Method, apparatus, equipment, and medium for generating underwater target detection results based on combined waveforms

Abstract

This application discloses a method, apparatus, device, and medium for generating underwater target detection results based on combined waveforms, relating to the fields of underwater acoustic engineering and signal processing technology. The method includes: transmitting a combined waveform of a linear frequency modulated pulse signal and a single-frequency signal to an underwater region; receiving the echo signal and performing matched filtering to generate a time-frequency detection map; employing a dynamic threshold to filter the candidate peak set; constructing a two-dimensional search window based on the theoretical secondary peak interval; verifying and superimposing the secondary peak energy onto the target's main peak; and generating the target detection result. This effectively suppresses reverberation interference and improves the accuracy of underwater target detection in shallow sea environments with strong reverberation.

Description

Technical Field

[0001] This invention relates to the fields of underwater acoustic engineering and signal processing technology, and in particular to a method, apparatus, equipment and medium for generating underwater target detection results based on combined waveforms. Background Technology

[0002] Underwater acoustics engineering is the study of underwater acoustic phenomena and their applications, widely used in marine resource development, underwater communication, underwater navigation, and marine environmental monitoring. Active sonar systems are a crucial component of underwater acoustics engineering, detecting, locating, and identifying underwater targets by emitting sound waves and receiving the echo signals reflected from them. Currently, common waveform designs for active sonar systems primarily focus on single-frequency signals, broadband signals, and comb-spectrum signals. Reverberation interference is mainly addressed by estimating the reverberation background using methods such as sliding window manipulation, combined with constant false alarm rate (CFAR) algorithms for target detection. However, this approach carries the risk of falsely suppressing high-gain target echo highlights.

[0003] While current technologies have addressed some issues in underwater target detection, limitations remain in several areas. Single-frequency and broadband signals struggle to achieve both high ranging and velocity accuracy simultaneously, failing to meet the requirements of high range and velocity resolution. Comb-spectrum signals, while possessing good anti-reverberation performance, suffer from high periodic sidelobes, easily triggering false alarms and impacting target detection reliability. While reverberation suppression algorithms can reduce false alarm rates, they risk falsely suppressing target echoes, especially in strongly reverberant environments where target signals may be submerged in the reverberant region. Existing algorithms have limited accuracy in estimating the reverberant background and are ill-suited to complex underwater environments. Therefore, a novel waveform design and target detection algorithm are urgently needed to improve the accuracy of target detection results in shallow, strongly reverberant environments. Summary of the Invention

[0004] The main objective of this application is to provide a method, apparatus, device, and medium for generating underwater target detection results based on combined waveforms, aiming to solve the technical problem of how to improve the accuracy of target detection in shallow sea environments.

[0005] To achieve the above objectives, this application proposes a method for generating underwater target detection results based on combined waveforms, comprising:

[0006] Acquire linear frequency modulated pulse signals and single-frequency signals;

[0007] The linear frequency modulated pulse signal and the single-frequency signal are combined in the time domain to obtain a combined waveform signal;

[0008] The combined waveform signal is transmitted to the underwater area, and the echo signal fed back from the underwater area is received;

[0009] The echo signal is subjected to matched filtering to generate a time-frequency detection map;

[0010] The time-frequency detection graph is subjected to dynamic threshold filtering to output a candidate peak set, wherein the candidate peak set includes the target main peak and interference peaks;

[0011] The candidate peak set is searched using a two-dimensional search window constructed based on the theoretical secondary peak interval to obtain the secondary peaks;

[0012] The secondary peak is verified based on a preset attenuation rule, and a secondary peak that satisfies the preset attenuation rule is output.

[0013] The energy values ​​of the secondary peaks are superimposed onto the target main peak to generate the target detection result.

[0014] In one embodiment, the step of combining the linear frequency modulated pulse signal and the single-frequency signal in the time domain to obtain a combined waveform signal includes:

[0015] The linear frequency modulated pulse signals are combined to obtain a linear frequency modulated train pulse signal, as shown in the following formula:

[0016]

[0017] in, For rectangle functions, and These are the lowest and highest frequencies of the linear frequency modulated pulse signal, respectively. The pulse length of the linear frequency modulated pulse signal is given. The total number of combinations of the linear frequency modulated pulse signals. The number of combinations of the currently described linear frequency modulated pulse signals. Representing the phase change, the total duration of the linear frequency modulated train pulse signal is ;

[0018] The single-frequency signal is subjected to window weighting processing to obtain a windowed single-frequency signal, the specific formula of which is:

[0019]

[0020] in, For window functions, It is the time-domain length of the single-frequency signal. The frequency of the single-frequency signal;

[0021] The combined waveform signal is obtained by time-domain combination of the linear frequency modulated pulse signal and the windowed single-frequency signal, and the duration of the combined waveform signal is [duration missing]. .

[0022] In one embodiment, the step of performing matched filtering on the echo signal to generate a time-frequency detection map includes:

[0023] A filter bank is constructed by selecting multiple matched filters, and the matched filters correspond to different Doppler frequency offset copies of the transmitted signal;

[0024] The echo signal is passed through the filter bank to obtain multiple sets of time-domain output sequences;

[0025] Perform Fourier transform on the multiple time-domain output sequences to obtain multiple time-frequency distribution maps;

[0026] Arrange the multiple time-frequency distribution maps in order of frequency offset to obtain a time-frequency detection map.

[0027] In one embodiment, the step of performing dynamic threshold filtering on the time-frequency detection map and outputting a candidate peak set includes:

[0028] Obtain the pixel value corresponding to the pixel point in the time-frequency detection image;

[0029] The pixel value is compared with the dynamic threshold to obtain the comparison result;

[0030] When the comparison result is that the pixel value is greater than the dynamic threshold, the target pixel corresponding to the pixel value is output;

[0031] The positions of the target pixels in the time-frequency detection map are recorded to obtain candidate peak combinations.

[0032] In one embodiment, the step of searching the candidate peak set to obtain secondary peaks includes:

[0033] The theoretical secondary peak spacing is obtained by calculating the combined waveform signals in the candidate peak set.

[0034] A two-dimensional search window is constructed based on the theoretical secondary peak spacing;

[0035] The two-dimensional search window is searched to obtain image points with energy of a preset value;

[0036] The image points with the preset values ​​are used as secondary peaks.

[0037] In one embodiment, the step of superimposing the energy value of the secondary peak onto the target main peak to generate a target detection result includes:

[0038] The energy of the secondary peak is added to the energy value of the target main peak in a linear or nonlinear superposition method to obtain the superimposed energy value. The linear superposition method is to add the energy value of the secondary peak to the energy value of the main peak, and the nonlinear superposition method is to add the energy of the secondary peak to the energy value of the main peak through weighted superposition.

[0039] The enhanced detection result is obtained by normalizing the superimposed energy value, which includes the position of the target main peak and the superimposed energy value.

[0040] Based on the enhanced detection results, target detection results are generated.

