Manifold constraint-based sonar imaging reverberation elimination method and system

Abstract

The invention provides a manifold constraint-based sonar imaging reverberation elimination method and system, and is applied to the field of imaging data processing. According to the method, the time domain waveform of the echo signal is collected in real time, whether the irregular reflection peak value is detected or not is judged, potential interference and reflection in the underwater environment can be rapidly recognized, and particularly, in the complex underwater environment, non-target objects (such as seabed obstacles or sundries) possibly cause irregular reflection signals, so that the interference and reflection in the underwater environment can be rapidly recognized. Meanwhile, the underwater propagation characteristics of sound waves can be accurately modeled by collecting the reflection path of the echo signal and predicting the propagation parameters of the reverberation signal. And accurate background data can be provided for the propagation modeling of the reverberation signal through the pre-recorded information of submarine topography, geological characteristics, sound wave propagation characteristics and the like.

Description

Technical Field

[0001] The present invention relates to the field of imaging data processing, and in particular to a sonar imaging reverberation elimination method and system based on manifold constraints. Background Art

[0002] In order to effectively monitor underwater targets, active sonar has become the main detection tool. Active sonar transmits sound waves to the underwater environment and receives echo signals reflected by the target to obtain the target's position, distance, speed and characteristic information. Compared with passive sonar, which only relies on the noise emitted by the target itself for detection, active sonar can actively transmit signals to improve the detection capability of stationary or low-noise targets, and is suitable for complex underwater environments.

[0003] However, active sonar often needs to work in an environment with complex noise and reverberation, where reverberation is formed by multiple scattering of sonar signals by a large number of scatterers in the ocean. In shallow water areas, reverberation caused by seabed reflection is the main source of interference, especially its strong scattering characteristics and the similarity of target echo signals, which significantly limits the target detection performance of active sonar systems. Summary of the invention

[0004] The present invention aims to solve the problem of how to reduce the reverberation caused by seabed reflection and improve the target detectability of active sonar, and provides a sonar imaging reverberation elimination method and system based on manifold constraints.

[0005] The present invention adopts the following technical means to solve the technical problem:

[0006] The present invention provides a sonar imaging reverberation elimination method based on manifold constraints, comprising:

[0007] Based on the echo signal of the target pre-monitored by the active sonar, the time domain waveform of the echo signal is collected;

[0008] Determining whether a preset number of irregular reflection peaks are detected in the time domain waveform;

[0009] If yes, then according to the pre-recorded characteristic information, the reflection path of the echo signal is collected, and according to the reflection path, the propagation parameters of the reverberation signal are predicted, and the detection threshold of the active sonar for the target is dynamically adjusted through the reflection path and the propagation parameters, wherein the characteristic information specifically includes seabed topography, geological characteristics and acoustic wave characteristics;

[0010] Determining whether the propagation parameters can be modeled and eliminated;

[0011] If possible, then based on the preset basic modeling information of the active sonar, the sound speed distribution data of the sound wave underwater is obtained, and according to the sound speed distribution data, the multipath propagation parameters of the sound wave are generated, and according to the multipath propagation parameters, the features to be eliminated of each path are modeled, wherein the basic modeling information specifically includes hard bottom, soft bottom and rock, the multipath propagation specifically includes the direct path from the sound source to the target and the indirect path after underwater reflection, and the features to be eliminated specifically include the time delay characteristics, frequency changes and amplitude attenuation of each path.

[0012] Furthermore, the step of collecting the reflection path of the echo signal and predicting the propagation parameters of the reverberation signal according to the reflection path further includes:

[0013] Calculating the propagation delay of the reflection path based on the sound speed profile preset by the active sonar;

[0014] Determining whether the propagation delay is greater than a preset delay threshold;

[0015] If so, the offset frequency of the echo signal is obtained according to the relative speed of sound wave propagation, and the attenuation characteristics of the reverberation signal are calculated according to the coefficient characteristics pre-recorded by the active sonar, wherein the coefficient characteristics specifically include the sound absorption coefficient of the water body, the reflection coefficient of the seabed and the attenuation coefficient of the distance.

[0016] Furthermore, before the step of dynamically adjusting the detection threshold of the active sonar for the target through the reflection path and the propagation parameter, the step further includes:

[0017] Based on the monitoring type of the target, collecting the reflection characteristics of the target, wherein the monitoring type specifically includes small targets, large targets and dynamic targets, and the reflection characteristics specifically include reflection intensity, shape, size and material;

[0018] determining whether the monitoring type matches the reflection characteristic;

[0019] If so, obtain the environmental noise monitored in real time, distinguish the difference data between the noise interference and the target echo from the environmental noise, and apply the preset Wiener filter to suppress the corresponding noise information based on the difference data, wherein the noise information specifically includes background noise and underwater environmental noise.

[0020] Furthermore, the step of obtaining the sound velocity distribution data of the sound wave underwater and generating the multipath propagation parameters of the sound wave according to the sound velocity distribution data further includes:

[0021] Based on the distribution law of the sound velocity distribution data in different water layers, identifying the influence coefficients of the different water layers on the sound waves, wherein the influence coefficients specifically include flow velocity coefficient, water temperature coefficient and salinity coefficient;

[0022] Determining whether the influence coefficient causes a change in the sound wave propagation path;

[0023] If so, the measured distance between the active sonar and the target is obtained according to the depth information of the different water layers, and the positioning accuracy of the target underwater is dynamically corrected according to the measured distance. The sonar parameters of the active sonar are adaptively adjusted through the positioning accuracy, wherein the sonar parameters specifically include transmission power, signal frequency and receiving sensitivity.

[0024] Furthermore, the step of determining whether a preset number of irregular reflection peaks are detected in the time domain waveform further includes:

[0025] Calculating the time delay distance of the irregular reflection peak based on the position parameter of the irregular reflection peak in the time domain waveform;

[0026] Determining whether the time delay distance matches actual sound wave propagation characteristics;

[0027] If so, collect the persistence of the irregular reflection peak, generate a change cycle of the irregular reflection peak based on the persistence, identify the variability of a single peak within a preset time period from the change cycle, and collect the corresponding environmental noise based on the variability.

[0028] Furthermore, the step of determining whether the propagation parameters can be modeled and eliminated further includes:

[0029] Based on the underwater environment and the target position, reconstructing the reverberation signal, wherein the signal reconstruction specifically includes time domain reconstruction and frequency domain reconstruction;

[0030] Determining whether the actual signal of the active sonar reaches a preset matching value of the signal reconstruction;

[0031] If so, the mean square error between the reconstructed signal and the actual signal is calculated, and the correlation coefficient between the reconstructed signal and the actual signal is obtained according to the mean square error. The information sharing degree between the reconstructed signal and the actual signal is reflected according to the correlation coefficient.

[0032] Furthermore, the step of collecting the time domain waveform of the echo signal based on the active sonar pre-monitoring of the target also includes:

[0033] Identifying noise sources in the echo signal, wherein the noise sources specifically include background noise, electronic noise and environmental interference;

[0034] Determining whether the noise source matches the underwater features pre-recorded by the active sonar;

[0035] If not, the detection parameters of the active sonar are dynamically adjusted based on the noise source, wherein the detection parameters specifically include propagation loss, reflection interface and scattering coefficient.

[0036] The present invention also provides a sonar imaging reverberation elimination system based on manifold constraints, comprising:

[0037] An acquisition module, used for acquiring a time domain waveform of an echo signal pre-monitored by active sonar to a target;

[0038] A judging module, used for judging whether a preset number of irregular reflection peaks are detected in the time domain waveform;

[0039] an execution module, configured to, if yes, collect the reflection path of the echo signal according to the pre-recorded characteristic information, predict the propagation parameters of the reverberation signal according to the reflection path, and dynamically adjust the detection threshold of the active sonar for the target through the reflection path and the propagation parameters, wherein the characteristic information specifically includes seabed topography, geological characteristics and acoustic wave characteristics;

[0040] A second judgment module is used to judge whether the propagation parameters can be modeled and eliminated;

[0041] The second execution module is used for, if possible, obtaining the sound speed distribution data of the sound wave underwater based on the basic modeling information preset by the active sonar, generating the multipath propagation parameters of the sound wave according to the sound speed distribution data, and modeling the features to be eliminated of each path according to the multipath propagation parameters, wherein the basic modeling information specifically includes hard bottom, soft bottom and rock, the multipath propagation specifically includes the direct path from the sound source to the target and the indirect path after underwater reflection, and the features to be eliminated specifically include the time delay characteristics, frequency change and amplitude attenuation of each path.

[0042] Furthermore, the execution module also includes:

[0043] A calculation unit, configured to calculate the propagation delay of the reflection path based on the sound speed profile preset by the active sonar;

[0044] A judging unit, configured to judge whether the propagation delay is greater than a preset delay threshold;

[0045] The execution unit is used to obtain the offset frequency of the echo signal according to the relative speed of the sound wave propagation, and calculate the attenuation characteristics of the reverberation signal according to the coefficient characteristics pre-recorded by the active sonar, wherein the coefficient characteristics specifically include the sound absorption coefficient of the water body, the reflection coefficient of the seabed and the attenuation coefficient of the distance.

[0046] Furthermore, it also includes:

[0047] A second acquisition module is used to acquire the reflection characteristics of the target based on the monitoring type of the target, wherein the monitoring type specifically includes small targets, large targets and dynamic targets, and the reflection characteristics specifically include reflection intensity, shape, size and material;

[0048] A third judgment module is used to judge whether the monitoring type matches the reflection characteristic;

[0049] The third execution module is used to obtain the environmental noise monitored in real time, distinguish the difference data between the noise interference and the target echo from the environmental noise, and apply a preset Wiener filter to suppress the corresponding noise information according to the difference data, wherein the noise information specifically includes background noise and underwater environmental noise.

[0050] The present invention provides a sonar imaging reverberation elimination method and system based on manifold constraints, which has the following beneficial effects:

[0051] The present invention can quickly identify potential interference and reflection in underwater environments by real-time acquisition of the time domain waveform of the echo signal and judging whether an irregular reflection peak is detected. In particular, in complex underwater environments, non-target objects (such as seabed obstacles or debris) may cause irregular reflection signals. At the same time, by acquiring the reflection path of the echo signal and predicting the propagation parameters of the reverberation signal, the propagation characteristics of the sound wave underwater can be accurately modeled. In particular, the seabed reflection signal, and through the pre-recorded information such as the seabed topography, geological characteristics and sound wave propagation characteristics, accurate background data can be provided for the propagation modeling of the reverberation signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a cross-sectional view of the overall structure of an embodiment of the sonar imaging reverberation elimination method based on manifold constraints of the present invention;

[0053] Figure 2 It is a partial structural diagram of an embodiment of the sonar imaging reverberation elimination system based on manifold constraints of the present invention. DETAILED DESCRIPTION

[0054] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. The implementation of the objectives, functional features and advantages of the present invention will be further described in conjunction with the embodiments and with reference to the accompanying drawings.

