Underwater target detection data analysis method and system for underwater equipment

Abstract

The present application provides a method and system for analyzing underwater target detection data for underwater equipment, which relates to the field of data processing technology. A phase correction matrix is ​​constructed through an inertial navigation data set, and delay error compensation is performed on the detection signal based on the phase correction matrix to obtain a compensation frequency band subset; a wavenumber domain spatial spectrum is constructed according to the central wavenumber of each compensation frequency band in the compensation frequency band subset, and then the wavenumber domain spatial spectrum is inverted and distributed mapped to obtain a target distribution model; based on the target distribution model, the detection and navigation information of the underwater equipment is coupled and estimated to obtain a state result and an observation vector during underwater detection, and then the state result is filtered and updated according to the observation vector to obtain a confidence tracking result; time domain smoothing and target enhancement operations are performed on the confidence tracking result to obtain an enhanced trajectory of the underwater target. The present application can realize the precise fusion of underwater multi-source detection data and target trajectory enhancement to improve the detection accuracy of underwater targets.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and more specifically, to a method and system for analyzing underwater target detection data for underwater equipment. Background Art

[0002] With the continuous development of information technology and computing power, data processing technology based on digital computing and signal processing has been widely used in military, industrial, marine and other fields. Especially in data fusion and analysis in complex environments, the use of multi-source information for structured modeling, transformation operations, state estimation and anomaly detection has become an important foundation of modern intelligent systems.

[0003] In the field of underwater equipment navigation, underwater target detection data analysis methods are primarily used to detect and track underwater targets using devices such as sonar and inertial navigation systems. Currently, underwater detection systems onboard underwater equipment often employ sonar arrays, inertial sensors, and environmental parameter devices in collaboration, aiming to accurately identify and continuously track underwater targets under conditions of strong interference, low signal-to-noise ratios, and weak target signatures. However, existing underwater target detection data analysis methods still cause data mismatches and phase error accumulation in practical applications, impacting subsequent fusion accuracy. Single-channel analysis based on the time or frequency domain lacks the ability to structurally model and invert frequency band wavenumber information. Conventional filtering and estimation algorithms struggle to achieve high-confidence target state recovery in complex sea conditions or weak target scenarios, resulting in limited detection accuracy. Therefore, achieving accurate fusion of underwater multi-source detection data and target trajectory enhancement to improve underwater target detection accuracy remains a challenging issue facing the industry. Summary of the Invention

[0004] The present application provides a method and system for analyzing underwater target detection data for underwater equipment, which can achieve accurate fusion of underwater multi-source detection data and target trajectory enhancement to improve the detection accuracy of underwater targets.

[0005] In a first aspect, the present application provides a method and system for analyzing underwater target detection data for underwater equipment, the data analysis method comprising the following steps:

[0006] Acquire inertial navigation data sets from underwater equipment and collect multi-modal detection signals through the underwater equipment's multi-source detection equipment;

[0007] constructing a phase correction matrix using the inertial navigation data set, and performing time delay error compensation on the detection signal based on the phase correction matrix to obtain a compensation frequency band subset;

[0008] constructing a wavenumber domain spatial spectrum according to the central wavenumber of each compensation frequency band in the compensation frequency band subset, and then performing inversion distribution mapping on the wavenumber domain spatial spectrum to obtain a target distribution model;

[0009] performing coupled estimation on the detection and navigation information of the underwater equipment based on the target distribution model to obtain a state result and an observation vector during underwater detection, and then filtering and updating the state result according to the observation vector to obtain a confidence tracking result;

[0010] Temporal smoothing and target enhancement operations are performed on the confidence tracking result to obtain an enhanced trajectory of the underwater target.

[0011] In this embodiment, the inertial navigation data set of the underwater equipment is obtained through the inertial navigation database of the underwater equipment management platform.

[0012] In this embodiment, the multi-source detection equipment includes: a vector hydrophone array, a sound velocity profiler, and a multi-channel broadband sonar.

[0013] In this embodiment, constructing a phase correction matrix using the inertial navigation data set specifically includes:

[0014] Extracting attitude angle and velocity information of the underwater equipment from the inertial navigation data set;

[0015] Correcting the channel response in the sonar array using the attitude angle and the velocity information to obtain a channel phase offset;

[0016] A phase correction matrix is ​​constructed based on the channel phase offsets.

[0017] In this embodiment, performing delay error compensation on the detection signal based on the phase correction matrix to obtain the compensated frequency band subset specifically includes:

[0018] Converting the detection signal into the frequency domain to obtain a frequency domain signal;

[0019] Dividing the frequency domain signal into a plurality of sub-band signals based on a time delay characteristic;

[0020] Rotational phase compensation is performed on each sub-band signal using the phase correction matrix to obtain a compensated frequency band subset.

