Underwater target detection result generation method and device based on combined waveform, equipment and medium

Abstract

The invention discloses an underwater target detection result generation method, device and equipment based on a combined waveform, and a medium, and relates to the technical field of underwater acoustic engineering and signal processing, and the method comprises the steps: transmitting a combined waveform of a linear frequency modulation pulse signal and a single frequency signal to an underwater region, receiving an echo signal, and carrying out the matched filtering processing, and generating a time-frequency detection graph. And a candidate peak set is screened by adopting a dynamic threshold, a two-dimensional search window is constructed based on a theoretical secondary peak interval, secondary peak energy is verified and superposed to a target main peak, and a target detection result is generated, so that reverberation interference is effectively inhibited, and the underwater target detection precision in a shallow sea strong reverberation environment is improved.

Description

Technical Field

[0001] The present invention relates to the field of underwater acoustic engineering and signal processing technology, and in particular to a method, device, equipment and medium for generating underwater target detection results based on a combined waveform. Background Art

[0002] Underwater acoustic engineering is the study of underwater acoustic phenomena and their applications, and is widely used in marine resource development, underwater communications, underwater navigation, and marine environmental monitoring. Active sonar systems are a crucial component of underwater acoustic engineering, detecting, locating, and identifying underwater targets by emitting sound waves and receiving echo signals reflected from the target. Currently, the waveform designs commonly used in active sonar systems focus on single-frequency signals, broadband signals, and comb spectrum signals. For reverberation interference, the reverberation background is estimated through methods such as sliding window operations, combined with a constant false alarm algorithm for target detection. However, this carries the risk of incorrectly suppressing high-gain target echo highlights.

[0003] While current technologies have addressed some of the challenges of underwater target detection to a certain extent, they still face limitations in several areas. Single-frequency and broadband signals struggle to balance ranging and velocity measurement accuracy, failing to meet the requirements for both high range and velocity resolution. While comb-spectrum signals offer superior anti-reverberation performance, they exhibit high periodic sidelobes, which can easily lead to false alarms and compromise the reliability of target detection. While reverberation suppression algorithms can reduce false alarm rates, they carry the risk of falsely suppressing target echoes, particularly in highly reverberant environments where target signals may be submerged within the reverberant region. Existing algorithms have limited accuracy in estimating the reverberant background and struggle to adapt to complex underwater environments. Therefore, a new waveform design and target detection algorithm are urgently needed to improve the accuracy of target detection in shallow waters with strong reverberation. Summary of the Invention

[0004] The main purpose of this application is to provide a method, device, equipment and medium for generating underwater target detection results based on a combined waveform, 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, the present application proposes a method for generating underwater target detection results based on a combined waveform, comprising: Obtain linear frequency modulated pulse signal and single frequency signal; Combining the linear frequency modulation 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 area and receiving an echo signal fed back from the underwater area; Performing matched filtering on the echo signal to generate a time-frequency detection graph; Performing dynamic threshold screening on the time-frequency detection graph to output a candidate peak set, wherein the candidate peak set includes a target main peak and interference peaks; Constructing a two-dimensional search window for the candidate peak set according to the theoretical secondary peak interval to search and obtain secondary peaks; Verifying the secondary peak based on a preset attenuation law, and outputting a secondary peak that satisfies the preset attenuation law; The energy value of the secondary peak is superimposed on the target main peak to generate a target detection result.

[0006] In one embodiment, the step of combining the linear frequency modulation pulse signal and the single frequency signal in the time domain to obtain a combined waveform signal includes: The linear frequency modulation pulse signal is combined to obtain a linear frequency modulation pulse train signal. The specific formula is: in, is a rectangular function, and are the lowest frequency and the highest frequency of the linear frequency modulation pulse signal respectively, is the pulse length of the linear frequency modulation pulse signal, is the total number of combinations of the linear frequency modulation pulse signals, is the number of combinations of the current linear frequency modulation pulse signal, Indicates the change of phase, the total duration of the linear frequency modulation pulse signal is ; The single-frequency signal is subjected to window weighting processing to obtain a windowed single-frequency signal. The specific formula is: in, is the window function, is the time domain length of the single frequency signal, is the frequency of the single-frequency signal; The linear frequency modulation pulse signal and the windowed single frequency signal are combined in time domain to obtain a combined waveform signal, the duration of which is .