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] Reference Figure 1 , is a sonar imaging reverberation elimination method based on manifold constraints in one embodiment of the present invention, comprising:

[0057] S1: Based on the echo signal of the target pre-monitored by the active sonar, the time domain waveform of the echo signal is collected;

[0058] S2: Determine whether a preset number of irregular reflection peaks are detected in the time domain waveform;

[0059] S3: If yes, then according to the pre-recorded characteristic information, the reflection path of the echo signal is collected, and according to the reflection path, the propagation parameters of the reverberation signal are predicted, and the detection threshold of the active sonar for the target is dynamically adjusted through the reflection path and the propagation parameters, wherein the characteristic information specifically includes seabed topography, geological characteristics and acoustic wave characteristics;

[0060] S4: Determine whether the propagation parameters can be modeled and eliminated;

[0061] S5: If possible, then based on the basic modeling information preset by the active sonar, the sound speed distribution data of the sound wave underwater is obtained, and according to the sound speed distribution data, the multipath propagation parameters of the sound wave are generated, and according to the multipath propagation parameters, the features to be eliminated of each path are modeled, wherein the basic modeling information specifically includes hard bottom, soft bottom and rock, the multipath propagation specifically includes a direct path from the sound source to the target and an indirect path after underwater reflection, and the features to be eliminated specifically include the time delay characteristics, frequency changes and amplitude attenuation of each path.

[0062] In this embodiment, the system pre-monitors the echo signals of the target underwater based on active sonar, collects the time domain waveforms of these echo signals, and then the system determines whether the time domain waveform detects a preset number of irregular reflection peaks to execute the corresponding steps; for example, when the system determines that the time domain waveform of the echo signal does not detect a preset number of irregular reflection peaks, the system will consider that the echo signal is relatively simple and is not significantly interfered by factors such as seabed reflections, stray objects or multipath propagation. The system will continue to perform target detection according to the normal process, enhance the amplification processing of the target signal, and adjust the sensitivity of the sonar system to capture the target echo. , further accurately analyze the time-frequency characteristics of the signal to ensure the detection of the target, and dynamically adjust the detection threshold of the system to make it more sensitive to weaker signals, especially in low signal-to-noise ratio environments; for example, when the system determines that the time domain waveform of the echo signal detects a pre-set number of irregular reflection peaks, the system will then believe that the echo signal is significantly interfered by factors such as seabed reflections. The system will collect the reflection path of the echo signal based on the pre-recorded characteristic information, which specifically includes the seabed topography, geological characteristics, and acoustic characteristics, and predict the propagation parameters of the reverberation signal based on different reflection paths. , dynamically adjust the detection threshold of active sonar for targets; the system can identify reverberation signals caused by seabed reflections or other stray reflections by detecting irregular reflection peaks. Such reflections may come from changes in seabed topography, geological characteristics (such as soft bottom, hard bottom, rocks, etc.) or sound wave propagation characteristics (such as water flow, temperature, salinity changes, etc.). After identifying these reflection interference signals, the system can use the reflection path information to accurately distinguish target signals from non-target signals, thereby reducing the impact of reverberation on target detection. At the same time, by analyzing different reflection paths and propagation parameters, the system can dynamically adjust the target detection threshold in real time to cope with changes in different underwater environments, such as In areas with strong seabed reflections, the system may appropriately increase the threshold to avoid misjudging these reflected signals as target signals. In areas with relatively clean environments, the system may lower the threshold to improve the sensitivity of target detection. Using the reflection path and propagation parameter information, the system can identify the characteristics of reverberation, and then adopt appropriate filtering and denoising techniques to reduce reverberation interference. By analyzing the characteristics of time delay, frequency change and amplitude attenuation on the propagation path, the system can more accurately identify which signals belong to reverberation, thereby performing effective denoising and signal enhancement. The system then determines whether the propagation parameters of the reverberation signal can be modeled and eliminated to execute the corresponding steps.For example, when the system determines that the propagation parameters of the reverberation signal cannot be modeled and eliminated, the system will consider that the signal propagation path is subject to strong interference (such as reflections from other underwater objects, marine biological activities, bubbles or noise sources, etc.), and the reverberation signal may be mixed in the target echo signal, resulting in the system being unable to accurately model the propagation path of these interference factors. The system will introduce a multipath propagation model and ultra-high-resolution echo analysis technology to further refine the analysis of the reflection path and try to distinguish the interference signal from the target echo. At the same time, in a complex interference environment, by fusing the data of multiple sonar sensors or combining underwater acoustic image data, it provides more accurate reverberation signal recognition and removal capabilities, such as using Sonar detection information of different frequencies and directions is used to enhance the signal differentiation; for example, when the system determines that the propagation parameters of the reverberation signal can be modeled and eliminated, the system will believe that the signal propagation path is not significantly interfered with and can model the propagation path of the interference factor. The system will obtain the sound velocity distribution data of the sound wave underwater based on the basic modeling information pre-set by the active sonar. The basic modeling information specifically includes hard bottom, soft bottom and rock. Based on these sound velocity distribution data, the multipath propagation parameters of the sound wave are generated. The multipath propagation specifically includes the direct path from the sound source to the target and the indirect path after underwater reflection. According to the multipath propagation parameters, the features to be eliminated of each path are modeled. The features to be eliminated are specific Including the time delay characteristics, frequency changes and amplitude attenuation of each path; by combining the underwater sound speed distribution data and the characteristics of different bottom materials (hard bottom, soft bottom and rock), the system can more accurately model the path of sound wave propagation underwater. This precise path modeling can help the system identify the propagation characteristics of the signal, including the direct path and the indirect reflection path, so as to effectively distinguish the reverberation from the target echo. At the same time, by modeling the multipath propagation parameters (such as time delay, frequency change, amplitude attenuation, etc.), the system can identify the reverberation characteristics on each path and perform targeted elimination. Specifically, the system can analyze the propagation characteristics of different paths to build an accurate elimination model to reduce the impact of reverberation and Based on the modeling of multipath propagation parameters, the system can dynamically adjust the threshold of target detection. By identifying the propagation characteristics of different paths, the system can adaptively adjust the threshold to avoid misjudging targets in complex underwater environments. For example, in areas with many reflection paths or strong signal attenuation, the system can improve the recognition ability of target signals by adjusting the threshold. By acquiring and using underwater sound velocity distribution data, the system can better adapt to the dynamic changes of the underwater environment (such as temperature, salinity, and flow rate changes). These changes will affect the speed and path of sound wave propagation and the reflection characteristics of the signal. By acquiring sound velocity data in real time and adjusting modeling parameters, the system can quickly respond to environmental changes and optimize the signal processing process. ;

[0063] It should be noted that the reflection path of the echo signal is collected, and the propagation parameters of the reverberation signal are predicted according to the reflection path. The detection threshold of the active sonar for the target is dynamically adjusted through the reflection path and the propagation parameters. The specific examples are as follows:

[0064] Assume that a submarine uses active sonar to detect targets in the ocean, and the environmental conditions are:

[0065] Water depth: 500 meters;

[0066] Bottom: The seabed is soft (silt layer);

[0067] Obstacles: underwater rocks and reefs;

[0068] Target: A submerged enemy submarine;

[0069] First, the reflection path of the echo signal is collected. The submarine's active sonar equipment emits a beam of sound waves and receives the echo signal reflected from the underwater target. At this time, the echo signal will go through the following reflection paths:

[0070] Direct path: Sound waves travel directly from the source (the submarine's sonar transmitter) to the target (the enemy submarine) and reflect back to the receiver;

[0071] Indirect path: A portion of the sound waves are reflected by the soft bottom of the seabed, propagate to the target again, and then reflect back; the echo of this path usually has a large time delay because the sound waves need to propagate through the seabed;

[0072] In this step, the system determines the propagation path of the echo signal through the arrival time difference of the echo signals collected by multiple array receivers; the delay differences of these paths help the system distinguish between direct paths and indirect paths;

[0073] Then the propagation parameters of the reverberation signal are predicted. By analyzing the propagation path of the echo signal, the system predicts the propagation parameters of the reverberation signal based on the following factors:

[0074] Delay: The echo signal of the indirect path has a large delay due to reflection from the seabed. By calculating the propagation speed of the sound wave and the path distance, the system can determine the delay.

[0075] Frequency change: As sound waves are reflected by the seabed, especially soft bottom, the frequency of the sound waves may change, especially when there is a significant speed difference between the mud layer and the water layer on the seabed; this frequency shift will affect the characteristics of the echo signal and thus affect the recognition of the target signal;

[0076] Amplitude attenuation: Since sound waves will attenuate due to absorption and scattering by water during propagation, especially signals reflected by soft bottoms, the attenuation is more significant. The system can estimate the amplitude attenuation of each path by analyzing the signal strength of different paths.

[0077] Then dynamically adjust the target detection threshold. Once the system predicts the propagation parameters of the reverberation signal, the next step is to dynamically adjust the target detection threshold:

[0078] Influence of delay characteristics: The echo signal of the indirect path has a longer delay, which means that the arrival time of the echo is different from that of the direct path. The system can set an appropriate delay window based on the delay information of the echo to distinguish between the valid target echo and the interference signal from other paths. If the system detects that the delay matches the delay of the target echo, the system will lower the threshold and enhance the detection sensitivity of the target signal.

[0079] Effect of frequency change: If it is predicted that the echo signal frequency will shift due to the soft bottom reflection, the system will adjust the frequency range to adapt to this change to avoid misjudging the reverberation signal with a large frequency shift as the target signal; the system can set a frequency width threshold to ensure that only the echo signal with a frequency matching the target signal is accepted;

[0080] Impact of amplitude attenuation: Since soft bottom reflection usually causes the amplitude attenuation of echo signals, the system dynamically adjusts the detection threshold by estimating the attenuation degree; if the amplitude attenuation of the echo signal is large, the system will lower the threshold of target detection in order to detect the possibly weaker target echo signal; if the attenuation is small, the threshold will be increased to avoid misjudging weak background noise as the target echo;

[0081] Optimized target detection: By accurately modeling the time delay, frequency variation, and amplitude attenuation of multipath signals, the system successfully identified the submarine’s echo signal and avoided the reverberation interference caused by the soft bottom reflection. After dynamically adjusting the detection threshold, the detection sensitivity of the target signal was effectively improved.

[0082] Reduce false alarms: By adjusting the threshold, the system avoids misjudging weak echoes caused by seabed reflections as target signals, reducing the occurrence of false alarms and ensuring more accurate submarine detection;

[0083] In summary, the system collects the reflection path of the echo signal, predicts the propagation parameters of the reverberation signal, and dynamically adjusts the detection threshold based on these parameters. The system can effectively identify the target echo in a complex underwater environment, reduce the interference of reverberation, and enhance the reliability of target detection.