[0021] In this embodiment, constructing a wavenumber domain spatial spectrum according to the center wavenumber of each compensation frequency band in the compensation frequency band subset specifically includes:

[0022] determining a center wave number of each compensation frequency band in the compensation frequency band subset;

[0023] For each compensation frequency band, the frequency band data of the compensation frequency band is normalized by the central wave number of the compensation frequency band, thereby obtaining a wave number domain distribution diagram;

[0024] The wave number domain distribution map is spatially corrected based on an underwater sound propagation model to obtain a wave number domain spatial map.

[0025] In this embodiment, performing inversion distribution mapping on the wavenumber domain spatial spectrum to obtain the target distribution model specifically includes:

[0026] Performing two-dimensional weighted processing on the wavenumber domain spatial spectrum to obtain a complex spectrum;

[0027] Inverting the complex spectrum into a spatial domain, and then performing distribution mapping on the spatial domain to obtain an inversion distribution map;

[0028] Amplitude-based noise suppression is performed on the inversion distribution map to obtain a target distribution model.

[0029] In this embodiment, coupled estimation is performed on the detection and navigation information of the underwater equipment based on the target distribution model to obtain the state result and observation vector during underwater detection. Specifically, the following steps are performed:

[0030] The detection and navigation information of underwater equipment is filtered based on Kalman filtering to obtain the observation vector during underwater detection;

[0031] The target distribution model is used to perform nonlinear state estimation on the detection and navigation information of the underwater equipment and the observation vector to obtain the state result during underwater detection.

[0032] In this embodiment, performing temporal smoothing and target enhancement operations on the confidence tracking result to obtain an enhanced trajectory of the underwater target specifically includes:

[0033] Performing time domain smoothing on the confidence tracking result by a filtering algorithm to obtain a smooth tracking result;

[0034] The target is enhanced by performing target enhancement on the smooth tracking result based on a compressed sensing reconstruction algorithm to obtain an enhanced trajectory of the underwater target.

[0035] In a second aspect, the present application provides an underwater target detection data analysis system for underwater equipment, which is used to perform an underwater target detection data analysis method for underwater equipment. The data analysis system includes:

[0036] Multi-source acquisition module, used to obtain inertial navigation data sets of underwater equipment and collect multi-modal detection signals through multi-source detection equipment of underwater equipment;

[0037] an error compensation module, configured to construct a phase correction matrix using the inertial navigation data set, and perform time delay error compensation on the detection signal based on the phase correction matrix to obtain a compensation frequency band subset;

[0038] a target spectrum construction module, configured to construct a wavenumber domain spatial spectrum according to the central wavenumber of each compensation frequency band in the compensation frequency band subset, and then perform inversion distribution mapping on the wavenumber domain spatial spectrum to obtain a target distribution model;

[0039] An observation update module is used to perform coupled estimation on the detection and navigation information of the underwater equipment based on the target distribution model to obtain a state result and an observation vector during underwater detection, and then filter and update the state result according to the observation vector to obtain a confidence tracking result;

[0040] The trajectory enhancement module is used to perform time domain smoothing and target enhancement operations on the confidence tracking result to obtain an enhanced trajectory of the underwater target.

[0041] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0042] Acquire an inertial navigation data set of underwater equipment, and collect multi-modal detection signals through the multi-source detection equipment of the underwater equipment; construct a phase correction matrix through the inertial navigation data set, and perform time delay error compensation on the detection signal based on the phase correction matrix to obtain a compensation frequency band subset; construct a wavenumber domain spatial spectrum according to the central wavenumber of each compensation frequency band in the compensation frequency band subset, and then perform inversion distribution mapping on the wavenumber domain spatial spectrum to obtain a target distribution model; perform coupling estimation on the detection and navigation information of the underwater equipment based on the target distribution model to obtain a state result and observation vector during underwater detection, and then filter and update the state result according to the observation vector to obtain a confidence tracking result; perform time domain smoothing and target enhancement operations on the confidence tracking result to obtain an enhanced trajectory of the underwater target.

[0043] It can be seen that in this application, accurate fusion of underwater multi-source detection data and enhancement of target trajectory can be achieved. First, by collecting multi-modal detection signals and synchronously collecting and structuring the fusion of heterogeneous sensor data, a high-quality data foundation is provided for subsequent signal compensation and model construction; and based on the phase correction matrix, the detection signal is compensated for the time delay error, which can dynamically correct the array channel phase offset caused by the change of the navigation posture of the underwater equipment, improve the consistency of the frequency band signal, and help enhance the accuracy of frequency band reconstruction; secondly, a wavenumber domain spatial map is constructed according to the central wavenumber of the compensation frequency band, and Fourier inversion distribution mapping is performed, which can realize the conversion of spectrum data to space. The transformed expression of the objective function makes the target scattering characteristics distinguishable in the two-dimensional map, which is beneficial to improving the detectability and image saliency of small targets in low signal-to-noise ratio environments; then, coupled modeling and filtering updates are performed based on the target distribution model and navigation information, which can enhance the dynamic response capability of the target tracking process and improve the confidence of the state prediction through the nonlinear estimation mechanism, which is beneficial to achieve dynamic and accurate estimation of the target state; finally, by performing time domain smoothing and compressed sensing enhancement on the confidence tracking results, false trajectories and background noise are suppressed while maintaining the stability of the target main trajectory, which is beneficial to improving the resolution of weak target trajectories.