[0007] In one embodiment, the step of performing matched filtering on the echo signal to generate a time-frequency detection graph includes: Selecting a plurality of matched filters to construct a filter bank, wherein the matched filters correspond to different Doppler frequency offset copies of the transmitted signal; Passing the echo signal through the filter bank to obtain multiple groups of time domain output sequences; Performing Fourier transform on the multiple time-domain output sequences to obtain multiple time-frequency distribution graphs; Arrange the multiple time-frequency distribution graphs in the order of frequency deviation to obtain a time-frequency detection graph.

[0008] In one embodiment, the step of performing dynamic threshold screening on the time-frequency detection graph and outputting a candidate peak set includes: Obtaining a pixel value corresponding to a pixel point in the time-frequency detection image; Comparing the pixel value with the dynamic threshold to obtain a comparison result; When the comparison result is that the pixel value is greater than the dynamic threshold, outputting the target pixel point corresponding to the pixel value; The position of the target pixel point in the time-frequency detection image is recorded to obtain a candidate peak combination.

[0009] In one embodiment, the step of searching the candidate peak set to obtain a secondary peak includes: Calculating based on the combined waveform signal in the candidate peak set to obtain a theoretical secondary peak interval; constructing a two-dimensional search window according to the theoretical secondary peak interval; Searching the two-dimensional search window to obtain image points with energy values ​​of preset values; The image point of the preset value is taken as the secondary peak.

[0010] 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: The energy of the secondary peak is added to the energy value of the target main peak in a linear superposition method or a nonlinear superposition method to obtain a superimposed energy value, wherein 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 by weighted superposition; performing normalization processing on the superimposed energy values ​​to obtain an enhanced detection result, wherein the enhanced detection result includes the position of the target main peak and the superimposed energy value; Generate a target detection result based on the enhanced detection result.

[0011] In one embodiment, the step of generating a target detection result based on the enhanced detection result includes: Calculating according to the enhanced detection result to obtain target motion parameters; Perform feature extraction based on the target motion parameters to obtain a target feature vector; The target feature vector is input into a preset neural network model to obtain a target detection result, which includes a target type and a target position.

[0012] In addition, to achieve the above-mentioned purpose, the present application also proposes a device for generating underwater target detection results based on a combined waveform, the device for generating underwater target detection results based on a combined waveform comprising: An acquisition module, used for acquiring linear frequency modulation pulse signals and single frequency signals; A combining module, configured to perform time domain combination of the linear frequency modulation pulse signal and the single frequency signal to obtain a combined waveform signal; a receiving module, configured to transmit the combined waveform signal to an underwater area and receive an echo signal fed back from the underwater area; a processing module, configured to perform matched filtering on the echo signal to generate a time-frequency detection graph; a screening module, configured to perform dynamic threshold screening on the time-frequency detection graph and output a candidate peak set, wherein the candidate peak set includes a target main peak and interference peaks; A search module is used to construct a two-dimensional search window for searching the candidate peak set according to the theoretical secondary peak interval to obtain secondary peaks; a verification module, configured to verify the secondary peak based on a preset attenuation law and output a secondary peak that satisfies the preset attenuation law; The result module is used to add the energy value of the secondary peak to the target main peak to generate a target detection result.

[0013] In addition, to achieve the above-mentioned purpose, the present application also proposes a medium, which is a computer-readable medium, and a computer program is stored on the medium. When the computer program is executed by the processor, the steps of the method for generating underwater target detection results based on combined waveforms as described above are implemented.

[0014] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the method for generating underwater target detection results based on combined waveforms as described above.