[0041] In one embodiment, the step of generating a target detection result based on the enhanced detection result includes:

[0042] The target motion parameters are calculated based on the enhanced detection results.

[0043] Based on the target motion parameters, feature extraction is performed to obtain the target feature vector;

[0044] The target feature vector is input into a preset neural network model to obtain the target detection result, which includes the target type and the target location.

[0045] Furthermore, to achieve the above objectives, this application also proposes an underwater target detection result generation device based on combined waveforms, the underwater target detection result generation device based on combined waveforms comprising:

[0046] The acquisition module is used to acquire linear frequency modulated pulse signals and single-frequency signals;

[0047] The combination module is used to combine the linear frequency modulated pulse signal and the single frequency signal in the time domain to obtain a combined waveform signal;

[0048] A receiving module is used to transmit the combined waveform signal to the underwater area and receive the echo signal fed back from the underwater area;

[0049] The processing module is used to perform matched filtering on the echo signal to generate a time-frequency detection map;

[0050] The filtering module is used to perform dynamic threshold filtering on the time-frequency detection map and output a candidate peak set, wherein the candidate peak set includes the target main peak and interference peaks;

[0051] The search module is used to search the candidate peak set by constructing a two-dimensional search window based on the theoretical secondary peak interval to obtain the secondary peaks;

[0052] The verification module is used to verify the secondary peak based on a preset attenuation rule and output the secondary peak that satisfies the preset attenuation rule.

[0053] The results module is used to superimpose the energy values ​​of the secondary peaks onto the target main peak to generate target detection results.

[0054] In addition, to achieve the above objectives, this application also proposes a medium, which is a computer-readable medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the underwater target detection result generation method based on combined waveforms as described above.

[0055] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the underwater target detection result generation method based on combined waveforms as described above.

[0056] This application transmits a combination of linear frequency modulated pulse signals and single-frequency signals to an underwater region, receives the echo signals, and performs matched filtering to generate a time-frequency detection map. A dynamic threshold is used to filter the candidate peak set, and a two-dimensional search window is constructed based on the theoretical secondary peak interval. The energy of the secondary peaks is verified and superimposed onto the target's main peak to generate the target detection result. This effectively suppresses reverberation interference and improves the accuracy of underwater target detection in shallow, strongly reverberant environments. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 This is a flowchart illustrating the first embodiment of the underwater target detection result generation method based on combined waveforms in this application;

[0059] Figure 2 This is a time-domain waveform diagram of the first embodiment of the underwater target detection result generation method based on combined waveforms in this application;

[0060] Figure 3 This is a spectrum analysis diagram of the first embodiment of the underwater target detection result generation method based on combined waveforms in this application;

[0061] Figure 4 This is a schematic diagram of the candidate peak results after dynamic threshold filtering in the first embodiment of the underwater target detection result generation method based on combined waveforms of this application;

[0062] Figure 5This is a time-frequency diagram of the target detection result from the first embodiment of the underwater target detection result generation method based on combined waveforms in this application.

[0063] Figure 6 This is a time-frequency diagram of the energy convergence result of the first embodiment of the underwater target detection result generation method based on combined waveforms in this application;

[0064] Figure 7 This is a flowchart illustrating the second embodiment of the underwater target detection result generation method based on combined waveforms in this application;

[0065] Figure 8 This is a flowchart illustrating the third embodiment of the underwater target detection result generation method based on combined waveforms in this application;

[0066] Figure 9 This is a schematic diagram of the module structure of the underwater target detection result generation device based on combined waveforms according to the first embodiment of the underwater target detection result generation method based on combined waveforms of this application;

[0067] Figure 10 This is a schematic diagram of the hardware operating environment of the underwater target detection result generation method based on combined waveforms in the embodiments of this application.

[0068] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0069] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0070] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0071] In the field of underwater acoustics engineering, active sonar systems are a key technology for underwater target detection, localization, and identification. Their working principle involves emitting sound waves and receiving the echo signals reflected from the target to obtain relevant target information. However, in shallow sea environments, complex underwater conditions cause the sound waves emitted by the sonar to encounter various scattering sources, such as rough seabeds, undulating sea surfaces, seamounts, and schools of fish, generating a large amount of reverberation signals. These reverberation signals superimpose with the target echo signals, potentially submerging the target signal in the reverberation region and severely affecting the detection performance of active sonar.

[0072] Therefore, to overcome the above problems, this application proposes an accurate method for generating underwater target detection results based on combined waveforms. The main solution of this application's embodiments is as follows: acquiring a linear frequency modulated pulse signal and a single-frequency signal; combining the linear frequency modulated pulse signal and the single-frequency signal in the time domain to obtain a combined waveform signal; transmitting the combined waveform signal to an underwater region and receiving the echo signal fed back from the underwater region; performing matched filtering on the echo signal to generate a time-frequency detection map; performing dynamic threshold filtering on the time-frequency detection map to output a candidate peak set, wherein the candidate peak set includes the target main peak and interference peaks; constructing a two-dimensional search window based on the theoretical secondary peak interval to search the candidate peak set and obtain secondary peaks; verifying the secondary peaks based on a preset attenuation law and outputting secondary peaks that satisfy the preset attenuation law; superimposing the energy value of the secondary peaks onto the target main peak to generate the target detection result.

[0073] Based on the above, this application also provides a method for generating underwater target detection results based on combined waveforms, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the underwater target detection result generation method based on combined waveforms in this application.

[0074] In this embodiment, the underwater target detection result generation method based on combined waveforms includes steps S10~S80:

[0075] Step S10: Obtain the linear frequency modulated pulse signal and the single-frequency signal.

[0076] It should be noted that a Linear Frequency Modulated Pulse (LFM) signal is a signal whose frequency varies linearly with time. LFM signals are widely used in radar and sonar systems due to their superior range resolution. The main characteristic of this signal is its wide bandwidth, which provides high range resolution. Specifically, after processing with a matched filter, an LFM signal can estimate target velocity through the Doppler effect while maintaining high range resolution. However, due to its broadband nature, LFM signals are sensitive to frequency shifts, meaning that even small changes in velocity can cause a shift in the entire spectrum, thus affecting velocity measurement accuracy.

[0077] Continuous Wave (CW) signals, on the other hand, are signals with a fixed frequency and are typically used to measure target velocity. The advantages of CW signals lie in their simplicity and ease of generation, while also providing good velocity resolution. As the target moves relative to the sonar system, the frequency of the echo signal changes (i.e., the Doppler effect), and by analyzing this change, the target's velocity can be accurately determined. However, the limitation of CW signals is that they provide limited range information because their spectrum contains only a single frequency component, which restricts their use in applications requiring high range resolution.

[0078] Step S20: Combine the linear frequency modulated pulse signal and the single frequency signal in the time domain to obtain a combined waveform signal.