[0044] In summary, the technical solution adopted in this application can achieve accurate fusion of underwater multi-source detection data and target trajectory enhancement to improve the detection accuracy of underwater targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0046] Figure 1 This is a flow chart of a method for analyzing underwater target detection data for underwater equipment provided in the present application;

[0047] Figure 2 is an exemplary flow chart of determining a compensation frequency band subset according to the present application;

[0048] Figure 3 is an exemplary flow chart for determining a target distribution model according to the present application;

[0049] Figure 4 It is a module structure diagram of the data analysis system provided by this application. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0051] An embodiment of the present application provides a method and system for analyzing underwater target detection data for underwater equipment, the core of which is to obtain an inertial navigation data set of the underwater equipment and collect multi-modal detection signals through the multi-source detection equipment of the underwater equipment; construct a phase correction matrix through the inertial navigation data set, and compensate the detection signal for time delay errors based on the phase correction matrix to obtain a compensation frequency band subset; construct a wavenumber domain spatial spectrum based on the center wavenumber of each compensation frequency band in the compensation frequency band subset, and then perform inversion distribution mapping on the wavenumber domain spatial spectrum to obtain a target distribution model; based on the target distribution model, couple estimate the detection and navigation information of the underwater equipment to obtain a state result and observation vector during underwater detection, and then filter and update the state result based on the observation vector to obtain a confidence tracking result; perform time domain smoothing and target enhancement operations on the confidence tracking result to obtain an enhanced trajectory of the underwater target.

[0052] Example 1: In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods. Figure 1 As shown in FIG. 1 , this figure is an exemplary flow chart of a method for analyzing underwater target detection data for underwater equipment according to this embodiment of the present application. The data analysis method includes the following steps:

[0053] In step S1, an inertial navigation data set of underwater equipment is acquired, and a multi-modal detection signal is collected by a multi-source detection device of the underwater equipment.

[0054] It should be noted that the inertial navigation data set in this application is a high-frequency dynamic data sequence that characterizes the motion state of underwater equipment and can reflect changes in the hull's attitude and speed. In specific implementation, the inertial navigation data set of the underwater equipment can be obtained through the inertial navigation database of the underwater equipment management platform, wherein the inertial navigation data set includes the attitude angle (roll angle, pitch angle, yaw angle) and speed information of the underwater equipment. The inertial navigation system refers to a navigation equipment system that realizes continuous measurement of the attitude and motion parameters of underwater equipment based on an inertial measurement unit, including an accelerometer, a gyroscope and a data fusion processing unit, which is used to estimate the three-dimensional displacement, velocity and attitude angle of the underwater equipment in real time without relying on external signals, wherein the attitude angle includes roll angle, pitch angle and yaw angle. The underwater equipment management platform refers to a comprehensive control system used to integrate and manage underwater equipment operating parameters, sensor data, equipment configuration and historical operation trajectory. Various types of sensor collection data can be retrieved, viewed or exported in the underwater equipment management platform.

[0055] In specific implementation, multi-modal detection signals are collected by multi-source detection equipment installed on the bottom of the underwater equipment. The multi-source detection equipment includes: a vector hydrophone array, a sound velocity profiler and a multi-channel broadband sonar. Among them, the vector hydrophone array is used to simultaneously collect sound pressure and vibration velocity signals, which can reflect the target directionality; the sound velocity profiler is used to measure the sound velocity gradient changes in the water body in real time; the multi-channel broadband sonar is used to receive reflected signals in a wide frequency range and has higher resolution.

[0056] It should be noted that the underwater equipment in this application refers to autonomous system equipment and semi-autonomous system equipment deployed in water environments such as oceans, lakes, and reservoirs for performing underwater detection, monitoring, navigation, and communication. The underwater equipment may include: underwater robots, submersibles, underwater unmanned boats, and ship bottom integrated equipment; in addition, in this application, the underwater equipment is equipped with a multi-source detection system and an inertial navigation system, among which the multi-source detection system can obtain underwater environmental information and detection navigation information through multi-source detection equipment, and is suitable for complex sea conditions, underwater target identification and tracking scenarios.

[0057] In step S2, a phase correction matrix is ​​constructed using the inertial navigation data set, and delay error compensation is performed on the detection signal based on the phase correction matrix to obtain a compensation frequency band subset.