[0084] It should be added that, based on the preset basic modeling information of the active sonar, the sound velocity distribution data of the sound wave underwater is obtained, and the multipath propagation parameters of the sound wave are generated according to the sound velocity distribution data. Based on the multipath propagation parameters, the features to be eliminated of each path are modeled. The specific examples are as follows:

[0085] Assume that the submarine detects underwater obstacles through active sonar, and the environmental conditions are:

[0086] Water depth: 300 meters;

[0087] Water body: deep sea waters, where the salinity and temperature of seawater are relatively stable;

[0088] Bottom: The seabed is soft (silt layer);

[0089] Obstacles: A large rock (need to be avoided);

[0090] First, obtain the sound velocity distribution data of sound waves underwater. When sound waves propagate underwater, their speed is affected by many factors, including water temperature, salinity, and depth. The system needs to obtain accurate sound velocity distribution data based on underwater environmental conditions, mainly including:

[0091] Water layer distribution: The propagation speed of sound waves is different in water layers of different depths, especially in deep sea areas, where changes in water temperature and salinity may lead to significant differences in sound speed;

[0092] Seabed characteristics: The material of the seabed (soft bottom, hard bottom, rock, etc.) also affects the propagation speed of sound waves;

[0093] The system can obtain sound speed distribution data through sound speed profile measurement tools (such as sound speed detectors) or from existing ocean data sets; suppose we measure the sound speed at a depth of 300 meters to be 1500 m / s, and the sound speed increases slightly with increasing depth;

[0094] Then, the multipath propagation parameters of the sound wave are generated according to the sound velocity distribution data. Based on the sound velocity distribution data, the system generates the multipath propagation parameters of the sound wave. After the sound wave is emitted from the transmitter, it may propagate to the receiver along different paths, mainly including:

[0095] Direct path: directly from the sound source to the target and back again;

[0096] Indirect path: The sound waves are reflected by the seabed or underwater obstacles (such as rocks, ships, etc.), then propagate to the target and reflect back to the receiver;

[0097] For each propagation path, the following parameters are calculated:

[0098] Delay: The propagation time of each path; different paths have different propagation times, with direct paths usually having shorter propagation times and indirect paths having longer propagation times due to reflections;

[0099] Frequency change: During the propagation of sound waves, the frequency may change due to the difference in propagation distance and medium; especially after reflection from the seabed, the frequency may be attenuated or offset;

[0100] Amplitude attenuation: The signal strength on each path is weakened due to scattering, absorption and reflection losses during the propagation process; direct paths usually have less amplitude attenuation, while indirect paths (especially those passing through the seabed) may have greater attenuation;

[0101] For example, in this example, assuming that the sound wave directly propagates to the target obstacle, after calculation, the sound wave propagation delay is 0.2 seconds, the amplitude attenuation is -3 dB, and the frequency change is small (only ±0.5%); the sound wave first reflects from the seabed and then propagates to the target obstacle, with a delay of 0.5 seconds, an amplitude attenuation of -8 dB, and a large frequency change (±5%);

[0102] Then, based on the multipath propagation parameters, the features to be eliminated of each path are modeled. Once the multipath propagation parameters are obtained, the system can model the features to be eliminated of each path based on these parameters, mainly including:

[0103] Delay characteristics: Since the propagation time of different paths is different, the system needs to adjust the signal receiving time window according to the delay characteristics of each path to avoid signal aliasing of different paths; for the echo signal of the indirect path, the system may delay the acquisition to ensure that the direct path signal and the indirect path signal are not mistakenly identified as the same target echo at the same time;

[0104] Frequency variation characteristics: Sound waves will produce frequency shifts during propagation due to different media, especially when reflected by the seabed; the system needs to model these frequency changes and correct the frequency during signal processing to avoid signal errors caused by frequency changes;

[0105] Amplitude attenuation characteristics: The amplitude attenuation of the signal can be corrected by the system's built-in propagation model; the system compensates the received echo signal according to the attenuation coefficient of different paths, thereby restoring a more realistic target signal; for indirect path signals reflected by the seabed, the system can perform gain compensation according to the attenuation model;

[0106] Finally, the reverberation is eliminated dynamically and the target signal detection is optimized. By modeling the characteristics to be eliminated for each path, the system can effectively eliminate the interference of the reverberation on the target signal and enhance the clarity of the signal.

[0107] Delay correction: The system will compensate for signal delay based on the delay characteristics of the path to ensure that signals from different paths are correctly aligned;

[0108] Frequency correction: Through frequency correction, the system will remove the frequency offset caused by seabed reflection, making the frequencies of the target signal and reverberation signal more consistent, avoiding false alarms caused by frequency drift;

[0109] Amplitude compensation: The system compensates for signals on different paths based on the attenuation characteristics of the propagation path to improve the detectability of weak signals;

[0110] As a result, the accuracy of target detection is improved: by dynamically eliminating the reverberation signal caused by multipath propagation, the system can effectively and clearly identify the echo signal of the target obstacle, avoiding the interference of seabed reflection or other obstacles; eliminating the signal error caused by time delay, frequency change and amplitude attenuation, ensuring that the target echo signal is clear and discernible;

[0111] In summary, the system can efficiently optimize the processing of echo signals by generating multipath propagation parameters based on sound speed distribution data and modeling the features to be eliminated for each path based on these parameters, thereby improving the accuracy and stability of target detection in complex underwater environments; this method is particularly suitable for environments with large changes in seabed sediments and many obstacles.

[0112] In this embodiment, the step S3 of collecting the reflection path of the echo signal and predicting the propagation parameters of the reverberation signal according to the reflection path further includes:

[0113] S31: Calculating the propagation delay of the reflection path based on the sound speed profile preset by the active sonar;

[0114] S32: Determine whether the propagation delay is greater than a preset delay threshold;

[0115] S33: If so, then the offset frequency of the echo signal is obtained according to the relative speed of the sound wave propagation, and the attenuation characteristics of the reverberation signal are calculated according to the coefficient characteristics pre-recorded by the active sonar, wherein the coefficient characteristics specifically include the sound absorption coefficient of the water body, the reflection coefficient of the seabed and the attenuation coefficient of the distance.

[0116] In this embodiment, the system calculates the propagation delay of the reflection path based on the sound velocity profile preset by the active sonar, and then the system determines whether the propagation delay is greater than the preset delay threshold to execute the corresponding steps; for example, when the system determines that the propagation delay of the reflection path is not greater than the preset delay threshold, the system will consider that the signal has not been reflected by a complex underwater environment (such as the seabed, rocks or obstacles, etc.), and therefore will not produce a complex multipath effect or severe reverberation. The system will appropriately lower the detection threshold to ensure that even if the target signal is weak (due to the close distance and low signal strength), it can be detected in time. , while increasing the threshold for false alarms and reducing the chance of false alarms. For example, the system may believe that the target is more reliable within this delay range, so it may no longer pay attention to interference sources (such as noise, clutter, etc.), and maintain the current target monitoring state, and be ready to enter a more complex monitoring mode at any time to deal with subsequent complex situations (such as changes in target position, changes in the seabed, etc.); for example, when the system determines that the propagation delay of the reflection path is greater than the preset delay threshold, the system will believe that the signal has been reflected by a complex underwater environment, and the system will obtain the offset frequency of the echo signal based on the relative speed of the sound wave propagation. , according to the coefficient characteristics pre-recorded by the active sonar, the coefficient characteristics specifically include the sound absorption coefficient of the water body, the reflection coefficient of the seabed and the attenuation coefficient of the distance, the attenuation characteristics of the reverberation signal are calculated; by judging that the propagation delay is greater than the set threshold, the system can accurately distinguish whether the signal has passed through a complex underwater reflection environment. These reflection signals usually become more complex due to the influence of multipath propagation, seabed reflection, rocks and other obstacles, resulting in reverberation, delay and other characteristics. The system can identify these characteristics and make corresponding optimization processing. At the same time, by calculating the attenuation characteristics of the reverberation signal, the system can identify and eliminate the interference of factors such as multipath effects and seabed reflections on the signal, thereby improving the detection accuracy of the target. Especially in complex environments, the system can accurately eliminate the errors caused by the underwater environment and improve the quality of the signal, thereby avoiding incorrect target identification or false alarms. In addition, by relying on pre-recorded coefficient characteristics (such as sound absorption coefficient, reflection coefficient, etc.), the system can dynamically adapt to changes in different underwater environments. The water quality, seabed geological characteristics and obstacle distribution in different regions may cause changes in signal propagation characteristics. By calculating and adjusting the attenuation characteristics, the system can adaptively adjust the signal processing strategy, thereby ensuring the efficient operation of the system in various environments.

[0117] It should be noted that, according to the relative speed of the sound wave propagation, the offset frequency of the echo signal is obtained, and according to the coefficient characteristics pre-recorded by the active sonar, the attenuation characteristics of the reverberation signal are calculated. The specific examples are as follows:

[0118] Assume that an active sonar is used to detect a target (such as a submarine) in the ocean. The submarine is in relative motion with the sonar transmitter, and during the signal propagation, the sound wave passes through the complex seabed environment, resulting in reflection and attenuation. The following are the specific steps for calculating the attenuation of the reverberation signal based on the above characteristics:

[0119] First, the echo signal is obtained. The system receives the echo signal returned from the submarine and detects the offset frequency of the signal through frequency analysis. Assuming that the original frequency of the echo is 10 kHz, the system calculates that the frequency offset is 200 Hz, which means that the submarine has a certain movement (relative speed) relative to the sonar source.

[0120] The frequency offset (Δf) can be calculated by the following formula:

[0121]

[0122] in, is the relative speed between the sound source and the target, is the speed of sound in water, is the original frequency of the sound wave;

[0123] By calculation,

[0124] This shows that the relative speed of the submarine is 10 m / s, which causes the echo frequency to shift by 66.67 Hz;

[0125] Calculate the attenuation characteristics: Assuming that the sound absorption coefficient of the water body is 0.02 dB / m, the reflection coefficient of the seabed is 0.8 (hard bottom), and the target is 1000 meters away from the sound source, based on the attenuation coefficient of distance (assuming it is 0.05 dB / m), the attenuation of the signal can be calculated:

[0126] Sound absorption attenuation = 0.02 dB / m × 1000 m = 20 dB;

[0127] Reflection attenuation = (1-0.8) × 10 dB = 2 dB;

[0128] Total attenuation = 20 dB + 2 dB = 22 dB;

[0129] Correct the reverberation signal. Based on the attenuation characteristic (22 dB), the system corrects the signal strength to make it closer to the echo signal of the real target. The attenuation characteristic helps the system identify which parts of the signal are strongly absorbed and attenuated, so as to decide on which frequencies and paths to strengthen or compensate.

[0130] In summary, by obtaining the offset frequency of the echo signal and calculating the attenuation characteristics of the reverberation signal according to the coefficient characteristics, the system can accurately identify the signal propagation path and the signal attenuation factor, and dynamically adjust the signal processing strategy, for example, enhance weak signals, weaken interference signals, and improve detection accuracy to ensure the effective extraction of target signals.

[0131] In this embodiment, before step S3 of dynamically adjusting the detection threshold of the active sonar for the target by using the reflection path and the propagation parameter, the method further includes:

[0132] S301: Based on the monitoring type of the target, collecting reflection characteristics of the target, wherein the monitoring type specifically includes small targets, large targets and dynamic targets, and the reflection characteristics specifically include reflection intensity, shape, size and material;

[0133] S302: Determine whether the monitoring type matches the reflection characteristic;

[0134] S303: If yes, then obtain the environmental noise monitored in real time, distinguish the difference data between the noise interference and the target echo from the environmental noise, and apply a preset Wiener filter to suppress the corresponding noise information based on the difference data, wherein the noise information specifically includes background noise and underwater environmental noise.

