is extracted to form amplitude sequence data; According to the echo signal data, the phase unwrapping operation is performed to extract the continuous phase change curve and calculate the phase growth rate of each frame segment to obtain the phase sequence and instantaneous frequency sequence data; The amplitude sequence, phase sequence and instantaneous frequency sequence data are organized in time sequence to obtain time series signal feature data.

3. The underwater ultrasonic signal analysis method based on the Doppler effect according to claim 1, characterized in that: Based on the time series signal feature data, the frequency change, amplitude change and phase change data are extracted and clustered to distinguish and mark the different underwater target components. The preliminary frequency shift signal set of multiple underwater targets is obtained, including: According to the time series signal characteristic data, the instantaneous frequency change, amplitude change and phase change data in each time window are extracted in sequence to form a frequency change sequence, an amplitude change sequence and a phase change sequence respectively; According to the frequency change sequence, the frequency changes of adjacent time windows are analyzed, and points with similar change trends are grouped into preliminary frequency change groups to obtain multiple frequency change candidate groups; For each frequency change candidate group, extract its amplitude change sequence, calculate its mean and standard deviation respectively, and screen out the frequency change candidate groups that meet the amplitude judgment criteria based on the conditions that the amplitude mean is higher than the preset valid signal judgment threshold and the standard deviation is lower than the preset amplitude stability judgment threshold; For each frequency change candidate group that meets the amplitude criterion, its phase change sequence is analyzed, and the correlation between the phase change trend and the frequency change trend within the group is calculated. The groups with correlation higher than the preset correlation threshold are determined as the preliminary frequency shift signal sets of the same underwater target. Each set contains one or more frequency trajectories.

4. The underwater ultrasonic signal analysis method based on the Doppler effect according to claim 1, characterized in that: Based on the preliminary frequency shift signal set, combined with the amplitude change and phase change data, the amplitude stability index and phase change synchronization index are calculated for each frequency trajectory in all time windows. The main frequency trajectory is filtered based on this to form the final frequency shift signal set, including: For each frequency trajectory, extract its amplitude change data in all time windows and calculate its standard deviation as the amplitude stability index; For each frequency trajectory, based on the frequency change data and phase change data in all time windows, the frequency change direction and phase change direction in each time window are compared to determine whether the signs are consistent. The proportion of windows with consistent signs in all time windows is counted to obtain the growth direction consistency score. For each frequency trajectory, the frequency change data and phase change data are first-order differencing, respectively, to obtain the frequency change rate sequence and phase change rate sequence. The two are then subjected to correlation analysis at the same window position. The proportion of windows with correlation higher than the preset synchronization judgment threshold is statistically calculated as the phase change synchronization indicator. For all frequency trajectories, combined with the amplitude stability index, growth direction consistency score and phase change synchronization index, a weighted scoring or threshold screening method is used to select the frequency trajectory with the highest comprehensive score as the main frequency trajectory, and all main frequency trajectories are collected to form the final frequency shift signal set.

5. The underwater ultrasonic signal analysis method based on the Doppler effect according to claim 1, characterized in that: Based on the final frequency shift signal set, the frequency shift value in each time window is extracted. The speed of each underwater target in each time window is calculated based on the Doppler effect. The direction of motion is determined by combining the frequency change data and phase change data. The motion state analysis results of the underwater target are generated and output, including: According to the main frequency trajectory, in accordance with the time window order, the instantaneous frequency value in each time window is extracted, and the instantaneous frequency value in each time window is differentiated from the transmitting frequency based on the transmitting frequency to obtain the frequency shift value of each time window; According to the frequency shift value in each time window of each main frequency trajectory, the Doppler effect calculation method is used to divide the frequency shift value by the transmission frequency and multiply it by the propagation speed of ultrasound in water to obtain the corresponding radial velocity sequence in each time window; For each main frequency trajectory, the frequency change data and phase change data in each time window are used to determine the signs of the frequency change direction and phase change direction in the same window. If the signs are consistent, the underwater target is determined to be close to the emission source. If the signs are opposite, the underwater target is determined to be far away from the emission source. This generates the motion direction criterion for each time window. The radial velocity sequence of each main frequency trajectory in all time windows is combined with the motion direction criterion to form the velocity and motion direction data of each underwater target in the entire time series; According to the speed and movement direction data of each underwater target in the entire time series, the motion state analysis results of the underwater target are generated and output.