[0058] In this embodiment, the phase correction matrix may be constructed using the inertial navigation data set in the following manner:

[0059] Extracting attitude angle and velocity information of the underwater equipment from the inertial navigation data set;

[0060] Correcting the channel response in the sonar array using the attitude angle and the velocity information to obtain a channel phase offset;

[0061] A phase correction matrix is ​​constructed based on the channel phase offsets.

[0062] It should be noted that the sonar array in this embodiment is an underwater sound wave receiving device composed of multiple sensor (i.e., array element) channels, which can be designed according to the deployment conditions of the underwater equipment, the target detection area and the expected operating frequency band, and is used to collect sound wave signals reflected from underwater targets; the sonar array has multiple receiving channels, each channel corresponds to an array element, and can independently receive sound wave signals reflected from the target.

[0063] In a specific implementation, first, the inertial navigation data set can be parsed using an existing multi-sensor fusion algorithm to obtain the attitude angle and velocity information of the underwater equipment. The attitude angle is used to describe the rotation state of the underwater equipment in three-dimensional space, and the velocity information is used to reflect the movement direction and speed of the underwater equipment at the current moment. Then, the channel phase offset refers to the phase difference caused by the signal reception time difference of each channel in the sonar array due to the different attitude changes and movement speeds of the underwater equipment. One channel in the sonar array can be used as a reference channel to calculate the propagation path difference between the reference channel and other channels in the target direction, and then use this propagation path difference as the channel phase offset. The propagation path difference can be calculated by using Euler angles based on the physical distance between array elements (the physical distance is specified when designing the sonar array) and the acceleration to calculate the attitude angle, and the calculated Euler angles are used as the channel phase offset. Finally, the channel phase offsets between each channel in the sonar array can be obtained through the above steps. The matrix calculated by combining the phase offsets of each channel using a complex function is used as the phase correction matrix.

[0064] It should be noted that the phase correction matrix in this application is a complex matrix, which is used for frequency domain compensation. It can adjust the phase consistency of multi-channel signals so that the signals between different channels reach a unified phase reference. It can eliminate the time delay error and array distortion caused by the posture change and movement of underwater equipment, and improve the spatial consistency of detection data.

[0065] Preferably, in this embodiment, reference Figure 2 As shown in the figure, this figure is an exemplary flow chart for determining a compensation frequency band subset according to the present application. In this embodiment, the delay error compensation of the detection signal is performed based on the phase correction matrix to obtain the compensation frequency band subset, which can be specifically implemented by the following steps:

[0066] In step S21, the detection signal is converted into the frequency domain to obtain a frequency domain signal;

[0067] In step S22, the frequency domain signal is divided into a plurality of sub-band signals based on the delay characteristics;

[0068] In step S23, rotation phase compensation is performed on each sub-band signal using the phase correction matrix to obtain a compensated frequency band subset.

[0069] In specific implementation, first, the detection signal is converted from the time domain to the frequency domain through the fast Fourier transform algorithm to obtain a frequency domain signal, wherein the frequency domain signal is represented by a complex spectrum; then, the frequency domain signal can be divided into multiple sub-band signals according to the attitude change range of the underwater equipment and the sonar bandwidth characteristics, wherein each sub-band corresponds to a certain center frequency and bandwidth; finally, each sub-band signal is subjected to rotational phase compensation through the phase correction matrix. In actual implementation, the complex number in the phase correction matrix is ​​multiplied with the sub-band signal to achieve phase alignment of multi-channel data, and the set of all phase-aligned sub-band signals is used as the compensation band subset.

[0070] It should be noted that the phase compensation process in this embodiment is a rotation phase operation performed in the frequency domain, and the phase correction matrix is ​​a complex matrix, wherein the elements of the phase correction matrix represent the complex numbers of different channels at different frequency points, which are used to compensate for phase rotation. It can achieve accurate compensation for the phase difference of multi-channel signals caused by the movement of underwater equipment, so that the frequency domain data of different channels remain consistent in spatial projection, thereby improving the coherence and spatial resolution of the signal, which is beneficial to the subsequent construction of wavenumber domain maps and the improvement of spatial inversion accuracy.

[0071] In step S3, a wavenumber domain spatial spectrum is constructed according to the central wavenumber of each compensation frequency band in the compensation frequency band subset, and then the wavenumber domain spatial spectrum is inverted and distributed mapped to obtain a target distribution model.

[0072] In this embodiment, the wavenumber domain spatial spectrum is constructed according to the central wavenumber of each compensation frequency band in the compensation frequency band subset in the following manner, namely:

[0073] determining a center wave number of each compensation frequency band in the compensation frequency band subset;

[0074] For each compensation frequency band, the frequency band data of the compensation frequency band is normalized by the central wave number of the compensation frequency band, thereby obtaining a wave number domain distribution diagram;

[0075] The wave number domain distribution map is spatially corrected based on an underwater sound propagation model to obtain a wave number domain spatial map.