[0079] It should be noted that the LFM and CW signals are processed separately to obtain a linear frequency modulated pulse (PTFM) signal and a windowed CW signal, respectively. Then, a time-domain combination design is performed to combine the advantages of both, overcoming the limitations of a single signal type. In this combination, the PTFM portion provides high distance resolution, while the windowed CW portion enhances velocity resolution. Furthermore, through proper design, this combined waveform can effectively suppress reverberation interference, reduce sidelobe levels, and improve detection performance.

[0080] Furthermore, by combining the linear frequency modulated pulse signals, a linear frequency modulated train pulse signal is obtained, as shown in the following formula:

[0081]

[0082] in, For rectangle functions, and These are the lowest and highest frequencies of the linear frequency modulated pulse signal, respectively. The pulse length of the linear frequency modulated pulse signal. This represents the total number of combinations of linear frequency modulated pulse signals. This represents the number of combinations of the current linear frequency modulated pulse signal. Representing the phase change, the total duration of the linear frequency modulated pulse signal is... ;

[0083] The single-frequency signal is windowed and weighted to obtain a windowed single-frequency signal. The specific formula is as follows:

[0084]

[0085] in, For window functions, It is the time domain length of a single-frequency signal. The frequency of a single-frequency signal;

[0086] A combined waveform signal is obtained by time-domain combination of a linear frequency modulated pulse signal and a windowed single-frequency signal. The duration of the combined waveform signal is... Sidelobes are suppressed by combining PTFM with a Hamming windowed single-frequency signal in the time domain. Figure 2 The time-domain waveform diagram and Figure 3 The spectrum analysis diagram shown illustrates the signal parameters. =1s, =0.5s, =0.5s, =0.1s, =5, =500, =700Hz. Observations show that as the number of LFM sub-pulses in the PTFM increases, the curves showing the change in the height of the maximum sidelobe in the zero-Doppler section and the maximum sidelobe height in the zero-delay section of the ambiguity function intersect at the case with 5 segments. Moreover, in this case, both maximum sidelobes are at a relatively low level. Therefore, a performance analysis plot was used to compare four commonly used active sonar waveforms: single-frequency signal (CW), linear frequency modulated signal (LFM), PTFM signal commonly used for anti-reverberation, and combined waveforms based on the correlation algorithm in this embodiment. The performance evaluation of these waveforms is based on the ambiguity function, which is a three-dimensional image that can simultaneously describe the waveform's behavior in the range-velocity domain. The ambiguity function is a three-dimensional image of range-velocity. The two-dimensional section with a distance of 0 is called the velocity resolution section. The -3dB width of the main lobe in this section determines the velocity resolution of the waveform. The ratio of the amplitude of the main lobe to the maximum sidelobe is called the peak-to-sidelobe ratio of the velocity resolution section, and the distance between the maximum sidelobe and the main lobe is called the maximum sidelobe range offset. The two-dimensional cross-section corresponding to a velocity of 0 is called the range resolution cross-section. The -3dB width of the main lobe in this cross-section determines the range resolution of the waveform. The ratio of the amplitude of the main lobe to that of the largest side lobe is called the peak-to-side-lobe ratio of the range resolution cross-section, and the distance between the largest side lobe and the main lobe is called the maximum side-lobe velocity offset. The total duration of the signals selected for analysis is 1 second. PTFM includes 10 segments of 500-700Hz, 0.1s linear frequency modulated pulses, a 700Hz single-frequency signal, and 500-700Hz linear frequency modulated signals. Analysis revealed that CW and LFM signals are incompatible with both high range and high velocity resolution. PTFM and combined signals, however, possess both high range and high velocity resolution due to their comb-like spectrum characteristics. Furthermore, the ratio of the main lobe to the side lobe and the distance between the largest and smallest main lobes are at a moderate level in both cross-sections of the ambiguity function. Notably, all indicators in this performance analysis graph are normalized to between 0 and 1, with closer to 1 indicating better performance. Since the resolution indicators are crucial for waveform selection and play a decisive role in target detection, these two indicators are weighted at 1, while the remaining four indicators, being relatively less important, are weighted at 0.5.

[0087] Step S30: Transmit the combined waveform signal to the underwater area and receive the echo signal fed back from the underwater area.

[0088] It should be noted that during the transmission phase, the sonar system's transducer converts the combined waveform into sound waves and transmits them into the underwater region. This combined waveform design aims to optimize range and velocity resolution while effectively suppressing reverberation interference. During transmission, the propagation characteristics of sound waves in the water medium, such as absorption and scattering, must be considered to ensure the signal efficiently covers the underwater area and returns a clear echo signal. When sound waves encounter a target or obstacles in the environment, they are reflected, forming echo signals that return to the sonar system. The task of the receiving phase is to accurately capture these echo signals and convert them into electrical signals for subsequent processing. Due to the complex and variable marine environment, the echo signals contain information from multiple reflection sources from different directions and distances, including the target itself, background noise, and reverberation. Therefore, the received echo signals are usually quite complex and require a series of complex signal processing techniques for analysis.

[0089] Step S40: Perform matched filtering on the echo signal to generate a time-frequency detection map.

[0090] It should be noted that after receiving the echo signal reflected from the underwater area, a filter matched to the transmitted signal is needed to process the signal. The matched filter is designed based on the combined transmitted waveforms (linear frequency modulated pulse train (PTFM) and windowed single-frequency CW signal), and its principle is to maximize the signal-to-noise ratio (SNR), thereby ensuring effective detection of weak target echoes even in the presence of noise and interference. Specifically, the matched filter achieves this by convolving the input signal with a time-reversed and conjugate template signal (i.e., the transmitted signal).

[0091] Specifically, the length of the entire combined signal is 1 second, the PTFM segment signal is 0.5 seconds long, containing 5 equal-length LFM segments with a frequency range of 500-700Hz, and the CW segment signal is 0.5 seconds long, using a hamming window. A 10-second reverberation signal is first generated using this combined signal. A 2-8 second reverberation segment is extracted from the reverberation signal as the received signal. A signal with a signal-to-mixing ratio of -34dB is added to the received signal during a 6-7 second period, simulating a target movement speed of -10m / s. The received signal is then processed using a matched filter bank, which is a detection sample group composed of a series of replica signals with different frequency offsets added to the transmitted signal. By performing correlation operations between the replica signals with different frequency offsets and the received signal, the maximum gain is observed on the detected time-frequency diagram when the frequency offset of the echo signal in the received signal corresponds to the frequency offset replica in the matched filter bank.