[0015] This application generates a time-frequency detection map by transmitting a combined waveform of a linear frequency-modulated pulse signal and a single-frequency signal to the underwater area, receiving the echo signal and performing matched filtering. A dynamic threshold is used to screen 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 on the target main peak to generate target detection results, effectively suppressing reverberation interference and improving underwater target detection accuracy in shallow waters with strong reverberation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0017] Figure 1 This is a flowchart of a first embodiment of the method for generating underwater target detection results based on combined waveforms of the present application; Figure 2 This is a time domain waveform diagram of the first embodiment of the method for generating underwater target detection results based on combined waveforms of the present application; Figure 3 This is a spectrum analysis diagram of the first embodiment of the method for generating underwater target detection results based on combined waveforms of the present application; Figure 4 This is a schematic diagram of candidate peak results after dynamic threshold screening according to the first embodiment of the method for generating underwater target detection results based on combined waveforms of the present application; Figure 5 This is a time-frequency diagram of target detection results according to the first embodiment of the method for generating underwater target detection results based on combined waveforms of the present application; Figure 6 This is a time-frequency diagram of the energy convergence result of the first embodiment of the method for generating underwater target detection results based on combined waveforms of this application; Figure 7 This is a flow chart of a second embodiment of the method for generating underwater target detection results based on combined waveforms of the present application; Figure 8 This is a flowchart of a third embodiment of the method for generating underwater target detection results based on a combined waveform of the present application; Figure 9 This is a schematic diagram of the module structure of the device for generating underwater target detection results based on a combined waveform according to the first embodiment of the method for generating underwater target detection results based on a combined waveform of the present application; Figure 10 Schematic diagram of the device structure of the hardware operating environment involved in the method for generating underwater target detection results based on combined waveforms in an embodiment of the present application.

[0018] 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 DESCRIPTION

[0019] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0020] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0021] In the field of underwater acoustic engineering, active sonar systems are a key technology for underwater target detection, positioning, and identification. They operate by transmitting sound waves and receiving echo signals reflected from the target, thereby acquiring relevant information about the target. However, in shallow waters, complex underwater conditions cause the sound waves emitted by sonar to encounter multiple scattering sources, such as the rough seabed, the undulating sea surface, seamounts, and schools of fish, generating a large amount of reverberation. These reverberation signals overlap with the target's echo signal, potentially drowning the target signal in the reverberation area, seriously affecting the detection effectiveness of active sonar.

[0022] Therefore, in order to overcome the above problems, the present application proposes a method for accurately generating underwater target detection results based on a combined waveform. The main solution of the embodiment of the present application is: obtaining a linear frequency modulation pulse signal and a single frequency signal; combining the linear frequency modulation pulse signal and the single frequency signal in the time domain to obtain a combined waveform signal; transmitting the combined waveform signal to the underwater area, and receiving the echo signal fed back from the underwater area; performing matched filtering on the echo signal to generate a time-frequency detection graph; performing dynamic threshold screening on the time-frequency detection graph to output a candidate peak set, wherein the candidate peak set includes the target main peak and the interference peak; constructing a two-dimensional search window for the candidate peak set according to the theoretical secondary peak interval to search and obtain a secondary peak; verifying the secondary peak based on a preset attenuation law, and outputting a secondary peak that meets the preset attenuation law; superimposing the energy value of the secondary peak on the target main peak to generate a target detection result.

[0023] Based on the above, the embodiment of the present application also provides a method for generating underwater target detection results based on combined waveforms, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the method for generating underwater target detection results based on combined waveforms of the present application.

[0024] In this embodiment, the method for generating underwater target detection results based on combined waveforms includes steps S10 to S80: Step S10: Acquire a linear frequency modulation pulse signal and a single frequency signal.

[0025] 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 key characteristic of this signal is its wide bandwidth, which provides high range resolution. Specifically, after matched filter processing, LFM signals can estimate target velocity through the Doppler effect while maintaining high range resolution. However, due to their broadband nature, LFM signals are sensitive to frequency offsets, meaning that even slight changes in velocity can cause the entire spectrum to shift, affecting velocity measurement accuracy.

[0026] Continuous Wave (CW) signals, on the other hand, have a fixed frequency and are commonly used to measure target velocity. The advantages of CW signals are their simplicity and ease of generation, along with their excellent velocity resolution. As a target moves relative to the sonar system, the frequency of the echo signal changes (known as the Doppler effect), and by analyzing this change, the target's velocity can be accurately determined. However, a limitation of CW signals is that they provide limited range information, as their spectrum consists of only a single frequency component. This limits their use in applications requiring high range resolution.

[0027] Step S20: combining the linear frequency modulation pulse signal and the single frequency signal in the time domain to obtain a combined waveform signal.

[0028] It's important to note that the LFM and CW signals are processed separately to generate a linear frequency modulated (PTFM) pulse train frequency modulated (PFM) signal and a windowed CW signal. This is then combined in the time domain to combine the advantages of both, overcoming the limitations of a single signal type. This combination provides high range resolution, while the windowed CW signal enhances velocity resolution. Furthermore, through proper design, this combined waveform effectively suppresses reverberation interference, reduces sidelobe levels, and improves detection performance.