[0135] In this embodiment, the system collects the reflection characteristics of the target based on the monitoring type of the target, which specifically includes small targets, large targets and dynamic targets. The reflection characteristics specifically include reflection intensity, shape, size and material. Then the system determines whether the monitoring type of the target matches the reflection characteristics of the target to execute the corresponding steps. For example, when the system determines that the monitoring type of the target cannot match the reflection characteristics of the target, the system will consider that the reflection characteristics of the target do not match the expected monitoring type. This may be because the physical properties, shape or material characteristics of the target do not match the set monitoring model. The system will automatically switch to a more suitable monitoring mode based on the reflection characteristics of the current target. For example, the system originally set to monitor large targets will switch to a more suitable monitoring mode. If the target is a small object, the system can switch to a monitoring type more suitable for small targets, adjust the detection sensitivity and algorithm parameters, and if the target is non-metal or sound-absorbing material, the system can increase or adjust the sound absorption coefficient, reflection coefficient and other parameters to make the model more in line with the reflection characteristics of the actual target. For targets with irregular or special shapes, the system can perform morphological analysis and adjust the morphological recognition model of the echo signal to adapt to the reflection pattern of irregular targets. In addition to sonar echo signals, the system can perform data fusion through other sensors (such as radar, infrared or optical sensors) to help improve the accuracy of target recognition, especially in complex environments, when the sonar echo signal cannot clearly identify the target. It is supplemented by information from other sensors (such as the target's thermal radiation, changes in reflected waveforms, etc.), so as to more accurately infer the characteristics and type of the target; for example, when the system determines that the monitoring type of the target can match the target's reflection characteristics, the system will consider that the target's reflection characteristics are consistent with the expected monitoring type, and the system will obtain the real-time monitored environmental noise, and from these environmental noises, distinguish the difference data between noise interference and target echoes. According to different difference data, the pre-set Wiener filter is applied to suppress the corresponding noise information, and the noise information specifically includes background noise and underwater environmental noise; the system can accurately identify noise by obtaining environmental noise information in real time and distinguishing the difference between noise and echo. Interference sources can be effectively reduced, which is particularly important for dynamic targets and target detection in complex environments. At the same time, through the dynamic application of Wiener filtering, the system can adjust the noise suppression strategy according to different environmental noise characteristics. For example, the characteristics of background noise and underwater environmental noise are different. The system can adjust the intensity and method of noise suppression according to the real-time environmental monitoring data to ensure the optimization of the noise suppression effect. After the target echo signal is noise suppressed, the system can perform further target identification, tracking, classification and other processing based on the cleaner signal. The improvement of signal quality provides a more accurate basis for subsequent decision-making, which helps to improve the work efficiency and accuracy of the entire system.

[0136] It should be noted that the environmental noise monitored in real time is obtained, and the difference data between the noise interference and the target echo is distinguished from the environmental noise. According to the difference data, a preset Wiener filter is applied to suppress the corresponding noise information. The specific examples are as follows:

[0137] Assume that the system is monitoring a submarine underwater. The system transmits sound waves through active sonar and receives echo signals reflected by the submarine. Underwater, the target echo signal is usually interfered by environmental noise (such as ocean currents, ship activities, seabed reflections, etc.), which affects the signal quality and reduces the accuracy of target detection.

[0138] First, collect environmental noise. The system first collects environmental noise signals through sonar sensors. Noise may come from different sources, such as:

[0139] Ship noise: Low-frequency noise, usually occurring between 20 Hz and 500 Hz, which may be caused by the mechanical equipment of surface ships or submarines;

[0140] Current noise: high-frequency noise, usually occurring between 2kHz and 5kHz, caused by the interaction of water currents and seafloor structures;

[0141] Assume that the system collects a 10-second signal segment in real time, which includes the submarine echo signal and the ocean current and ship noise;

[0142] Then the system distinguishes the difference between noise and target echo signals. The system distinguishes noise and submarine echo by analyzing the spectral characteristics of the signal:

[0143] Submarine echo signals usually exhibit frequency stability and may have significant time delay and amplitude attenuation. For example, the reflected echo of a submarine may be mainly concentrated between 1kHz and 3kHz, and its amplitude will gradually decay as the distance increases.

[0144] Ship noise is mainly low-frequency (20 Hz to 500 Hz) and exhibits strong randomness;

[0145] Ocean current noise is usually high frequency (2kHz to 5kHz) noise with a relatively wide spectrum and usually no obvious periodicity;

[0146] Through these features, the system can distinguish the spectrum differences between submarine echo signals and noise; for example, the delay of submarine echo signals is about 3 seconds, while the delay of noise is random and irregular;

[0147] Then the Wiener filter is applied. After clarifying the spectral difference between the noise and the echo signal, the system starts to apply the Wiener filter to suppress the noise. The Wiener filter optimizes its coefficients based on the power spectral density of the signal and the noise to eliminate the noise.

[0148] Step 1: Estimate the power spectral density (PSD) of the noise. The system first estimates the power spectral density of the noise. Assume that the system uses the signal over a period of time to estimate the statistical characteristics of the noise (i.e., the power spectrum). Assume that:

[0149] The power spectrum of ship noise is concentrated between 20 Hz and 500 Hz;

[0150] The power spectrum of ocean current noise is concentrated between 2kHz and 5kHz;

[0151] Assume that the system can obtain such a noise power spectrum through the noise model:

[0152] Low frequency noise (20Hz-500Hz): power spectrum is P_noise_low=0.5;

[0153] High frequency noise (2kHz-5kHz): power spectrum is P_noise_high=0.2;

[0154] Step 2: Estimate the power spectrum of the target echo. The system then estimates the power spectrum of the submarine echo signal. Assuming that the spectrum of the submarine echo signal is concentrated between 1kHz and 3kHz, and the delay characteristics of the signal are relatively clear, as the reflection distance increases, the signal will have a certain frequency attenuation (for example, in the high frequency band of 3kHz, the amplitude of the echo signal will attenuate more than at 1kHz). The system estimates the power spectrum of the submarine echo signal: P_echo=1 is in the range of 1kHz to 3kHz.

[0155] Step 3: Optimize the Wiener filter. Based on the estimated signal and noise power spectra, the Wiener filter will be optimized. The goal of the Wiener filter is to minimize the mean square error between the signal and the noise, that is, to maximize the retention of the submarine echo signal while suppressing the noise by calculating the gain function of the filter.

[0156] For example, for echo signals from 1kHz to 3kHz, the Wiener filter will give a higher gain, assuming the gain is G_echo=1 (i.e., it does not change the signal), while for ocean current noise from 2kHz to 5kHz, the filter will reduce the gain, assuming the gain is G_noise_high=0.1;

[0157] Step 4: Filter and enhance the echo signal. According to the optimized filter coefficients, the system will filter the original signal to remove the noise part while retaining the target echo signal;

[0158] For echo signals in the range of 1kHz to 3kHz, the system will completely retain the signals in these frequency bands;

[0159] For noise in the range of 20Hz to 500Hz and 2kHz to 5kHz, the system will suppress the signal strength in these frequency bands;

[0160] After Wiener filtering, the target echo signal becomes clearer and the noise part is effectively removed; for example, the frequency attenuation characteristics of the submarine echo signal are retained, and the time delay and frequency changes can be more clearly reflected; the noise part (such as noise from ships and ocean currents) is effectively suppressed, especially in the low-frequency and high-frequency bands, and the impact of noise is greatly reduced;

[0161] Finally, the enhanced signal is detected. The enhanced signal will greatly improve the signal-to-noise ratio of the target echo; the system can more accurately extract the submarine's position, speed, reflection intensity and other information; for example:

[0162] The intensity and position of a submarine's reflection can be accurately estimated;

[0163] The system can track the movement of submarines, further improving the accuracy of target detection and tracking;

[0164] In summary, the system can effectively remove underwater noise and enhance target echo signals through Wiener filtering; in this case, Wiener filtering effectively removes the low-frequency noise of the ship and the high-frequency noise of the ocean current, making the submarine's echo signal clearer; in this way, the system can detect and track submarines more accurately, significantly improving the accuracy and reliability of target detection; this noise suppression method is very important for target monitoring in complex underwater environments and can greatly improve the performance of active sonar.

[0165] In this embodiment, the step S5 of acquiring the sound velocity distribution data of the sound wave underwater and generating the multipath propagation parameters of the sound wave according to the sound velocity distribution data further includes:

[0166] S51: Based on the distribution law of the sound velocity distribution data in different water layers, identifying the influence coefficients of the different water layers on the sound waves, wherein the influence coefficients specifically include a velocity coefficient, a water temperature coefficient and a salinity coefficient;

[0167] S52: Determine whether the influence coefficient causes a change in the sound wave propagation path;

[0168] S53: If so, then according to the depth information of the different water layers, the measured distance between the active sonar and the target is obtained, and based on the measured distance, the positioning accuracy of the target underwater is dynamically corrected, and the sonar parameters of the active sonar are adaptively adjusted through the positioning accuracy, wherein the sonar parameters specifically include transmission power, signal frequency and receiving sensitivity.

[0169] In this embodiment, the system identifies the influence coefficients of different water layers on sound waves based on the distribution law of sound velocity distribution data in different water layers. The influence coefficients specifically include flow velocity coefficient, water temperature coefficient and salinity coefficient. Then the system determines whether these influence coefficients cause changes in the sound wave propagation path to execute corresponding steps. For example, when the system determines that the influence coefficients of different water layers on sound waves do not cause changes in the sound wave propagation path, the system will believe that changes in parameters such as flow velocity, water temperature and salinity in the water layer have not significantly changed the propagation mode of the sound wave. The system will continue to use the current propagation model for target detection, and there is no need to adjust the sound wave propagation path. That is, the system can continue to perform conventional echo signal processing, target identification and tracking tasks, while saving computing resources, without additional path correction or data correction, and directly enter target detection and tracking, thereby improving real-time performance. In addition, if no significant path changes are found, the system can ensure the monitoring stability under existing environmental conditions, and there is no need to frequently respond to environmental changes. The system can maintain stable operation and focus on target detection and echo analysis. For example, when the system determines that the influence coefficients of different water layers on sound waves cause changes in the sound wave propagation path, At this time, the system will think that the water layer has significantly changed the propagation mode of sound waves. The system will obtain the measured distance between the active sonar and the target according to the depth information of different water layers. Based on these measured distances, the positioning accuracy of the target underwater is dynamically corrected. Through different positioning accuracies, the sonar parameters of the active sonar are adaptively adjusted. The sonar parameters specifically include transmission power, signal frequency and receiving sensitivity. The system can optimize the sonar performance and provide the best signal quality under different water layer conditions by adaptively adjusting the transmission power, signal frequency and receiving sensitivity of the active sonar according to the positioning accuracy. At the same time, according to the actual position and positioning accuracy of the target, the system can optimize the transmission power and receiving sensitivity of the sound wave, thereby improving the signal-to-noise ratio of the echo signal and reducing the interference of environmental noise on the signal, which is crucial for target detection and recognition, especially in the case of strong background noise and complex underwater environment. In addition, dynamic adjustment of sonar parameters can not only improve positioning accuracy and detection performance, but also save unnecessary power and computing resources. For example, in areas with small underwater signal attenuation, the system can appropriately reduce the transmission power and reduce energy consumption, while in areas with large signal attenuation, the system will enhance the signal to ensure reliable detection of the target.