[0092] Further, step S40 includes: selecting multiple matched filters to construct a filter bank, each matching filter corresponding to a different Doppler frequency shift copy of the transmitted signal; then passing the echo signal through the filter bank to obtain multiple sets of time-domain output sequences; performing Fourier transforms on the multiple time-domain output sequences to obtain multiple time-frequency distribution maps; and finally arranging the multiple time-frequency distribution maps in order of frequency shift to obtain a time-frequency detection map. Specifically, in active sonar systems, to effectively handle the Doppler frequency shift problem caused by target motion, multiple matched filters are typically selected to construct a filter bank. Each matched filter corresponds to a different Doppler frequency shift copy of the transmitted signal, thereby ensuring that the echo signal can be accurately detected and identified regardless of the target's speed. First, a specific matched filter is designed for each possible target speed (or Doppler frequency shift). These filters are essentially based on the time-reversed conjugate form of the transmitted signal and adjusted according to the expected Doppler frequency shift. When the received echo signal passes through this filter bank, each filter outputs a set of time-domain output sequences. These output sequences reflect the correlation between the echo signal and the corresponding Doppler frequency shift version of the transmitted signal. Theoretically, if a filter matches the Doppler frequency shift of the actual target, its output will show a significant energy peak, indicating that the target's exact location and relative velocity have been found. Next, a Fourier Transform (FFT) is performed on the multiple time-domain output sequences obtained from the matched filters. The Fourier Transform is a mathematical tool that converts a time-domain signal into a frequency-domain representation, revealing the frequency components of the signal. For active sonar systems, this means transforming the temporal energy distribution into a time- and frequency-based energy distribution map—an instantaneous time-frequency distribution map. These time-frequency distribution maps provide richer information about the echo signal, including but not limited to the target's distance, velocity, and surrounding environmental characteristics. Then, multiple time-frequency distribution maps are arranged according to their corresponding Doppler frequency shifts to form the final time-frequency detection map. This serves several important purposes: first, it allows target echoes at different velocities to be visually displayed on the same graph, facilitating analysis; second, this method helps distinguish true echoes from the target from other interference sources (such as reverberation and noise); and finally, comprehensive analysis across the entire frequency shift range improves the accuracy of target detection and reduces the false alarm rate. As the reverberation intensity decays over time, higher correlation values ​​appear in the early part of the time-frequency graph, and the target's echo will be submerged in these highly correlated regions.

[0093] Step S50: Perform dynamic threshold filtering on the time-frequency detection map and output a set of candidate peaks.

[0094] It should be noted that after generating the time-frequency detection map, we find that it contains a large amount of energy information from different time and frequency points, with a total of 6161 peak structures meeting the criteria in the initial candidate peak results. This information includes both valid echo signals from the target and various forms of interference, such as background noise and reverberation effects. Therefore, directly using a fixed threshold to filter candidate peaks may lead to a large number of false alarms or missed alarms. To solve this problem, a dynamic thresholding method becomes necessary. The core idea of ​​dynamic thresholding is to adaptively adjust the threshold based on the local background noise level. Specifically, a sliding window technique can be used to traverse the entire time-frequency detection map and calculate the estimated value of the background noise within each window. Based on this estimate, a relative threshold can be set to determine whether the peak in the current window should be considered a candidate peak. The advantage of this method is that it can effectively distinguish between weak target echoes in strong reverberation regions and actual noise peaks.

[0095] Furthermore, the dynamic threshold is generated based on the background estimate obtained through median filtering. A median filtering window is used to iterate through the time-frequency detection map. This process involves selecting an appropriate sliding window size, which needs to be determined based on the temporal characteristics of the matching peaks in the combined waveform. This ensures that the influence of strong reverberation regions is effectively suppressed without destroying the characteristics of the matching peaks. The specific steps include: calculating the median value of the pixels within the window by passing the median filtering window through the time-frequency detection map; using the median as the background estimate for the window center point; and calculating the dynamic threshold based on the background estimate and a preset offset. Specifically, for each window position, the median of all pixel values ​​(i.e., energy intensity) within the window is calculated as the background estimate. The advantage of median filtering is that it effectively removes impulse noise interference while preserving edges and other important details. Therefore, this method can yield a more accurate estimate of the background noise level. Next, the calculated median is used as the background noise estimate for the window center point, and a preset offset is added to this to generate the dynamic threshold. The selection of this preset offset is crucial because it determines the balance of the algorithm's sensitivity to the target signal. If the offset is too small, too many noise peaks may be mistaken for targets; conversely, if the offset is too large, some weak target signals may be missed. Typically, this offset is determined experimentally or based on empirical data, aiming to reduce false alarm rates in complex environments while maintaining a high target detection rate. Through the above steps, we assign a dynamic threshold based on the local background noise level to each point on the time-frequency detection map. The advantage of this is that the threshold can be flexibly adjusted according to the actual background noise situation, whether in areas of strong reverberation or relatively quiet areas, thereby improving the accuracy of target detection.

[0096] Furthermore, through dynamic threshold filtering, the threshold can be flexibly adjusted according to changes in background noise, enabling even weaker target echoes located behind strong reverberation regions to be correctly identified, ultimately yielding a candidate peak set. This candidate peak set includes the target main peak and interference peaks. Step S50 specifically includes: acquiring the pixel values ​​corresponding to the pixels in the time-frequency detection map, comparing the pixel values ​​with the dynamic threshold, obtaining the comparison result, and when the comparison result is that the pixel value is greater than the dynamic threshold, outputting the target pixel corresponding to the pixel value, and recording the position of the target pixel in the time-frequency detection map to obtain the candidate peak combination. Specifically, the pixel values ​​corresponding to each pixel in the time-frequency detection map are acquired. The time-frequency detection map essentially converts the received echo signal into an energy distribution map in time and frequency through Fourier transform. In this process, each pixel represents the energy intensity at a specific time-frequency coordinate. These pixel values ​​not only reflect the energy distribution of the signal but also contain important information about the presence or absence of the target. Next, the acquired pixel values ​​are compared with the dynamic threshold previously generated based on median filtering background estimation. The introduction of dynamic thresholds is to adapt to the complexity and variability of the marine environment, ensuring effective identification of weak target signals even against strong reverberation or noise backgrounds. Specifically, for each pixel, if its corresponding pixel value exceeds the dynamic threshold calculated for that location, then that point is considered a potential target echo signal. This comparison operation can be viewed as a locally adaptive screening mechanism, flexibly adjusting the threshold according to changes in background noise levels, avoiding excessively high false alarm rates or missed detections caused by fixed thresholds. When the comparison result indicates that the pixel value is greater than the dynamic threshold, the target pixel corresponding to that pixel value is output. This means that an energy peak containing target information has been found in the time-frequency detection map. Recording the location of target pixels typically involves two dimensions of information: time (or distance) and frequency (or Doppler shift). These two dimensions together determine the specific characteristics of a candidate peak. For example, a significant energy peak appearing at a certain time and frequency point may indicate that a target moving at a specific speed reflected the sound wave signal at that moment. By recording all such target pixels, we obtain a candidate peak set, which will be used for subsequent fine-tuning and verification steps. Figure 4 The diagram shows the candidate peak results after dynamic threshold filtering. Only 1159 candidate peaks were detected. The comparison shows that most of the reduction is in the strong reverberation area at the front, while the number of candidate peaks at the later target locations has not decreased. This also reflects that the background estimation based on dynamic threshold can reduce reverberation interference.

[0097] Step S60: Search the candidate peak set by constructing a two-dimensional search window based on the theoretical secondary peak interval to obtain the secondary peaks.