[0076] In the specific implementation, first, the central wave number of each compensation frequency band in the compensation frequency band subset is extracted. The central wave number can be obtained by multiplying the central frequency of the compensation frequency band with the average sound velocity of the propagation medium in the water body. The average sound velocity of the propagation medium in the water body can be obtained by water quality detection technology, which will not be described here. Then, for each compensation frequency band, the frequency domain amplitude spectrum or phase spectrum is normalized and scaled with the central wave number as a reference to construct a wave number response expression under a unified dimension, and then the wave number after unified dimension is mapped into a wave number domain distribution diagram through a visualization library. Finally, the wave number domain distribution is analyzed based on the existing underwater sound propagation model. The map is spatially corrected, wherein the spatial correction can correct the wavenumber distortion caused by environmental changes by introducing the seawater depth layered structure, sound speed profile and propagation path model, and finally obtain a wavenumber domain spatial map that can reflect the spatial distribution characteristics of the target echo. As a preferred embodiment, the existing underwater sound propagation model can use a parabolic equation model. The advantage of using the parabolic equation model for correction is that under known boundary conditions and sound speed profile information, the propagation path and energy attenuation of the sound wave in seawater can be accurately modeled, thereby improving the ability of the spatially corrected wavenumber map to truly restore the target reflection characteristics.

[0077] It should be noted that the wavenumber domain spatial spectrum in this embodiment refers to a two-dimensional mapping structure that expresses frequency band response in the wavenumber dimension. The horizontal axis of the wavenumber domain spatial spectrum is the spatial position, and the vertical axis is the wavenumber value. Each pixel point in the wavenumber domain spatial spectrum reflects the energy distribution of the corresponding spatial area under a specific wavenumber. By normalizing different compensation frequency bands to a unified wavenumber scale and performing spatial correction in combination with the underwater sound propagation characteristics, the spectrum distortion problem caused by multi-band heterogeneous sampling and complex water source media can be effectively solved, thereby improving the expression ability of the wavenumber domain spectrum for target features.

[0078] Preferably, in this embodiment, reference Figure 3 As shown in FIG. 1 , this figure is an exemplary flow chart for determining a target distribution model according to the present application. In this embodiment, the wavenumber domain spatial spectrum is inverted and distributed to obtain the target distribution model, which can be specifically implemented by the following steps:

[0079] In step S31, a two-dimensional weighted processing is performed on the wavenumber domain spatial spectrum to obtain a complex spectrum;

[0080] In step S32, the complex spectrum is inverted into a spatial domain, and then distribution mapping is performed on the spatial domain to obtain an inversion distribution map;

[0081] In step S33, amplitude-based noise suppression is performed on the inversion distribution map to obtain a target distribution model.

[0082] In a specific implementation, first, a two-dimensional weighted processing is performed on the wavenumber domain spatial spectrum, wherein the weighted processing can perform edge suppression and energy focusing through a window function, for example: a Gaussian filter window is used to perform weighted filtering on the boundary area of ​​the wavenumber domain spatial spectrum to obtain a complex spectrum, which is beneficial to improving the concentration of the spectrum; then, the complex spectrum is converted to the spatial domain through a two-dimensional inverse Fourier transform to obtain an initial distribution map representing the echo intensity of each spatial position, and the scattering intensity of the initial distribution map can be reconstructed and expressed based on contour interpolation to obtain an inversion distribution map, wherein the inversion distribution map is a spatial domain representation obtained after the Fourier inversion transform, and the inversion distribution map can intuitively reflect the spatial echo intensity distribution of the underwater target; finally, noise suppression based on amplitude characteristics is performed on the inversion distribution map, preferably, an amplitude threshold method can be used to constrain background noise, and then the inversion distribution map after noise suppression processing is visualized as a target distribution model through a visualization library, wherein the use of the amplitude threshold method to constrain background noise is beneficial to eliminating background noise and discrete abnormal points and retaining the main echo component.

[0083] It should be noted that the target distribution model in this application refers to a spatial target structure diagram constructed by performing complex spectrum inversion and noise suppression on the wavenumber domain spectrum. The target distribution model can reflect the scattering intensity and distribution characteristics of underwater targets in three-dimensional space. In addition, in this embodiment, by introducing two-dimensional weighting and noise amplitude constraints in the inversion process, the imaging resolution of the main reflection area can be enhanced while effectively suppressing false responses caused by unstructured signal interference, thereby improving the spatial stability and recognizability of the target distribution model.

[0084] In step S4, the detection and navigation information of the underwater equipment is coupled and estimated based on the target distribution model to obtain the state result and observation vector during underwater detection, and then the state result is filtered and updated according to the observation vector to obtain the confidence tracking result.