[0170] In this embodiment, the step S2 of determining whether a preset number of irregular reflection peaks are detected in the time domain waveform further includes:

[0171] S21: Calculating the time delay distance of the irregular reflection peak based on the position parameter of the irregular reflection peak in the time domain waveform;

[0172] S22: Determine whether the time delay distance matches the actual sound wave propagation characteristics;

[0173] S23: If yes, collect the persistence of the irregular reflection peak, generate a variation cycle of the irregular reflection peak based on the persistence, identify the variability of a single peak within a preset time period from the variation cycle, and collect the corresponding environmental noise based on the variability.

[0174] In this embodiment, the system calculates the time delay distance of the irregular reflection peak based on the position parameter of the irregular reflection peak in the time domain waveform, and then the system determines whether the time delay distance matches the actual sound wave propagation characteristics to execute the corresponding steps; for example, when the system determines that the time delay distance of the irregular reflection peak cannot match the actual sound wave propagation characteristics, the system will believe that the reflection path of the echo signal may be affected by external interference or environmental factors, resulting in the propagation characteristics of the reflected signal not being consistent with expectations. The system will correct the sound speed distribution according to the real-time data obtained by the underwater sensor, thereby adjusting the sound wave propagation delay calculation to avoid errors caused by environmental changes. At the same time, check the working status of the active sonar equipment to see if there is any equipment failure or configuration error, which causes the delay of the reflection peak to fail to match the theoretical value. If the equipment is operating normally, but the delay still does not match, check whether it is necessary to adjust the sonar signal's transmission power, frequency, receiving sensitivity and other parameters. If the delay calculation of the irregular reflection peak continues to fail to match the actual propagation characteristics, the system can enable the target tracking function, combine the target's motion state and historical trajectory, infer the target's position and propagation path, and correct the delay calculation. For example, when the system determines that the delay distance of the irregular reflection peak can match the actual sound wave propagation characteristics, the system The system will believe that the reflection path of the echo signal is not affected by external interference, etc. The system will collect the persistence of the irregular reflection peak, and based on the persistence, generate the change cycle of the irregular reflection peak, identify the variability of a single peak within a preset time from the change cycle, and collect the corresponding environmental noise based on different variabilities; the system can ensure that the reflection path of the echo signal is not interfered by external factors by confirming that the delay of the irregular reflection peak is consistent with the actual propagation characteristics, which is crucial for accurately judging the target position and sound wave propagation path. At the same time, analyzing the persistence, change cycle and variability of the irregular reflection peak is helpful The system identifies possible regular features in the echo signal. Based on these features, the system can determine whether the echo signal has continuous periodic or non-periodic fluctuations, and by monitoring the changing period of the irregular reflection peak, the system can identify which peak changes are related to environmental noise and which are related to target reflection. The system can capture the changing features related to environmental noise based on the differences identified in the changing period, and by accurately analyzing the changing period of the irregular reflection peak and its matching with the target echo, the system can more clearly distinguish the difference between the target signal and the environmental noise, thereby improving the accuracy and response speed of target positioning.

[0175] In this embodiment, the step S4 of determining whether the propagation parameters can be modeled and eliminated further includes:

[0176] S41: reconstructing the reverberation signal based on the underwater environment and the target position, wherein the signal reconstruction specifically includes time domain reconstruction and frequency domain reconstruction;

[0177] S42: Determine whether the actual signal of the active sonar reaches the preset matching value of the signal reconstruction;

[0178] S43: If yes, then calculate the mean square error between the reconstructed signal and the actual signal, obtain the correlation coefficient between the reconstructed signal and the actual signal according to the mean square error, and reflect the information sharing degree between the reconstructed signal and the actual signal according to the correlation coefficient.

[0179] In this embodiment, the system reconstructs the reverberation signal based on the underwater environment and the target position. The signal reconstruction specifically includes time domain reconstruction and frequency domain reconstruction. Then the system determines whether the actual signal of the active sonar reaches the preset matching value of the signal reconstruction to execute the corresponding steps; for example, when the system determines that the actual signal of the active sonar does not reach the preset matching value of the signal reconstruction, the system will think that it means that there are some deviations that do not meet expectations in the process of signal reconstruction, which may be due to errors or external interference in the signal processing process. The system will re-collect environmental parameters and dynamically adjust the propagation model of the sonar. At the same time, based on new environmental data, such as underwater sound speed profile and propagation path, the time domain or frequency domain parameters in the signal reconstruction are adjusted in real time to perform adaptive correction, and based on the real-time echo signal, the propagation parameters such as the reflection path, propagation delay and attenuation coefficient are re-estimated, thereby optimizing the signal reconstruction process; for example, when the system determines that the actual signal of the active sonar reaches the preset matching value of the signal reconstruction, the system will think that the process of signal reconstruction is as expected, and the system will calculate the mean square error between the reconstructed signal and the actual signal, and obtain the reconstruction according to the mean square error. The correlation coefficient between the signal and the actual signal reflects the information sharing degree between the reconstructed signal and the actual signal based on these correlation coefficients; the system can quantify the accuracy of signal reconstruction by calculating the mean square error (MSE) between the reconstructed signal and the actual signal. The smaller the mean square error, the smaller the gap between the signal reconstruction and the actual echo signal, and the system can more accurately restore the propagation characteristics of the signal. At the same time, by obtaining the correlation coefficient between the reconstructed signal and the actual signal, the system can quantify the linear correlation between them. The closer the correlation coefficient is to 1, the more consistent the characteristics of the reconstructed signal and the actual signal are, and the information sharing degree is high. When the signal reconstruction process meets expectations and the mean square error and correlation coefficient are within the ideal range, the system can determine that the reconstruction process is effective and reliable, which provides a solid foundation for subsequent signal analysis, target detection, noise elimination, etc., and according to the correlation between the reconstructed signal and the actual signal, the system can further adjust and optimize the parameters in the signal processing process (such as transmission power, frequency, receiving sensitivity, etc.). If the correlation is found to be low, the system will adjust the sonar operation mode in time and optimize the signal processing algorithm to better match the actual signal characteristics.

[0180] It should be noted that the mean square error between the reconstructed signal and the actual signal is calculated, and the correlation coefficient between the reconstructed signal and the actual signal is obtained according to the mean square error. According to the correlation coefficient, the information sharing degree between the reconstructed signal and the actual signal is reflected. The specific examples are as follows:

[0181] Assume that in an underwater environment, the system uses sonar to send signals and receive echo signals. After certain signal processing, the system reconstructs the echo signal to obtain a reconstructed signal. Now, it is necessary to compare the similarity between the reconstructed signal and the actual received echo signal to evaluate the quality of signal reconstruction.

[0182] First, calculate the mean square error (MSE). The mean square error (MSE) is a standard for measuring the difference between two signals (the actual signal and the reconstructed signal). It calculates the difference between the actual signal and the reconstructed signal at each sampling point, and squares and averages these differences. The formula is as follows:

[0183]

[0184] in, is the total number of sampling points of the signal;

[0185] is the i-th sampling point value of the actual signal;

[0186] is the i-th sampling point value of the reconstructed signal;

[0187] Assume that the first sample point values ​​of the actual signal and the reconstructed signal are:

[0188] Actual signal: ;

[0189] Reconstruct the signal: ;

[0190] If the signal length is 5, the actual signal and the reconstructed signal are:

[0191] Actual signal: [10,15,20,25,30];

[0192] Reconstructed signal: [8,16,19,26,29];

[0193] Then the mean square error is calculated as:

[0194]

[0195] Then use the correlation coefficient formula to calculate the correlation between the two signals. The correlation coefficient is an indicator that measures the degree of linear correlation between two signals. Its value range is between [-1,1]. The closer the value is to 1, the higher the correlation between the two signals. The closer the value is to -1, the lower the correlation between the two signals. The closer the value is to 0, the lower the correlation between the two signals. The formula for calculating the correlation coefficient is:

[0196]

[0197] in, and is the mean of the actual signal and the reconstructed signal; the value of the correlation coefficient r reflects the similarity between the reconstructed signal and the actual signal. When r is close to 1, it means that the two are highly consistent and the reconstruction effect is good. When the correlation coefficient is negative or close to zero, it means that the similarity between the reconstructed signal and the actual signal is low, and the signal reconstruction algorithm may need to be adjusted.

[0198] Assume that the means of the actual signal and the reconstructed signal are:

[0199]

[0200]

[0201] Substitute the above data into the formula to calculate the correlation coefficient r:

[0202]

[0203] Assuming that the calculated correlation coefficient is r=0.98, according to the results, the mean square error MSE=1.6 and the correlation coefficient r=0.98, the following conclusions can be drawn: the mean square error is small, indicating that the difference between the reconstructed signal and the actual signal is small, and the reconstruction effect is good, while the correlation coefficient is high (close to 1), indicating that the reconstructed signal is highly consistent with the actual signal, the information sharing degree is high, and the system can effectively restore the characteristics of the echo signal.

[0204] In this embodiment,

[0205] In this embodiment, the step S1 of collecting the time domain waveform of the echo signal based on the active sonar pre-monitoring of the target further includes:

[0206] S11: Identifying noise sources in the echo signal, wherein the noise sources specifically include background noise, electronic noise and environmental interference;

[0207] S12: Determine whether the noise source matches the underwater features pre-recorded by the active sonar;

[0208] S13: If not, dynamically adjusting the detection parameters of the active sonar based on the noise source, wherein the detection parameters specifically include propagation loss, reflection interface and scattering coefficient.

[0209] In this embodiment, the system identifies the source of noise in the echo signal, which specifically includes background noise, electronic noise and environmental interference, and then determines whether these noise sources match the underwater features pre-recorded by the active sonar to execute the corresponding steps; for example, when the system determines that the source of noise in the echo signal can match the underwater features pre-recorded by the active sonar, the system will consider that the noise in the echo signal may be known environmental noise, and can take preset noise suppression measures to process it according to the existing underwater features and noise characteristics. The system will identify the source of noise in the echo signal (such as background noise, electronic noise, Environmental interference, etc.), by comparing with the pre-recorded underwater noise characteristics, determine its source. For example, if the identified noise matches the underwater current noise or equipment electronic noise preset by the system, the system can infer that the noise does not come from the target echo itself, but from the environmental noise. At the same time, according to the frequency range, amplitude characteristics and time domain characteristics of the noise, the corresponding filtering algorithm is used to remove or reduce noise interference to ensure that the target echo signal is purer. If the noise matches the known characteristics, the system can also dynamically adjust the signal processing parameters (such as the sampling rate of the signal, the window size of the filter, the gain, etc.) to make the signal processing process more accurate; for example When the system determines that the source of noise in the echo signal cannot match the underwater features pre-recorded by the active sonar, the system will consider the noise in the echo signal to be other unknown noise. Based on the source of the noise, the system will dynamically adjust the detection parameters of the active sonar. The detection parameters specifically include propagation loss, reflection interface and scattering coefficient. By identifying unknown noise sources and adjusting the sonar detection parameters in a timely manner, the system can respond to new environmental changes, reduce the interference of unknown noise sources on the signal, and improve noise recognition and processing capabilities. At the same time, dynamic adjustment of detection parameters (such as propagation loss, reflection interface and scattering coefficient) can help the system adapt to different Environmental conditions and target characteristics, optimize the quality of the received echo signal, thereby improving the accuracy of target detection, and adjust the working parameters according to the real-time noise source changes, enhancing the adaptability of active sonar in complex or dynamic underwater environments. For example, in a complex underwater environment, the existence of marine life, changing ocean currents and other factors will introduce unknown noise. By adjusting the system's detection parameters, the sonar equipment can work more reliably, and by adapting to different noise sources and dynamically adjusting the detection parameters, the system can reduce noise interference, ensure the extraction of more target information, and improve detection sensitivity and recognition accuracy, especially in changeable or complex underwater environments.