[0098] It should be noted that a two-dimensional search window is constructed based on the theoretical secondary peak spacing, and the search and verification of secondary peaks are performed on this basis. This method allows for the effective differentiation of the true target echo signal from a complex reverberant background.

[0099] Based on the characteristics of the combined waveform after matched filtering, we know that a series of secondary peaks distributed according to a specific pattern exist around the main peak. The energy of these secondary peaks shows a decreasing trend, and they have a clear positional relationship in the time dimension. Specifically, the time interval of the secondary peaks can be calculated based on the design parameters of the combined waveform. For example, for a PTFM signal containing multiple linear frequency modulation (LFM) pulses, the location of its secondary peaks can be predicted by theoretical formulas. Based on this information, we can construct a two-dimensional search window on both sides of each candidate peak (i.e., the potential main peak) according to the theoretical secondary peak interval. When constructing the two-dimensional search window, considering various interference factors in the actual marine environment, such as multipath effects and sea surface fluctuations, the actual location of the secondary peaks may deviate from the theoretical value. Therefore, when determining the specific range of the search window, in addition to the theoretically calculated time interval, a certain tolerance range needs to be introduced. For example, if theoretical calculations show that a certain secondary peak should appear at the 0.5-second position, a tolerance of ±0.03 seconds can be set, that is, the search can be performed within the time range of 0.47 to 0.53 seconds. At the same time, a certain tolerance must also be considered in the speed dimension to cope with the possible effects of Doppler frequency shift.

[0100] Once the two-dimensional search window is established, the next task is to find the maximum value point within each window as the secondary peak. This means selecting the point with the highest energy within each search area to represent the existence of the secondary peak. This process not only helps us confirm the existence of secondary peaks that conform to the expected pattern, but also effectively eliminates false peaks caused by background noise or non-target scatterers. In this embodiment, two different sizes of two-dimensional search boxes are given. The main peak is marked with a pentagram, and the maximum value within the two-dimensional search box is selected as the secondary peak and marked with a cross. Considering the actual application scenario, the matching peaks of the same target on the time-frequency diagram do not differ much in the velocity dimension, so the width of the search box is chosen to be 0.02 m / s. However, considering the complex background of reverberation, the search interval in the time dimension is chosen. Considering that the two secondary peaks near the main peak have relatively high energy, the search box length on the left and right sides of the main peak is 0.06 s, but the center positions of the search boxes are spaced 0.1 s apart. This is because two two-dimensional search boxes are constructed on both sides of the comb-like space on the matching peak.

[0101] Ultimately, all qualified secondary peaks obtained through the search will be used in subsequent constraint verification stages, including energy decay verification, symmetry verification, and continuity verification, thereby further improving the reliability and accuracy of target detection.

[0102] Step S70: Verify the secondary peak based on the preset attenuation law, and output the secondary peak that satisfies the preset attenuation law.

[0103] It should be noted that, based on the characteristics of the combined waveform after matched filtering, we know that a series of secondary peaks with progressively decreasing energy exist around the main peak, distributed according to a specific pattern. The energy decay patterns of these secondary peaks are determined in advance through theoretical calculations and experiments. Specifically, the energy of the secondary peaks typically exhibits a progressively decreasing trend, and this trend is closely related to the design parameters of the combined waveform. For example, in some designs, the energy of the first secondary peak near the main peak might be 50% of the main peak's energy, while the energy of the second secondary peak might be 50% of the first, and so on. Next, for each candidate peak (i.e., the potential main peak), after constructing a two-dimensional search window on its left and right sides and finding possible secondary peaks, these secondary peaks need to be verified based on a preset decay pattern. This step includes several key preset decay patterns:

[0104] Energy decay verification: Check whether the energy of each secondary peak conforms to the expected gradual decrease pattern. For example, if the energy of a secondary peak is found to be significantly higher or lower than the expected value, then the secondary peak is likely not caused by the target echo.

[0105] Symmetry verification: Ensure that the number of secondary peaks on both sides of the main peak is consistent and that their energy distribution is symmetrical. Asymmetrical energy distribution may indicate the presence of disturbances or other non-target factors.

[0106] Continuity verification: Confirm that secondary peaks appear sequentially in order of their distance from the main peak, without skipping any levels. For example, there should not be a situation where the second-order secondary peak has already appeared before the first-order secondary peak has been detected.

[0107] By verifying the above conditions, true secondary peaks that satisfy the preset attenuation law can be effectively screened out. Only those secondary peaks that fully meet all the constraints are considered valid and used in the final target detection results. This method significantly improves the accuracy of target recognition, especially in complex marine environments, and can more reliably distinguish the true target signal from background noise or reverberation interference.

[0108] Step S80: The energy value of the secondary peak is superimposed onto the target main peak to generate the target detection result.

[0109] It should be noted that the energy values ​​of the verified secondary peaks will be superimposed on the target main peak. Specifically, the energy values ​​of the secondary peaks are superimposed on the energy values ​​of the main peak using either a linear or nonlinear method. Linear superposition directly adds the energy values ​​of the secondary peaks to the main peak, while nonlinear superposition assigns different weights based on the energy decay pattern of the secondary peaks to optimize the superposition effect. The energy superposition process needs to be optimized and adjusted according to the actual application scenario. For example, when the signal-to-noise ratio of the target signal is high, a more stringent energy superposition strategy can be adopted; while when the signal-to-noise ratio is low, a more lenient strategy is needed to avoid falsely suppressing the target signal. In addition, the results after energy superposition need to be normalized to ensure the uniformity and comparability of the detection results.

[0110] Furthermore, based on the superimposed and normalized energy values, an enhanced detection result is generated. This result includes not only the location information of the target main peak but also the new energy value after energy superposition. Figure 5 Target detection results time-frequency graph and Figure 6 The energy convergence result time-frequency graph shows that the energy of the secondary peak is converged to the main peak. The convergence method is to first map the entire time-frequency graph to linear values, then superimpose the linear values ​​on the secondary peaks onto the main peak, then take the maximum value of the entire time-frequency graph for normalization, and finally display the processed result in dB. The final result shows that the target appearance time of 7s and the target velocity of -10m / s are consistent with the preset target parameters. Moreover, by converging the energy of the secondary peaks to the main peak, the gain of the main peak before energy convergence is -5.6dB, and the gain after energy convergence is -0.89dB, which is an increase of 4.7dB.

[0111] Next, based on these enhanced detection results, more detailed target detection results are generated. These results include not only the target's specific location (such as time-frequency coordinates) but also a preliminary assessment of the target type. For example, by analyzing the characteristics of the echo signal, it can be inferred whether the target is a submarine, a school of fish, or another underwater object. Furthermore, velocity information provided by the Doppler effect can be combined to further refine the target's motion state and trajectory.