[0085] In this embodiment, coupled estimation is performed on the detection and navigation information of the underwater equipment based on the target distribution model to obtain the state result and observation vector during underwater detection. Specifically, the following method can be used, namely:

[0086] The detection and navigation information of underwater equipment is filtered based on Kalman filtering to obtain the observation vector during underwater detection;

[0087] The target distribution model is used to perform nonlinear state estimation on the detection and navigation information of the underwater equipment and the observation vector to obtain the state result during underwater detection.

[0088] In the specific implementation, first, the detection and navigation information of the underwater equipment is filtered using the Kalman filter, wherein the detection and navigation information includes the navigation status parameters such as the position, speed, attitude angle, etc. of the underwater equipment, which can be collected by the multi-source detection equipment in the multi-source detection system. The detection and navigation information is then filtered using the Kalman filter algorithm, which can be combined with the inertial navigation system and the multi-source detection signal to suppress high-frequency disturbances and cumulative errors, thereby obtaining an observation vector reflecting the current position of the underwater equipment; then, the Kalman filter algorithm can be used to perform joint iterative estimation on the observation vector and the target distribution model to obtain the state result at the moment of underwater detection, wherein the Kalman filter algorithm is a recursive The minimum mean square error estimation algorithm has the advantages of strong real-time performance, high estimation accuracy and low computational complexity. It can continuously estimate state variables in a dynamic system. By constructing a state transition model and an observation model, and jointly modeling the detection and navigation information of the underwater equipment with the target distribution model, and then dynamically correcting the state estimate based on the residual between the current observation vector and the predicted state, it can effectively suppress the influence of system noise and measurement error on the estimation accuracy. Preferably, the Kalman filter algorithm in this application can be an extended Kalman filter algorithm (i.e., EKF) to adapt to the nonlinear state transfer relationship in the target distribution model, thereby improving the adaptability of the coupled estimation in complex underwater environments and the stability of the target state prediction.

[0089] It should be noted that the state result in this application refers to a set of dynamic variables obtained after performing coupled estimation to describe the motion state of the underwater target. The state result includes the spatial position, velocity vector, track direction, and optional acceleration or azimuth of the underwater target, which is used to reflect the physical state of the target at the time of detection. The state result can be predicted and updated in a continuous time series through a state transition model to form a continuous characterization of the target motion trend; in addition, the observation vector refers to a set of actual measurement values ​​obtained after sensor observation and filtering for auxiliary estimation, including position and velocity information obtained by the inertial navigation system, and data such as the target scattering center or echo intensity coordinates extracted from the target distribution model, which is used to match the predicted state during the filtering process to correct the estimation error; in this embodiment, the use of Kalman filtering for nonlinear state estimation can integrate the spatial characteristics of underwater equipment navigation data and the target distribution model, which is conducive to improving the accuracy of state estimation.

[0090] In addition, it should be noted that in this application, the state results and observation vectors during underwater detection are determined in real time and synchronously, that is, the state results and observation vectors during underwater detection are variables and can be updated according to the actual detection time.

[0091] In this embodiment, the state result is filtered and updated according to the observation vector to obtain the confidence tracking result, which can be specifically obtained in the following manner, namely:

[0092] Determining an update error term based on the observation vector and the state result;

[0093] Calculating a gain factor by using the updated error term, and then performing a weighted update on the state result during underwater detection by using the gain factor to obtain an updated state result;

[0094] Confidence evaluation is performed on the updated status results to obtain confidence tracking results.

[0095] In a specific implementation, first, the difference between the observation vector and the state result can be used as an update error term, wherein the update error term refers to the difference between the observation vector and the predicted state result, which is used to reflect the degree of dynamic deviation between the current observation data of the system and the result predicted by the state estimation model. Preferably, the difference between the observation vector and the state result can be determined using the existing Kalman filter residual calculation formula; then, the update error term is calculated as the independent variable of the existing Kalman gain calculation formula, and the calculation result is used as the gain factor, and the gain factor is used as a weighting coefficient to perform weighted correction on the predicted state result to obtain an updated state result; Finally, a confidence tracking result representing the credibility of the state estimation can be formed based on the Mahalanobis distance. In actual implementation, the Mahalanobis distance is used to measure the relative deviation of the current state result from the observation center under the covariance constraint, and can be calculated using the existing Mahalanobis distance calculation formula. The smaller the Mahalanobis distance, the more consistent the state result and the observation data, and the higher the credibility. It should be noted that the gain factor in this embodiment refers to a dynamic weight parameter used to adjust the fusion ratio between the predicted state result and the actual observation data, which can be determined by the ratio between the system state covariance and the observation noise covariance. The specific calculation can use the existing Kalman filter gain calculation formula.