6. The underwater ultrasonic signal analysis method based on the Doppler effect according to claim 2, characterized in that: According to the segmented signal sequence, envelope demodulation processing is performed on the echo signal of each frame segment, and its amplitude change curve is extracted to form amplitude sequence data, including: For each frame signal in the segmented signal sequence, for each sampling point, the absolute value of the signal between it and multiple adjacent sampling points is counted, and the absolute value of the signal obtained is averaged in the time direction to obtain the envelope amplitude corresponding to the sampling point; Arrange the envelope amplitudes of all sampling points in each frame segment in sequence according to the sampling time to form an amplitude change curve; According to the amplitude variation curve, the amplitude at each sampling moment is extracted point by point to form amplitude sequence data.

7. The underwater ultrasonic signal analysis method based on the Doppler effect according to claim 2, characterized in that: According to the echo signal data, the phase unwrapping operation is performed to extract the continuous phase change curve and calculate the phase growth rate of each frame segment to obtain the phase sequence data and instantaneous frequency sequence data, including: According to the echo signal data, for each sampling point, the preliminary phase value of the sampling point is calculated using its sine and cosine components to obtain a set of preliminary phase sequence data; Perform continuity correction on the preliminary phase sequence data. When the phase difference between adjacent sampling points is found to be greater than half a cycle, eliminate the sudden change by adding or subtracting 2π, so that the phase change curve remains monotonically continuous in time, forming continuous phase sequence data. According to the continuous phase sequence data, the phase difference between each two adjacent sampling points is calculated, and the phase difference is divided by the time interval between the two sampling points to obtain the phase growth rate sequence of each sampling point; The phase growth rates of all sampling points are arranged in the order of sampling time to form instantaneous frequency sequence data.

8. The underwater ultrasonic signal analysis method based on the Doppler effect according to claim 3, characterized in that: According to the frequency change sequence, the frequency changes in adjacent time windows are analyzed, and points with similar change trends are grouped into preliminary frequency change groups to obtain multiple frequency change candidate groups, including: According to the frequency change sequence, the frequency change in each two adjacent time windows is differentiated to obtain the frequency change increment corresponding to each time window; For the frequency change increments of all time windows, according to the principle of numerical proximity and consistent change trend, the frequency change increments are clustered using the similarity criterion to obtain multiple frequency change subsequences with similar change trends; The frequency change amounts of adjacent time windows in each frequency change subsequence are sequentially connected to form a preliminary frequency change grouping; A plurality of frequency change candidate groups are generated based on all preliminary frequency change groupings.

9. The underwater ultrasonic signal analysis method based on the Doppler effect according to claim 3, characterized in that: For each frequency change candidate group that meets the amplitude criterion, its phase change sequence is analyzed, and the correlation between the phase change trend and the frequency change trend within the group is calculated. The group with a correlation higher than the preset correlation threshold is determined as the preliminary frequency shift signal set of the same underwater target, including: For each frequency change candidate group that meets the amplitude criterion, extract the phase change sequence within the corresponding time window to form the phase change data of the group; A first-order difference method is used to obtain a frequency change rate sequence and a phase change rate sequence of the frequency change candidate group respectively; Perform correlation analysis on the frequency change rate sequence and the phase change rate sequence in each corresponding time window, calculate the correlation coefficient, and obtain the correlation criterion of each group; For each frequency change candidate group, determine whether its correlation criterion is higher than the preset correlation threshold. The groups above the threshold are determined as the preliminary frequency shift signal sets of the same underwater target, and all preliminary frequency shift signal sets that meet the conditions are output.

10. The underwater ultrasonic signal analysis method based on the Doppler effect according to claim 4, characterized in that: For each frequency trajectory, the frequency change data and phase change data are first-order differencing to obtain the frequency change rate sequence and phase change rate sequence, and the correlation analysis of the two at the same window position is performed, including: For each frequency trajectory, extract its frequency change data and phase change data in all time windows, and perform first-order difference processing on each time window to obtain the corresponding frequency change rate sequence and phase change rate sequence; According to the frequency change rate sequence and phase change rate sequence, the rate values ​​of the two are extracted at each time window position to construct a set of rate pairs in the same time window; For the rate pairs of all time windows, the correlation coefficient algorithm is used to perform correlation analysis to obtain the correlation score sequence of each time window.

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