[0112] This embodiment acquires a linear frequency modulated (LFM) pulse signal and a single-frequency signal; combines the LFM pulse signal and the single-frequency signal in the time domain to obtain a combined waveform signal; transmits the combined waveform signal to an underwater region and receives the echo signal fed back from the underwater region; performs matched filtering on the echo signal to generate a time-frequency detection map; performs dynamic threshold filtering on the time-frequency detection map to output a candidate peak set, which includes the target main peak and interference peaks; constructs a two-dimensional search window based on the theoretical secondary peak interval to search for secondary peaks in the candidate peak set; verifies the secondary peaks based on a preset attenuation law and outputs secondary peaks that satisfy the preset attenuation law; and superimposes the energy value of the secondary peaks onto the target main peak to generate the target detection result. The time-frequency detection map is generated by transmitting a combined waveform of a LFM pulse signal and a single-frequency signal to an underwater region, receiving the echo signal, and performing matched filtering. A dynamic threshold is used to filter the candidate peak set, and a two-dimensional search window is constructed based on the theoretical secondary peak interval. The secondary peak energy is verified and superimposed onto the target main peak to generate the target detection result. This effectively suppresses reverberation interference and improves the underwater target detection accuracy in shallow sea environments with strong reverberation.

[0113] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 7 The underwater target detection result generation method based on combined waveforms, step S60, further includes steps S201 to S204:

[0114] Step S201: Calculate the theoretical secondary peak interval based on the combined waveform signals in the candidate peak set.

[0115] It should be noted that combined waveform signals (such as a combination of a linear frequency modulated pulse train (PTFM) and a windowed single-frequency CW signal) will exhibit a specific pattern on the time-frequency graph after processing by a matched filter. Specifically, this combined waveform will form a series of secondary peaks with gradually decreasing energy around the main peak. The appearance and spacing of the secondary peaks are determined by the design parameters of the combined waveform, such as the number of sub-pulses in the PTFM segment, the frequency variation range, and the frequency of the CW segment. To calculate the theoretical secondary peak spacing, it is first necessary to analyze the structure of the combined waveform. Taking a PTFM signal containing multiple LFM pulses as an example, each LFM pulse will generate a corresponding peak in the output after matched filtering. The spacing between these peaks depends on the duration and frequency change rate of the LFM pulse.

[0116] Step S202: Construct a two-dimensional search window based on the theoretical secondary peak interval.

[0117] It's important to note that when constructing a two-dimensional search window, two dimensions need to be considered: time and velocity (or frequency). Specifically, in the time dimension, the window's position and size are set based on the theoretical secondary peak interval. To account for deviations in real-world environments, a certain tolerance range (e.g., ±0.03 seconds) is typically introduced beyond the theoretical interval to ensure that secondary peaks are captured even with slight offsets. In the velocity dimension, while the velocity variation of the same target echo is relatively small, an appropriate tolerance still needs to be set to cover possible Doppler shifts.

[0118] This method can effectively locate potential secondary peaks in complex time-frequency detection maps, thereby improving the accuracy and reliability of target detection.

[0119] Step S203: Search the two-dimensional search window to obtain image points with preset energy values.

[0120] It should be noted that after constructing the two-dimensional search window around the candidate primary peak, the specific location and size of each window need to be determined based on the theoretically calculated secondary peak intervals and tolerance ranges. Each two-dimensional search window actually defines a local region, within which one or more secondary peaks may exist. The next task is to find the energy peaks within these windows, that is, the image points whose energy reaches the preset value.

[0121] In practice, this can be achieved by iterating through all pixels within each two-dimensional search window. For each pixel, its energy value is compared with a preset energy threshold. If the energy value of a pixel exceeds the preset energy threshold, the corresponding image point is obtained, and this point is considered a potential secondary peak.

[0122] Step S204: Use the image points of the preset value as secondary peaks.

[0123] This embodiment constructs a two-dimensional search window by calculating the theoretical secondary peak interval of the combined waveform, and searches for image points with energy reaching a preset value to identify secondary peaks, thereby improving the accuracy and reliability of target detection and effectively distinguishing target echoes from background noise.

[0124] Based on the first embodiment of this application, in the third embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 8 The underwater target detection result generation step S80 based on the combined waveform further includes steps S301 to S303:

[0125] Step S301: The energy of the secondary peak is superimposed onto the energy value of the target main peak using either a linear or nonlinear superposition method to obtain the superimposed energy value.

[0126] It's important to note that linear superposition is the most direct method, where the energy value of each secondary peak is directly added to the corresponding target primary peak's energy value. This method is suitable when the energy difference between the secondary and primary peaks is small and the background noise is relatively low. For example, if there are three effective secondary peaks around a primary peak, their energy values ​​will be added to the primary peak's energy value, significantly enhancing the primary peak's signal strength. This simple superposition method effectively improves the signal-to-noise ratio, making the target echo more prominent. Nonlinear superposition is more flexible and precise. It involves weighting the energy of the secondary peaks before superimposing it onto the primary peak's energy value. This method can adjust the weights based on the importance of the secondary peaks or other factors, making the superposition process more targeted. For example, in some cases, secondary peaks closer to the primary peak may be more important for enhancing it, thus they can be given higher weights. Furthermore, the weights can be dynamically adjusted based on the energy attenuation patterns of the secondary peaks to ensure that the superimposed energy distribution better reflects the actual situation.

[0127] Regardless of the superposition method used to superimpose the energy, the resulting superimposed energy value not only improves the accuracy of target identification but also better handles interference factors in the complex and ever-changing marine environment.

[0128] Step S302: Normalize the superimposed energy values ​​to obtain the enhanced detection results.

[0129] It should be noted that when the energy of secondary peaks is superimposed onto the main peak, the resulting enhanced energy values ​​may be distributed over a wide range. To ensure that these energy values ​​can be compared and analyzed under a unified standard, they need to be normalized. Normalization typically refers to mapping data to a specific range (such as between 0 and 1) to make energy values ​​at different locations comparable. In practice, normalization can be achieved through linear transformations or other nonlinear methods.

[0130] Step S303: Generate target detection results based on the enhanced detection results.

[0131] It should be noted that the normalized energy value provides the enhanced detection result, which includes not only the specific location information of the target's main peak, but also the energy distribution after energy superposition and normalization. The final target detection result is obtained by processing the enhanced detection result.

[0132] Further, step S303 also includes: calculating the target motion parameters based on the enhanced detection results, extracting features based on the target motion parameters to obtain a target feature vector, and finally inputting the target feature vector into a preset neural network model to obtain the target detection result, which includes the target type and target position. Specifically, the target motion parameters are calculated based on the enhanced detection results. These parameters include, but are not limited to, the target's velocity, direction, and trajectory. By analyzing the Doppler frequency shift information in the echo signal, the target's velocity can be accurately estimated; combined with the time-frequency distribution on the time-frequency map, the target's direction of movement can also be inferred. In addition, by using detection data at multiple consecutive time points, filtering and smoothing algorithms (such as Kalman filtering) can be used to optimize the estimation of the target trajectory, thereby obtaining more stable and accurate target motion parameters. Next, feature extraction is performed based on the above target motion parameters to generate a target feature vector. Feature extraction is a process of converting raw data into a representative feature representation. For sonar target recognition, commonly used features include spectral features, time-domain features, and energy distribution-based features. For example, energy distribution in specific frequency bands can be extracted as features from the enhanced time-frequency map; alternatively, the autocorrelation function of the echo signal can be calculated to extract periodic and aperiodic components as features. These features together constitute a feature vector describing the target's characteristics. Subsequently, the extracted target feature vector is input into a pre-defined neural network model for final determination of the target type and location. This neural network model is trained on a large amount of labeled data. By inputting the feature vector, the neural network can output a probability distribution regarding the target type and determine the most likely target category (such as a submarine, a school of fish, or other underwater objects). Simultaneously, based on previously calculated motion parameters, the target's positional information can be further refined, providing more accurate coordinate positioning.