[0096] It should be noted that the confidence tracking result in the present application is a target state estimation result with a credibility assessment. The confidence tracking result includes a set of state variables including an estimated value and an uncertainty measure (such as covariance, confidence interval), which are used to quantify the reliability of the estimation result. By introducing a dynamic confidence assessment mechanism in the state estimation process, it is possible to identify and eliminate observation anomalies and reduce the interference of sensor noise on system performance, which is beneficial to improving the robustness and intelligent response capability of the system in complex environments. In addition, in this embodiment, the role of filtering and updating the state result is to dynamically correct the state result predicted at the previous moment by introducing the observation vector at the current moment, which can effectively suppress error accumulation, improve estimation accuracy, and enhance the system's response capability to sudden target behaviors.

[0097] In step S5, time domain smoothing and target enhancement operations are performed on the confidence tracking result to obtain an enhanced trajectory of the underwater target.

[0098] In this embodiment, time domain smoothing and target enhancement operations are performed on the confidence tracking results to obtain an enhanced trajectory of the underwater target, specifically in the following manner:

[0099] Performing time domain smoothing on the confidence tracking result by a filtering algorithm to obtain a smooth tracking result;

[0100] The target is enhanced by performing target enhancement on the smooth tracking result based on a compressed sensing reconstruction algorithm to obtain an enhanced trajectory of the underwater target.

[0101] In specific implementation, first, the time series of the confidence tracking results can be smoothed by a sliding window filtering algorithm, wherein the sliding window filtering refers to weighted averaging of multiple consecutive confidence tracking results in units of a set time window length, thereby eliminating the trajectory jitter problem caused by noise, mutation or irrational drift. Preferably, a weighted sliding average method can be used, wherein the weighting coefficient is set according to the time distance of the sample in the time window, so that the smoothed tracking result is more consistent with the target motion trend; then, a linear observation model can be established based on compressed sensing theory, and the sparse coefficients in the linear observation model can be solved by minimizing the L1 norm, and then the solution of the linear observation model can be visualized through a visualization library to obtain an enhanced trajectory. Preferably, two compressed sensing reconstruction algorithms, matching pursuit or basis pursuit, can be used, which is conducive to improving the ability of the reconstruction result to represent the true trajectory of the target.

[0102] It should be noted that the enhanced trajectory in the present application is an underwater target motion trajectory with higher stability and accuracy. The enhanced trajectory represents a credible result that suppresses noise interference. The enhanced trajectory is conducive to improving the continuity and resolution of the target trajectory, and provides reliable data support for underwater target behavior analysis. In this embodiment, the target continuity expression effect can be improved by time domain smoothing processing, and the compressed sensing reconstruction algorithm can be used to mine the significant feature patterns in the trajectory and enhance the key motion state of the target. In addition, the visualization library used in this application can use the Matplotlib library, which has the advantages of low computational complexity, fast rendering speed and strong interactivity, and can realize intuitive underwater target trajectory display.

[0103] In summary, the technical solution adopted in this application can achieve accurate fusion of underwater multi-source detection data and target trajectory enhancement to improve the detection accuracy of underwater targets.

[0104] In the second embodiment, the present application provides an underwater target detection data analysis system for underwater equipment, referring to Figure 4 As shown in FIG, this figure is a module structure diagram of the data analysis system shown in this embodiment of the present application, and the data analysis system includes:

[0105] The multi-source acquisition module 100 is used to obtain the inertial navigation data set of the underwater equipment and collect multi-modal detection signals through the multi-source detection equipment of the underwater equipment;

[0106] an error compensation module 200, configured to construct a phase correction matrix using the inertial navigation data set, and perform delay error compensation on the detection signal based on the phase correction matrix to obtain a compensation frequency band subset;

[0107] A target spectrum construction module 300 is configured to construct a wavenumber domain spatial spectrum according to the central wavenumber of each compensation frequency band in the compensation frequency band subset, and then perform inversion distribution mapping on the wavenumber domain spatial spectrum to obtain a target distribution model;

[0108] An observation update module 400 is configured to perform coupled estimation of the detection and navigation information of the underwater equipment based on the target distribution model to obtain a state result and an observation vector during underwater detection, and then filter and update the state result according to the observation vector to obtain a confidence tracking result;

[0109] The trajectory enhancement module 500 is configured to perform time domain smoothing and target enhancement operations on the confidence tracking result to obtain an enhanced trajectory of the underwater target.