[0210] Reference Figure 2 , is a sonar imaging reverberation elimination system based on manifold constraints in one embodiment of the present invention, comprising:

[0211] The acquisition module 10 is used to acquire the time domain waveform of the echo signal based on the echo signal of the target pre-monitored by the active sonar;

[0212] A determination module 20, configured to determine whether a preset number of irregular reflection peaks are detected in the time domain waveform;

[0213] An execution module 30 is used for, if yes, collecting the reflection path of the echo signal according to the pre-recorded characteristic information, predicting the propagation parameters of the reverberation signal according to the reflection path, and dynamically adjusting the detection threshold of the active sonar for the target through the reflection path and the propagation parameters, wherein the characteristic information specifically includes seabed topography, geological characteristics and acoustic wave characteristics;

[0214] A second judgment module 40 is used to judge whether the propagation parameters can be modeled and eliminated;

[0215] The second execution module 50 is used to obtain the sound speed distribution data of the sound wave underwater based on the basic modeling information preset by the active sonar, if possible, generate the multipath propagation parameters of the sound wave according to the sound speed distribution data, and model the features to be eliminated of each path according to the multipath propagation parameters, wherein the basic modeling information specifically includes hard bottom, soft bottom and rock, the multipath propagation specifically includes the direct path from the sound source to the target and the indirect path after underwater reflection, and the features to be eliminated specifically include the time delay characteristics, frequency changes and amplitude attenuation of each path.

[0216] In this embodiment, the acquisition module 10 collects the time domain waveforms of the echo signals based on the active sonar pre-monitoring of the target underwater, and then the judgment module 20 judges whether the time domain waveform detects a preset number of irregular reflection peaks to execute the corresponding steps; for example, when the system determines that the time domain waveform of the echo signal does not detect a preset number of irregular reflection peaks, the system will consider that the echo signal is relatively simple and is not significantly interfered by factors such as seabed reflections, stray objects or multipath propagation. The system will continue to perform target detection according to the normal process, enhance the amplification processing of the target signal, and adjust the sensitivity of the sonar system to capture The target echo is further accurately analyzed in terms of the time-frequency characteristics of the signal to ensure the detection of the target, and the detection threshold of the system is dynamically adjusted to make it more sensitive to weaker signals, especially in a low signal-to-noise ratio environment; for example, when the system determines that the time domain waveform of the echo signal detects a preset number of irregular reflection peaks, the execution module 30 will consider that the echo signal is significantly interfered by factors such as seabed reflection. The system will collect the reflection path of the echo signal based on the pre-recorded characteristic information, which specifically includes the seabed topography, geological characteristics and acoustic characteristics, and predict the propagation parameters of the reverberation signal based on different reflection paths. The system can dynamically adjust the detection threshold of active sonar for targets by detecting irregular reflection peaks. The system can identify reverberation signals caused by seabed reflections or other stray reflections. Such reflections may come from changes in seabed topography, geological characteristics (such as soft bottom, hard bottom, rocks, etc.) or sound wave propagation characteristics (such as water flow, temperature, salinity changes, etc.). After identifying these reflection interference signals, the system can use the reflection path information to accurately distinguish target signals from non-target signals, thereby reducing the impact of reverberation on target detection. At the same time, by analyzing different reflection paths and propagation parameters, the system can dynamically adjust the target detection threshold in real time to cope with changes in different underwater environments, such as in In areas with strong seabed reflections, the system may appropriately increase the threshold to avoid misjudging these reflected signals as target signals, while in areas with relatively clean environments, the system may lower the threshold to improve the sensitivity of target detection. The system can identify the characteristics of reverberation by using the reflection path and propagation parameter information, and then adopt appropriate filtering and denoising techniques to reduce reverberation interference. By analyzing the characteristics of time delay, frequency change and amplitude attenuation on the propagation path, the system can more accurately identify which signals belong to reverberation, thereby performing effective denoising and signal enhancement. Then the second judgment module 40 determines whether the propagation parameters of the reverberation signal can be modeled and eliminated, so as to execute the corresponding steps.For example, when the system determines that the propagation parameters of the reverberation signal cannot be modeled and eliminated, the system will consider that the signal propagation path is subject to strong interference (such as reflections from other underwater objects, marine biological activities, bubbles or noise sources, etc.), and the reverberation signal may be mixed in the target echo signal, causing the system to be unable to accurately model the propagation path of these interference factors. The system will introduce multipath propagation models and ultra-high resolution echo analysis technology to further refine the analysis of the reflection path and try to distinguish the interference signal from the target echo. At the same time, in complex interference environments, by fusing data from multiple sonar sensors or combining underwater acoustic image data, it provides more accurate reverberation signal recognition and removal capabilities, such as using different The system uses sonar detection information of different frequencies and directions to enhance the signal differentiation; for example, when the system determines that the propagation parameters of the reverberation signal can be modeled and eliminated, the second execution module 50 will believe that the signal propagation path is not significantly interfered with and can model the propagation path of the interference factor. The system will obtain the underwater sound velocity distribution data of the sound wave based on the pre-set basic modeling information of the active sonar, which specifically includes hard bottom, soft bottom and rock. According to these sound velocity distribution data, the multipath propagation parameters of the sound wave are generated. The multipath propagation specifically includes the direct path from the sound source to the target and the indirect path after underwater reflection. According to the multipath propagation parameters, the features to be eliminated of each path are modeled, and the features to be eliminated are The characteristics specifically include the time delay characteristics, frequency changes and amplitude attenuation of each path; by combining the underwater sound speed distribution data and the characteristics of different bottom materials (hard bottom, soft bottom and rock), the system can more accurately model the path of sound wave propagation underwater. This precise path modeling can help the system identify the propagation characteristics of the signal, including direct paths and indirect reflection paths, so as to effectively distinguish reverberation from target echoes. At the same time, by modeling multipath propagation parameters (such as time delay, frequency change, amplitude attenuation, etc.), the system can identify the reverberation characteristics on each path and perform targeted elimination. Specifically, the system can analyze the propagation characteristics of different paths to build an accurate elimination model to reduce the impact of reverberation. , and based on the modeling of multipath propagation parameters, the system can dynamically adjust the threshold of target detection. By identifying the propagation characteristics of different paths, the system can adaptively adjust the threshold to avoid misjudging targets in complex underwater environments. For example, in areas with many reflection paths or strong signal attenuation, the system can improve the recognition ability of target signals by adjusting the threshold. By acquiring and using underwater sound velocity distribution data, the system can better adapt to the dynamic changes of the underwater environment (such as temperature, salinity, and flow rate changes). These changes will affect the speed and path of sound wave propagation and the reflection characteristics of the signal. By acquiring sound velocity data in real time and adjusting modeling parameters, the system can quickly respond to environmental changes and optimize the signal processing process. ;

[0217] In this embodiment, the execution module further includes:

[0218] A calculation unit, configured to calculate the propagation delay of the reflection path based on the sound speed profile preset by the active sonar;

[0219] A judging unit, configured to judge whether the propagation delay is greater than a preset delay threshold;

[0220] The execution unit is used to obtain the offset frequency of the echo signal according to the relative speed of the sound wave propagation, and calculate the attenuation characteristics of the reverberation signal according to the coefficient characteristics pre-recorded by the active sonar, wherein the coefficient characteristics specifically include the sound absorption coefficient of the water body, the reflection coefficient of the seabed and the attenuation coefficient of the distance.

[0221] In this embodiment, the system calculates the propagation delay of the reflection path based on the sound velocity profile preset by the active sonar, and then the system determines whether the propagation delay is greater than the preset delay threshold to execute the corresponding steps; for example, when the system determines that the propagation delay of the reflection path is not greater than the preset delay threshold, the system will consider that the signal has not been reflected by a complex underwater environment (such as the seabed, rocks or obstacles, etc.), and therefore will not produce a complex multipath effect or severe reverberation. The system will appropriately lower the detection threshold to ensure that even if the target signal is weak (due to the close distance and low signal strength), it can be detected in time. , while increasing the threshold for false alarms and reducing the chance of false alarms. For example, the system may believe that the target is more reliable within this delay range, so it may no longer pay attention to interference sources (such as noise, clutter, etc.), and maintain the current target monitoring state, and be ready to enter a more complex monitoring mode at any time to deal with subsequent complex situations (such as changes in target position, changes in the seabed, etc.); for example, when the system determines that the propagation delay of the reflection path is greater than the preset delay threshold, the system will believe that the signal has been reflected by a complex underwater environment, and the system will obtain the offset frequency of the echo signal based on the relative speed of the sound wave propagation. , according to the coefficient characteristics pre-recorded by the active sonar, the coefficient characteristics specifically include the sound absorption coefficient of the water body, the reflection coefficient of the seabed and the attenuation coefficient of the distance, the attenuation characteristics of the reverberation signal are calculated; by judging that the propagation delay is greater than the set threshold, the system can accurately distinguish whether the signal has passed through a complex underwater reflection environment. These reflection signals usually become more complex due to the influence of multipath propagation, seabed reflection, rocks and other obstacles, resulting in reverberation, delay and other characteristics. The system can identify these characteristics and make corresponding optimization processing. At the same time, by calculating the attenuation characteristics of the reverberation signal, the system can identify and eliminate the interference of factors such as multipath effects and seabed reflections on the signal, thereby improving the detection accuracy of the target. Especially in complex environments, the system can accurately eliminate the errors caused by the underwater environment and improve the quality of the signal, thereby avoiding incorrect target identification or false alarms. In addition, by relying on pre-recorded coefficient characteristics (such as sound absorption coefficient, reflection coefficient, etc.), the system can dynamically adapt to changes in different underwater environments. The water quality, seabed geological characteristics and obstacle distribution in different regions may cause changes in signal propagation characteristics. By calculating and adjusting the attenuation characteristics, the system can adaptively adjust the signal processing strategy, thereby ensuring the efficient operation of the system in various environments.

[0222] In this embodiment, it also includes:

[0223] A second acquisition module is used to acquire the reflection characteristics of the target based on the monitoring type of the target, wherein the monitoring type specifically includes small targets, large targets and dynamic targets, and the reflection characteristics specifically include reflection intensity, shape, size and material;

[0224] A third judgment module is used to judge whether the monitoring type matches the reflection characteristic;

[0225] The third execution module is used to obtain the environmental noise monitored in real time, distinguish the difference data between the noise interference and the target echo from the environmental noise, and apply a preset Wiener filter to suppress the corresponding noise information according to the difference data, wherein the noise information specifically includes background noise and underwater environmental noise.