[0133] This embodiment obtains enhanced detection results by linearly or nonlinearly superimposing secondary peak energies to the main peak and normalizing them, ultimately generating a target detection report. This improves target detection accuracy and signal-to-noise ratio, effectively distinguishes targets from background noise, and enhances system reliability and adaptability.

[0134] Based on the first embodiment of this application, this application also provides an underwater target detection result generation device based on combined waveforms. Please refer to... Figure 9 The device includes:

[0135] The acquisition module 10 is used to acquire linear frequency modulated pulse signals and single-frequency signals.

[0136] The combination module 20 is used to combine the linear frequency modulated pulse signal and the single frequency signal in the time domain to obtain the combined waveform signal.

[0137] The receiving module 30 is used to transmit the combined waveform signal to the underwater area and receive the echo signal fed back from the underwater area.

[0138] The processing module 40 is used to perform matched filtering on the echo signal and generate a time-frequency detection map.

[0139] The filtering module 50 is used to perform dynamic threshold filtering on the time-frequency detection map and output a candidate peak set, which includes the target main peak and interference peaks.

[0140] The search module 60 is used to search the candidate peak set by constructing a two-dimensional search window based on the theoretical secondary peak interval to obtain the secondary peaks.

[0141] The verification module 70 is used to verify the secondary peaks based on the preset attenuation law and output the secondary peaks that satisfy the preset attenuation law.

[0142] Result module 80 is used to superimpose the energy values ​​of secondary peaks onto the target main peak to generate target detection results.

[0143] The underwater target detection result generation device based on combined waveforms provided in this application, employing the underwater target detection result generation method based on combined waveforms in the above embodiments, can solve the technical problem of how to improve the accuracy of target detection in shallow sea environments. Compared with the prior art, the beneficial effects of the underwater target detection result generation device based on combined waveforms provided in this application are the same as those of the underwater target detection result generation method based on combined waveforms provided in the above embodiments, and other technical features in the underwater target detection result generation device based on combined waveforms are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0144] In one embodiment, the combination module 20 is further configured to combine the linear frequency modulated pulse signals to obtain a linear frequency modulated train pulse signal; perform window weighting processing on the single frequency signal to obtain a windowed single frequency signal; and perform time-domain combination of the linear frequency modulated train pulse signal and the windowed single frequency signal to obtain a combined waveform signal.

[0145] In one embodiment, the processing module 40 is further configured to select multiple matched filters to construct a filter bank, wherein the matched filters correspond to different Doppler frequency offset copies of the transmitted signal; pass the echo signal through the filter bank to obtain multiple time-domain output sequences; perform Fourier transform on the multiple time-domain output sequences to obtain multiple time-frequency distribution maps; and arrange the multiple time-frequency distribution maps in the order of frequency offset to obtain a time-frequency detection map.

[0146] In one embodiment, the filtering module 50 is further configured to obtain the pixel value corresponding to the pixel point in the time-frequency detection image; compare the pixel value with a dynamic threshold to obtain a comparison result; when the comparison result is that the pixel value is greater than the dynamic threshold, output the target pixel point corresponding to the pixel value; and record the position of the target pixel point in the time-frequency detection image to obtain a candidate peak combination.

[0147] In one embodiment, the search module 60 is further configured to calculate the theoretical secondary peak interval based on the combined waveform signals in the candidate peak set; construct a two-dimensional search window based on the theoretical secondary peak interval; search the two-dimensional search window to obtain image points with preset energy values; and use the image points with preset energy values ​​as secondary peaks.

[0148] In one embodiment, the result module 80 is further configured to add the energy of the secondary peak to the energy value of the target main peak using a linear or nonlinear superposition method to obtain a superimposed energy value. The linear superposition method adds the energy value of the secondary peak to the energy value of the main peak, while the nonlinear superposition method adds the energy of the secondary peak to the energy value of the main peak through weighted superposition. The superimposed energy value is then normalized to obtain an enhanced detection result, which includes the position of the target main peak and the superimposed energy value. Based on the enhanced detection result, a target detection result is generated.

[0149] In one embodiment, the result module 80 is further configured to calculate the target motion parameters based on the enhanced detection results; extract features based on the target motion parameters to obtain the target feature vector; and input the target feature vector into a preset neural network model to obtain the target detection result, which includes the target type and the target location.

[0150] This application provides an underwater target detection result generation device based on combined waveforms. The underwater target detection result generation device based on combined waveforms includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the underwater target detection result generation method based on combined waveforms in the above embodiment 1.

[0151] The following is for reference. Figure 10This document illustrates a structural schematic diagram of an underwater target detection result generation device based on combined waveforms suitable for implementing embodiments of this application. The underwater target detection result generation device based on combined waveforms in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 10 The underwater target detection result generation device based on combined waveforms shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0152] like Figure 10 As shown, the underwater target detection result generation device based on combined waveforms may include a processing unit 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the underwater target detection result generation device based on combined waveforms. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following can be connected to I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the underwater target detection result generation device based on combined waveforms to wirelessly or wiredly communicate with other devices to exchange data. Although various underwater target detection result generation devices based on combined waveforms are shown in the figures, it should be understood that it is not required to implement or possess all of them. More or fewer may be implemented alternatively.

[0153] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0154] The underwater target detection result generation device based on combined waveforms provided in this application, employing the underwater target detection result generation method based on combined waveforms in the above embodiments, can solve the technical problem of how to improve the accuracy of target detection in shallow sea environments. Compared with the prior art, the beneficial effects of the underwater target detection result generation device based on combined waveforms provided in this application are the same as those of the underwater target detection result generation method based on combined waveforms provided in the above embodiments, and other technical features in this underwater target detection result generation device based on combined waveforms are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0155] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0156] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0157] This application provides a computer-readable medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the underwater target detection result generation method based on combined waveforms in the above embodiments.

[0158] The computer-readable medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor devices, or any combination thereof. More specific examples of computer-readable media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable medium may be any tangible medium containing or storing a program that can be executed by instructions, used by a device, or used in conjunction with it. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0159] The aforementioned computer-readable medium may be included in an underwater target detection result generation device based on combined waveforms; or it may exist independently and not be assembled into an underwater target detection result generation device based on combined waveforms.