[0110] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0111] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program. The program can be stored in a computer-readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disc storage, magnetic disk storage, or magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0112] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

Claims

1. A method for analyzing underwater target detection data for underwater equipment, characterized in that: The data analysis method comprises the following steps: Acquire inertial navigation data sets from underwater equipment and collect multi-modal detection signals through the underwater equipment's multi-source detection equipment; constructing a phase correction matrix using the inertial navigation data set, and performing time delay error compensation on the detection signal based on the phase correction matrix to obtain a compensation frequency band subset; constructing a wavenumber domain spatial spectrum according to the central wavenumber of each compensation frequency band in the compensation frequency band subset, and then performing inversion distribution mapping on the wavenumber domain spatial spectrum to obtain a target distribution model; performing coupled estimation on the detection and navigation information of the underwater equipment based on the target distribution model to obtain a state result and an observation vector during underwater detection, and then filtering and updating the state result according to the observation vector to obtain a confidence tracking result; Performing temporal smoothing and target enhancement operations on the confidence tracking result to obtain an enhanced trajectory of the underwater target; The inversion distribution mapping of the wavenumber domain spatial spectrum is performed to obtain the target distribution model specifically includes: Performing two-dimensional weighted processing on the wavenumber domain spatial spectrum to obtain a complex spectrum; Inverting the complex spectrum into a spatial domain, and then performing distribution mapping on the spatial domain to obtain an inversion distribution map; Amplitude-based noise suppression is performed on the inversion distribution map to obtain a target distribution model.

2. The underwater target detection data analysis method for underwater equipment according to claim 1, characterized in that: The inertial navigation data set of the underwater equipment is obtained through the inertial navigation database of the underwater equipment management platform.

3. The underwater target detection data analysis method for underwater equipment according to claim 1, characterized in that: The multi-source detection equipment includes: a vector hydrophone array, a sound velocity profiler and a multi-channel broadband sonar.

4. The underwater target detection data analysis method for underwater equipment according to claim 1, characterized in that: Constructing a phase correction matrix using the inertial navigation data set specifically includes: Extracting attitude angle and velocity information of the underwater equipment from the inertial navigation data set; Correcting the channel response in the sonar array using the attitude angle and the velocity information to obtain a channel phase offset; A phase correction matrix is ​​constructed based on the channel phase offsets.

5. The underwater target detection data analysis method for underwater equipment according to claim 1, characterized in that: Performing delay error compensation on the detection signal based on the phase correction matrix to obtain a compensated frequency band subset specifically includes: Converting the detection signal into the frequency domain to obtain a frequency domain signal; Dividing the frequency domain signal into a plurality of sub-band signals based on a time delay characteristic; Rotational phase compensation is performed on each sub-band signal using the phase correction matrix to obtain a compensated frequency band subset.

6. The underwater target detection data analysis method for underwater equipment according to claim 1, characterized in that: Constructing a wavenumber domain spatial spectrum according to the center wavenumber of each compensation frequency band in the compensation frequency band subset specifically includes: determining a center wave number of each compensation frequency band in the compensation frequency band subset; For each compensation frequency band, the frequency band data of the compensation frequency band is normalized by the central wave number of the compensation frequency band, thereby obtaining a wave number domain distribution diagram; The wave number domain distribution map is spatially corrected based on an underwater sound propagation model to obtain a wave number domain spatial map.

7. The underwater target detection data analysis method for underwater equipment according to claim 1, characterized in that: The detection and navigation information of the underwater equipment is coupled and estimated based on the target distribution model to obtain the state results and observation vectors during underwater detection. Specifically, the following steps are performed: The detection and navigation information of underwater equipment is filtered based on Kalman filtering to obtain the observation vector during underwater detection; The target distribution model is used to perform nonlinear state estimation on the detection and navigation information of the underwater equipment and the observation vector to obtain the state result during underwater detection.

8. The underwater target detection data analysis method for underwater equipment according to claim 1, characterized in that: Performing temporal smoothing and target enhancement operations on the confidence tracking result to obtain an enhanced trajectory of the underwater target specifically includes: Performing time domain smoothing on the confidence tracking result by a filtering algorithm to obtain a smooth tracking result; The target is enhanced by performing target enhancement on the smooth tracking result based on a compressed sensing reconstruction algorithm to obtain an enhanced trajectory of the underwater target.

9. An underwater target detection data analysis system for underwater equipment, used to execute the underwater target detection data analysis method for underwater equipment according to any one of claims 1 to 8, characterized in that: The data analysis system includes: Multi-source acquisition module, used to obtain inertial navigation data sets of underwater equipment and collect multi-modal detection signals through multi-source detection equipment of underwater equipment; an error compensation module, configured to construct a phase correction matrix using the inertial navigation data set, and perform time delay error compensation on the detection signal based on the phase correction matrix to obtain a compensation frequency band subset; a target spectrum construction module, configured to construct a wavenumber domain spatial spectrum according to the central wavenumber of each compensation frequency band in the compensation frequency band subset, and then perform inversion distribution mapping on the wavenumber domain spatial spectrum to obtain a target distribution model; An observation update module is used to perform coupled estimation on the detection and navigation information of the underwater equipment based on the target distribution model to obtain a state result and an observation vector during underwater detection, and then filter and update the state result according to the observation vector to obtain a confidence tracking result; The trajectory enhancement module is used to perform time domain smoothing and target enhancement operations on the confidence tracking result to obtain an enhanced trajectory of the underwater target.

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