[0226] In this embodiment, the system collects the reflection characteristics of the target based on the monitoring type of the target, which specifically includes small targets, large targets and dynamic targets. The reflection characteristics specifically include reflection intensity, shape, size and material. Then the system determines whether the monitoring type of the target matches the reflection characteristics of the target to execute the corresponding steps. For example, when the system determines that the monitoring type of the target cannot match the reflection characteristics of the target, the system will consider that the reflection characteristics of the target do not match the expected monitoring type. This may be because the physical properties, shape or material characteristics of the target do not match the set monitoring model. The system will automatically switch to a more suitable monitoring mode based on the reflection characteristics of the current target. For example, the system originally set to monitor large targets will switch to a more suitable monitoring mode. If the target is a small object, the system can switch to a monitoring type more suitable for small targets, adjust the detection sensitivity and algorithm parameters, and if the target is non-metal or sound-absorbing material, the system can increase or adjust the sound absorption coefficient, reflection coefficient and other parameters to make the model more in line with the reflection characteristics of the actual target. For targets with irregular or special shapes, the system can perform morphological analysis and adjust the morphological recognition model of the echo signal to adapt to the reflection pattern of irregular targets. In addition to sonar echo signals, the system can perform data fusion through other sensors (such as radar, infrared or optical sensors) to help improve the accuracy of target recognition, especially in complex environments, when the sonar echo signal cannot clearly identify the target. It is supplemented by information from other sensors (such as the target's thermal radiation, changes in reflected waveforms, etc.), so as to more accurately infer the characteristics and type of the target; for example, when the system determines that the monitoring type of the target can match the target's reflection characteristics, the system will consider that the target's reflection characteristics are consistent with the expected monitoring type, and the system will obtain the real-time monitored environmental noise, and from these environmental noises, distinguish the difference data between noise interference and target echoes. According to different difference data, the pre-set Wiener filter is applied to suppress the corresponding noise information, and the noise information specifically includes background noise and underwater environmental noise; the system can accurately identify noise by obtaining environmental noise information in real time and distinguishing the difference between noise and echo. Interference sources can be effectively reduced, which is particularly important for dynamic targets and target detection in complex environments. At the same time, through the dynamic application of Wiener filtering, the system can adjust the noise suppression strategy according to different environmental noise characteristics. For example, the characteristics of background noise and underwater environmental noise are different. The system can adjust the intensity and method of noise suppression according to the real-time environmental monitoring data to ensure the optimization of the noise suppression effect. After the target echo signal is noise suppressed, the system can perform further target identification, tracking, classification and other processing based on the cleaner signal. The improvement of signal quality provides a more accurate basis for subsequent decision-making, which helps to improve the work efficiency and accuracy of the entire system.

[0227] In this embodiment, the second execution module further includes:

[0228] an identification unit, configured to identify the influence coefficients of the different water layers on the sound waves based on the distribution law of the sound velocity distribution data in different water layers, wherein the influence coefficients specifically include a velocity coefficient, a water temperature coefficient and a salinity coefficient;

[0229] A second judgment unit, used to judge whether the influence coefficient causes a change in the sound wave propagation path;

[0230] The second execution unit is used to obtain the measured distance between the active sonar and the target according to the depth information of the different water layers, dynamically correct the positioning accuracy of the target underwater according to the measured distance, and adaptively adjust the sonar parameters of the active sonar through the positioning accuracy, wherein the sonar parameters specifically include transmission power, signal frequency and receiving sensitivity.

[0231] In this embodiment, the system identifies the influence coefficients of different water layers on sound waves based on the distribution law of sound velocity distribution data in different water layers. The influence coefficients specifically include flow velocity coefficient, water temperature coefficient and salinity coefficient. Then the system determines whether these influence coefficients cause changes in the sound wave propagation path to execute corresponding steps. For example, when the system determines that the influence coefficients of different water layers on sound waves do not cause changes in the sound wave propagation path, the system will believe that changes in parameters such as flow velocity, water temperature and salinity in the water layer have not significantly changed the propagation mode of the sound wave. The system will continue to use the current propagation model for target detection, and there is no need to adjust the sound wave propagation path. That is, the system can continue to perform conventional echo signal processing, target identification and tracking tasks, while saving computing resources, without additional path correction or data correction, and directly enter target detection and tracking, thereby improving real-time performance. In addition, if no significant path changes are found, the system can ensure the monitoring stability under existing environmental conditions, and there is no need to frequently respond to environmental changes. The system can maintain stable operation and focus on target detection and echo analysis. For example, when the system determines that the influence coefficients of different water layers on sound waves cause changes in the sound wave propagation path, At this time, the system will think that the water layer has significantly changed the propagation mode of sound waves. The system will obtain the measured distance between the active sonar and the target according to the depth information of different water layers. Based on these measured distances, the positioning accuracy of the target underwater is dynamically corrected. Through different positioning accuracies, the sonar parameters of the active sonar are adaptively adjusted. The sonar parameters specifically include transmission power, signal frequency and receiving sensitivity. The system can optimize the sonar performance and provide the best signal quality under different water layer conditions by adaptively adjusting the transmission power, signal frequency and receiving sensitivity of the active sonar according to the positioning accuracy. At the same time, according to the actual position and positioning accuracy of the target, the system can optimize the transmission power and receiving sensitivity of the sound wave, thereby improving the signal-to-noise ratio of the echo signal and reducing the interference of environmental noise on the signal, which is crucial for target detection and recognition, especially in the case of strong background noise and complex underwater environment. In addition, dynamic adjustment of sonar parameters can not only improve positioning accuracy and detection performance, but also save unnecessary power and computing resources. For example, in areas with small underwater signal attenuation, the system can appropriately reduce the transmission power and reduce energy consumption, while in areas with large signal attenuation, the system will enhance the signal to ensure reliable detection of the target.

[0232] In this embodiment, the judgment module further includes:

[0233] A second calculation unit, configured to calculate a time delay distance of the irregular reflection peak value based on a position parameter of the irregular reflection peak value in the time domain waveform;

[0234] A third judgment unit is used to judge whether the delay distance matches the actual sound wave propagation characteristics;

[0235] The third execution unit is used to collect the persistence of the irregular reflection peak, generate a change cycle of the irregular reflection peak according to the persistence, identify the variability of a single peak within a preset time period from the change cycle, and collect the corresponding environmental noise according to the variability.

[0236] In this embodiment, the system calculates the time delay distance of the irregular reflection peak based on the position parameter of the irregular reflection peak in the time domain waveform, and then the system determines whether the time delay distance matches the actual sound wave propagation characteristics to execute the corresponding steps; for example, when the system determines that the time delay distance of the irregular reflection peak cannot match the actual sound wave propagation characteristics, the system will believe that the reflection path of the echo signal may be affected by external interference or environmental factors, resulting in the propagation characteristics of the reflected signal not being consistent with expectations. The system will correct the sound speed distribution according to the real-time data obtained by the underwater sensor, thereby adjusting the sound wave propagation delay calculation to avoid errors caused by environmental changes. At the same time, check the working status of the active sonar equipment to see if there is any equipment failure or configuration error, which causes the delay of the reflection peak to fail to match the theoretical value. If the equipment is operating normally, but the delay still does not match, check whether it is necessary to adjust the sonar signal's transmission power, frequency, receiving sensitivity and other parameters. If the delay calculation of the irregular reflection peak continues to fail to match the actual propagation characteristics, the system can enable the target tracking function, combine the target's motion state and historical trajectory, infer the target's position and propagation path, and correct the delay calculation. For example, when the system determines that the delay distance of the irregular reflection peak can match the actual sound wave propagation characteristics, the system The system will believe that the reflection path of the echo signal is not affected by external interference, etc. The system will collect the persistence of the irregular reflection peak, and based on the persistence, generate the change cycle of the irregular reflection peak, identify the variability of a single peak within a preset time from the change cycle, and collect the corresponding environmental noise based on different variabilities; the system can ensure that the reflection path of the echo signal is not interfered by external factors by confirming that the delay of the irregular reflection peak is consistent with the actual propagation characteristics, which is crucial for accurately judging the target position and sound wave propagation path. At the same time, analyzing the persistence, change cycle and variability of the irregular reflection peak is helpful The system identifies possible regular features in the echo signal. Based on these features, the system can determine whether the echo signal has continuous periodic or non-periodic fluctuations, and by monitoring the changing period of the irregular reflection peak, the system can identify which peak changes are related to environmental noise and which are related to target reflection. The system can capture the changing features related to environmental noise based on the differences identified in the changing period, and by accurately analyzing the changing period of the irregular reflection peak and its matching with the target echo, the system can more clearly distinguish the difference between the target signal and the environmental noise, thereby improving the accuracy and response speed of target positioning.

[0237] In this embodiment, the second determination module further includes:

[0238] A reconstruction unit, configured to reconstruct the reverberation signal based on the underwater environment and the target position, wherein the signal reconstruction specifically includes time domain reconstruction and frequency domain reconstruction;

[0239] A fourth judgment unit, used to judge whether the actual signal of the active sonar reaches a preset matching value of the signal reconstruction;

[0240] The fourth execution unit is used to calculate the mean square error between the reconstructed signal and the actual signal, obtain the correlation coefficient between the reconstructed signal and the actual signal according to the mean square error, and reflect the information sharing degree between the reconstructed signal and the actual signal according to the correlation coefficient.

[0241] In this embodiment, the system reconstructs the reverberation signal based on the underwater environment and the target position. The signal reconstruction specifically includes time domain reconstruction and frequency domain reconstruction. Then the system determines whether the actual signal of the active sonar reaches the preset matching value of the signal reconstruction to execute the corresponding steps; for example, when the system determines that the actual signal of the active sonar does not reach the preset matching value of the signal reconstruction, the system will think that it means that there are some deviations that do not meet expectations in the process of signal reconstruction, which may be due to errors or external interference in the signal processing process. The system will re-collect environmental parameters and dynamically adjust the propagation model of the sonar. At the same time, based on new environmental data, such as underwater sound speed profile and propagation path, the time domain or frequency domain parameters in the signal reconstruction are adjusted in real time to perform adaptive correction, and based on the real-time echo signal, the propagation parameters such as the reflection path, propagation delay and attenuation coefficient are re-estimated, thereby optimizing the signal reconstruction process; for example, when the system determines that the actual signal of the active sonar reaches the preset matching value of the signal reconstruction, the system will think that the process of signal reconstruction is as expected, and the system will calculate the mean square error between the reconstructed signal and the actual signal, and obtain the reconstruction according to the mean square error. The correlation coefficient between the signal and the actual signal reflects the information sharing degree between the reconstructed signal and the actual signal based on these correlation coefficients; the system can quantify the accuracy of signal reconstruction by calculating the mean square error (MSE) between the reconstructed signal and the actual signal. The smaller the mean square error, the smaller the gap between the signal reconstruction and the actual echo signal, and the system can more accurately restore the propagation characteristics of the signal. At the same time, by obtaining the correlation coefficient between the reconstructed signal and the actual signal, the system can quantify the linear correlation between them. The closer the correlation coefficient is to 1, the more consistent the characteristics of the reconstructed signal and the actual signal are, and the information sharing degree is high. When the signal reconstruction process meets expectations and the mean square error and correlation coefficient are within the ideal range, the system can determine that the reconstruction process is effective and reliable, which provides a solid foundation for subsequent signal analysis, target detection, noise elimination, etc., and according to the correlation between the reconstructed signal and the actual signal, the system can further adjust and optimize the parameters in the signal processing process (such as transmission power, frequency, receiving sensitivity, etc.). If the correlation is found to be low, the system will adjust the sonar operation mode in time and optimize the signal processing algorithm to better match the actual signal characteristics.