[0160] The aforementioned computer-readable medium carries one or more programs that, when executed by the underwater target detection result generation device based on combined waveforms, enable the device to write computer program code for performing the operations of this application in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0161] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using dedicated hardware-based implementations that perform the specified functions or operations, or can be implemented using a combination of dedicated hardware and computer instructions.

[0162] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0163] The readable medium provided in this application is a computer-readable medium, which stores computer-readable program instructions (i.e., a computer program) for executing the above-described method for generating underwater target detection results based on combined waveforms, and can solve the technical problem of how to improve the accuracy of target detection in shallow sea environments. Compared with the prior art, the beneficial effects of the computer-readable medium provided in this application are the same as the beneficial effects of the underwater target detection result generation method based on combined waveforms provided in the above embodiments, and will not be repeated here.

[0164] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the underwater target detection result generation method based on combined waveforms as described above.

[0165] The computer program product provided in this application can solve the technical problem of how to improve the accuracy of target detection in shallow sea environments. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the underwater target detection result generation method based on combined waveforms provided in the above embodiments, and will not be repeated here.

[0166] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for generating underwater target detection results based on combined waveforms, characterized in that, The method includes: Acquire linear frequency modulated pulse signals and single-frequency signals; The linear frequency modulated pulse signal and the single-frequency signal are combined in the time domain to obtain a combined waveform signal; The combined waveform signal is transmitted to the underwater area, and the echo signal fed back from the underwater area is received; The echo signal is subjected to matched filtering to generate a time-frequency detection map; The time-frequency detection graph is subjected to dynamic threshold filtering to output a candidate peak set, wherein the candidate peak set includes the target main peak and interference peaks; The candidate peak set is searched using a two-dimensional search window constructed based on the theoretical secondary peak interval to obtain the secondary peaks; The secondary peak is verified based on a preset attenuation rule, and a secondary peak that satisfies the preset attenuation rule is output. The energy values ​​of the secondary peaks are superimposed onto the target main peak to generate the target detection result.

2. The method as described in claim 1, characterized in that, The step of combining the linear frequency modulated pulse signal and the single-frequency signal in the time domain to obtain a combined waveform signal includes: The linear frequency modulated pulse signals are combined to obtain a linear frequency modulated train pulse signal, as shown in the following formula: in, For rectangle functions, and These are the lowest and highest frequencies of the linear frequency modulated pulse signal, respectively. The pulse length of the linear frequency modulated pulse signal is given. The total number of combinations of the linear frequency modulated pulse signals. The number of combinations of the currently described linear frequency modulated pulse signals. Representing the phase change, the total duration of the linear frequency modulated train pulse signal is ; The single-frequency signal is subjected to window weighting processing to obtain a windowed single-frequency signal, the specific formula of which is: in, For window functions, It is the time-domain length of the single-frequency signal. The frequency of the single-frequency signal; The combined waveform signal is obtained by time-domain combination of the linear frequency modulated pulse signal and the windowed single-frequency signal, and the duration of the combined waveform signal is [duration missing]. .

3. The method as described in claim 1, characterized in that, The step of performing matched filtering on the echo signal to generate a time-frequency detection map includes: A filter bank is constructed by selecting multiple matched filters, and the matched filters correspond to different Doppler frequency offset copies of the transmitted signal; The echo signal is passed through the filter bank to obtain multiple sets of time-domain output sequences; Perform Fourier transform on the multiple time-domain output sequences to obtain multiple time-frequency distribution maps; Arrange the multiple time-frequency distribution maps in order of frequency offset to obtain a time-frequency detection map.

4. The method as described in claim 1, characterized in that, The step of performing dynamic threshold filtering on the time-frequency detection map and outputting a candidate peak set includes: Obtain the pixel value corresponding to the pixel point in the time-frequency detection image; The pixel value is compared with the dynamic threshold to obtain the comparison result; When the comparison result is that the pixel value is greater than the dynamic threshold, the target pixel corresponding to the pixel value is output; The positions of the target pixels in the time-frequency detection map are recorded to obtain candidate peak combinations.

5. The method as described in claim 1, characterized in that, The step of searching the candidate peak set to obtain secondary peaks includes: The theoretical secondary peak spacing is obtained by calculating the combined waveform signals in the candidate peak set. A two-dimensional search window is constructed based on the theoretical secondary peak spacing; The two-dimensional search window is searched to obtain image points with energy of a preset value; The image points with the preset values ​​are used as secondary peaks.

6. The method as described in claim 1, characterized in that, The step of superimposing the energy value of the secondary peak onto the target main peak to generate the target detection result includes: The energy of the secondary peak is added to the energy value of the target main peak in a linear or nonlinear superposition method to obtain the superimposed energy value. The linear superposition method is to add the energy value of the secondary peak to the energy value of the main peak, and the nonlinear superposition method is to add the energy of the secondary peak to the energy value of the main peak through weighted superposition. The enhanced detection result is obtained by normalizing the superimposed energy value, which includes the position of the target main peak and the superimposed energy value. Based on the enhanced detection results, target detection results are generated.

7. The method as described in claim 6, characterized in that, The step of generating target detection results based on the enhanced detection results includes: The target motion parameters are calculated based on the enhanced detection results. Based on the target motion parameters, feature extraction is performed to obtain the target feature vector; The target feature vector is input into a preset neural network model to obtain the target detection result, which includes the target type and the target location.

8. A device for generating underwater target detection results based on combined waveforms, characterized in that, The device includes: The acquisition module is used to acquire linear frequency modulated pulse signals and single-frequency signals; The combination module is used to combine the linear frequency modulated pulse signal and the single frequency signal in the time domain to obtain a combined waveform signal; A receiving module is used to transmit the combined waveform signal to the underwater area and receive the echo signal fed back from the underwater area; The processing module is used to perform matched filtering on the echo signal to generate a time-frequency detection map; The filtering module is used to perform dynamic threshold filtering on the time-frequency detection map and output a candidate peak set, wherein the candidate peak set includes the target main peak and interference peaks; The search module is used to search the candidate peak set by constructing a two-dimensional search window based on the theoretical secondary peak interval to obtain the secondary peaks; The verification module is used to verify the secondary peak based on a preset attenuation rule and output the secondary peak that satisfies the preset attenuation rule. The results module is used to superimpose the energy values ​​of the secondary peaks onto the target main peak to generate target detection results.

9. A device for generating underwater target detection results based on combined waveforms, characterized in that, The device includes: a memory, a processor, and an underwater target detection result generation program based on a combined waveform stored in the memory and running on the processor, the underwater target detection result generation program based on a combined waveform being configured to implement the steps of the underwater target detection result generation method based on a combined waveform as described in any one of claims 1-7.

10. A storage medium, characterized in that, The storage medium stores an underwater target detection result generation program based on combined waveforms. When the underwater target detection result generation program based on combined waveforms is executed by the processor, it implements the steps of the underwater target detection result generation method based on combined waveforms as described in any one of claims 1-7.

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