[0242] In this embodiment, the acquisition module further includes:

[0243] A second identification unit, used to identify a noise source in the echo signal, wherein the noise source specifically includes background noise, electronic noise and environmental interference;

[0244] A fifth judgment unit, used to judge whether the noise source matches the underwater features pre-recorded by the active sonar;

[0245] The fifth execution unit is used to dynamically adjust the detection parameters of the active sonar based on the noise source if no, wherein the detection parameters specifically include propagation loss, reflection interface and scattering coefficient.

[0246] In this embodiment, the system identifies the source of noise in the echo signal, which specifically includes background noise, electronic noise and environmental interference, and then determines whether these noise sources match the underwater features pre-recorded by the active sonar to execute the corresponding steps; for example, when the system determines that the source of noise in the echo signal can match the underwater features pre-recorded by the active sonar, the system will consider that the noise in the echo signal may be known environmental noise, and can take preset noise suppression measures to process it according to the existing underwater features and noise characteristics. The system will identify the source of noise in the echo signal (such as background noise, electronic noise, Environmental interference, etc.), by comparing with the pre-recorded underwater noise characteristics, determine its source. For example, if the identified noise matches the underwater current noise or equipment electronic noise preset by the system, the system can infer that the noise does not come from the target echo itself, but from the environmental noise. At the same time, according to the frequency range, amplitude characteristics and time domain characteristics of the noise, the corresponding filtering algorithm is used to remove or reduce noise interference to ensure that the target echo signal is purer. If the noise matches the known characteristics, the system can also dynamically adjust the signal processing parameters (such as the sampling rate of the signal, the window size of the filter, the gain, etc.) to make the signal processing process more accurate; for example When the system determines that the source of noise in the echo signal cannot match the underwater features pre-recorded by the active sonar, the system will consider the noise in the echo signal to be other unknown noise. Based on the source of the noise, the system will dynamically adjust the detection parameters of the active sonar. The detection parameters specifically include propagation loss, reflection interface and scattering coefficient. By identifying unknown noise sources and adjusting the sonar detection parameters in a timely manner, the system can respond to new environmental changes, reduce the interference of unknown noise sources on the signal, and improve noise recognition and processing capabilities. At the same time, dynamic adjustment of detection parameters (such as propagation loss, reflection interface and scattering coefficient) can help the system adapt to different Environmental conditions and target characteristics, optimize the quality of the received echo signal, thereby improving the accuracy of target detection, and adjust the working parameters according to the real-time noise source changes, enhancing the adaptability of active sonar in complex or dynamic underwater environments. For example, in a complex underwater environment, the existence of marine life, changing ocean currents and other factors will introduce unknown noise. By adjusting the system's detection parameters, the sonar equipment can work more reliably, and by adapting to different noise sources and dynamically adjusting the detection parameters, the system can reduce noise interference, ensure the extraction of more target information, and improve detection sensitivity and recognition accuracy, especially in changeable or complex underwater environments.

[0247] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A sonar imaging reverberation elimination method based on manifold constraints, characterized in that: The following steps are involved: Based on the echo signal of the target pre-monitored by the active sonar, the time domain waveform of the echo signal is collected; Determining whether a preset number of irregular reflection peaks are detected in the time domain waveform; If yes, then according to the pre-recorded characteristic information, the reflection path of the echo signal is collected, and according to the reflection path, the propagation parameters of the reverberation signal are predicted, and the detection threshold of the active sonar for the target is dynamically adjusted through the reflection path and the propagation parameters, wherein the characteristic information specifically includes seabed topography, geological characteristics and acoustic wave characteristics; Determining whether the propagation parameters can be modeled and eliminated; If possible, then based on the preset basic modeling information of the active sonar, the sound speed distribution data of the sound wave underwater is obtained, and according to the sound speed distribution data, the multipath propagation parameters of the sound wave are generated, and according to the multipath propagation parameters, the features to be eliminated of each path are modeled, wherein the basic modeling information specifically includes hard bottom, soft bottom and rock, the multipath propagation specifically includes the direct path from the sound source to the target and the indirect path after underwater reflection, and the features to be eliminated specifically include the time delay characteristics, frequency changes and amplitude attenuation of each path.

2. The sonar imaging reverberation elimination method based on manifold constraint according to claim 1 is characterized in that: The step of collecting the reflection path of the echo signal and predicting the propagation parameters of the reverberation signal according to the reflection path further includes: Calculating the propagation delay of the reflection path based on the sound speed profile preset by the active sonar; Determining whether the propagation delay is greater than a preset delay threshold; If so, the offset frequency of the echo signal is obtained according to the relative speed of sound wave propagation, and the attenuation characteristics of the reverberation signal are calculated according to the coefficient characteristics pre-recorded by the active sonar, wherein the coefficient characteristics specifically include the sound absorption coefficient of the water body, the reflection coefficient of the seabed and the attenuation coefficient of the distance.

3. The sonar imaging reverberation elimination method based on manifold constraint according to claim 1, characterized in that: Before the step of dynamically adjusting the detection threshold of the active sonar to the target through the reflection path and the propagation parameter, the method further includes: Based on the monitoring type of the target, collecting the reflection characteristics of the target, wherein the monitoring type specifically includes small targets, large targets and dynamic targets, and the reflection characteristics specifically include reflection intensity, shape, size and material; determining whether the monitoring type matches the reflection characteristic; If so, obtain the environmental noise monitored in real time, distinguish the difference data between the noise interference and the target echo from the environmental noise, and apply the preset Wiener filter to suppress the corresponding noise information based on the difference data, wherein the noise information specifically includes background noise and underwater environmental noise.

4. The sonar imaging reverberation elimination method based on manifold constraint according to claim 1, characterized in that: The step of obtaining the sound velocity distribution data of the sound wave underwater and generating the multipath propagation parameters of the sound wave according to the sound velocity distribution data further includes: Based on the distribution law of the sound velocity distribution data in different water layers, identifying the influence coefficients of the different water layers on the sound waves, wherein the influence coefficients specifically include flow velocity coefficient, water temperature coefficient and salinity coefficient; Determining whether the influence coefficient causes a change in the sound wave propagation path; If so, the measured distance between the active sonar and the target is obtained according to the depth information of the different water layers, and the positioning accuracy of the target underwater is dynamically corrected according to the measured distance. The sonar parameters of the active sonar are adaptively adjusted through the positioning accuracy, wherein the sonar parameters specifically include transmission power, signal frequency and receiving sensitivity.

5. The sonar imaging reverberation elimination method based on manifold constraint according to claim 1, characterized in that: The step of determining whether a preset number of irregular reflection peaks are detected in the time domain waveform further includes: Calculating the time delay distance of the irregular reflection peak based on the position parameter of the irregular reflection peak in the time domain waveform; Determining whether the time delay distance matches actual sound wave propagation characteristics; If so, collect the persistence of the irregular reflection peak, generate a change cycle of the irregular reflection peak based on the persistence, identify the variability of a single peak within a preset time period from the change cycle, and collect the corresponding environmental noise based on the variability.

6. The sonar imaging reverberation elimination method based on manifold constraints according to claim 1 is characterized in that: The step of determining whether the propagation parameters can be modeled and eliminated also includes: Based on the underwater environment and the target position, reconstructing the reverberation signal, wherein the signal reconstruction specifically includes time domain reconstruction and frequency domain reconstruction; Determining whether the actual signal of the active sonar reaches a preset matching value of the signal reconstruction; If so, the mean square error between the reconstructed signal and the actual signal is calculated, and the correlation coefficient between the reconstructed signal and the actual signal is obtained according to the mean square error. The information sharing degree between the reconstructed signal and the actual signal is reflected according to the correlation coefficient.

7. The sonar imaging reverberation elimination method based on manifold constraints according to claim 1 is characterized in that: The step of collecting the time domain waveform of the echo signal based on the active sonar pre-monitoring of the target also includes: Identifying noise sources in the echo signal, wherein the noise sources specifically include background noise, electronic noise and environmental interference; Determining whether the noise source matches the underwater features pre-recorded by the active sonar; If not, the detection parameters of the active sonar are dynamically adjusted based on the noise source, wherein the detection parameters specifically include propagation loss, reflection interface and scattering coefficient.

8. A sonar imaging reverberation elimination system based on manifold constraints, characterized in that: include: An acquisition module, used for acquiring a time domain waveform of an echo signal pre-monitored by active sonar to a target; A judging module, used for judging whether a preset number of irregular reflection peaks are detected in the time domain waveform; an execution module, configured to, if yes, collect the reflection path of the echo signal according to the pre-recorded characteristic information, predict the propagation parameters of the reverberation signal according to the reflection path, and dynamically adjust the detection threshold of the active sonar for the target through the reflection path and the propagation parameters, wherein the characteristic information specifically includes seabed topography, geological characteristics and acoustic wave characteristics; A second judgment module is used to judge whether the propagation parameters can be modeled and eliminated; The second execution module is used for, if possible, obtaining the sound speed distribution data of the sound wave underwater based on the basic modeling information preset by the active sonar, generating the multipath propagation parameters of the sound wave according to the sound speed distribution data, and modeling the features to be eliminated of each path according to the multipath propagation parameters, wherein the basic modeling information specifically includes hard bottom, soft bottom and rock, the multipath propagation specifically includes the direct path from the sound source to the target and the indirect path after underwater reflection, and the features to be eliminated specifically include the time delay characteristics, frequency change and amplitude attenuation of each path.

9. The sonar imaging reverberation elimination system based on manifold constraints according to claim 8, characterized in that: The execution module also includes: A calculation unit, configured to calculate the propagation delay of the reflection path based on the sound speed profile preset by the active sonar; A judging unit, configured to judge whether the propagation delay is greater than a preset delay threshold; The execution unit is used to obtain the offset frequency of the echo signal according to the relative speed of the sound wave propagation, and calculate the attenuation characteristics of the reverberation signal according to the coefficient characteristics pre-recorded by the active sonar, wherein the coefficient characteristics specifically include the sound absorption coefficient of the water body, the reflection coefficient of the seabed and the attenuation coefficient of the distance.

10. The sonar imaging reverberation elimination system based on manifold constraints according to claim 8, characterized in that: Also includes: A second acquisition module is used to acquire the reflection characteristics of the target based on the monitoring type of the target, wherein the monitoring type specifically includes small targets, large targets and dynamic targets, and the reflection characteristics specifically include reflection intensity, shape, size and material; A third judgment module is used to judge whether the monitoring type matches the reflection characteristic; The third execution module is used to obtain the environmental noise monitored in real time, distinguish the difference data between the noise interference and the target echo from the environmental noise, and apply a preset Wiener filter to suppress the corresponding noise information according to the difference data, wherein the noise information specifically includes background noise and underwater environmental noise.

Images