[0029] Furthermore, the linear frequency modulation pulse signal is combined to obtain the linear frequency modulation pulse train signal. The specific formula is: in, is a rectangular function, and are the lowest frequency and the highest frequency of the linear frequency modulation pulse signal respectively, is the pulse length of the linear frequency modulated pulse signal, is the total number of combinations of linear frequency modulation pulse signals, is the number of combinations of the current linear frequency modulation pulse signal, Indicates the change of phase. The total duration of the linear frequency modulation pulse signal is ; The single-frequency signal is processed by window weighting to obtain a windowed single-frequency signal. The specific formula is: in, is the window function, is the time domain length of the single-frequency signal, is the frequency of the single-frequency signal; According to the time domain combination of the linear frequency modulation pulse signal and the windowed single frequency signal, a combined waveform signal is obtained. The duration of the combined waveform signal is PTFM is used to combine the Hamming windowed single frequency signal in the time domain to suppress the side lobes. Figure 2 The time domain waveform and Figure 3 The spectrum analysis diagram shown in the figure shows the signal parameters. =1s, =0.5s, =0.5s, =0.1s, =5, =500, =700Hz. It was observed that as the number of PTFM sub-pulses (LFMs) increases, the maximum sidelobe heights in the zero-Doppler section and the maximum sidelobe heights in the zero-delay section of the ambiguity function intersect at the case of five sections. In this case, both maximum sidelobes are at a relatively low level. To this end, a performance analysis chart compares four commonly used active sonar waveforms: a single-frequency (CW) signal, a linear frequency modulated (LFM) signal, a PTFM signal commonly used for anti-reverberation, and a combined waveform based on the correlation algorithm proposed in this implementation. The performance of these waveforms is evaluated based on the ambiguity function, a three-dimensional image that simultaneously describes the waveform's behavior in the range-velocity domain. The ambiguity function is a three-dimensional image of range and velocity. The two-dimensional section at range 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 mainlobe to the maximum sidelobe amplitude is called the velocity resolution section peak-to-sidelobe ratio, and the distance between the maximum sidelobe and the mainlobe is called the maximum sidelobe range offset. The two-dimensional cross-section corresponding to zero velocity 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 main lobe to the maximum sidelobe amplitude is called the range resolution cross-section peak-to-sidelobe ratio, and the distance between the maximum sidelobe and the mainlobe is called the maximum sidelobe velocity offset. The total duration of the signals selected for analysis is 1 second. The PTFM includes 10 0.1s linear frequency modulation pulses from 500-700 Hz, a single-frequency signal at 700 Hz, and a linear frequency modulation signal from 500-700 Hz. The analysis found that neither CW nor LFM is compatible with high range resolution and high velocity resolution. However, PTFM signals and combined signals, due to their comb spectrum characteristics, have both high range resolution and high velocity resolution. Moreover, the main lobe to side lobe ratio and the distance between the maximum main lobe and the main lobe in the two sections of the ambiguity function are at a medium level. It is particularly noted that the various indicators in the performance analysis chart are normalized to between 0 and 1, and the closer to 1, the better the performance. Since the two resolution indicators are very important for waveform selection and play a decisive role in target detection, they are weighted as 1, while the remaining four indicators are relatively less important and are weighted as 0.5.

[0030] Step S30: transmitting the combined waveform signal to the underwater area and receiving the echo signal fed back from the underwater area.

[0031] It should be noted that during the transmission phase, the sonar system's transducer converts the combined waveform into sound waves and transmits them underwater. This combined waveform is designed to optimize range and velocity resolution while effectively suppressing reverberation interference. During the transmission process, the propagation characteristics of sound waves in water, such as absorption and scattering, must be considered to ensure efficient signal coverage of the underwater area and return clear echo signals. When the sound waves encounter the target or obstacles in the environment, they are reflected, generating echo signals that return to the sonar system. The task of the reception phase is to accurately capture these echo signals and convert them into electrical signals for subsequent processing. Due to the complex and ever-changing ocean environment, echo signals contain information from multiple reflection sources at different directions and distances, including the target itself, as well as background noise and reverberation. Therefore, the received echo signals are often complex and require a series of sophisticated signal processing techniques to interpret them.

[0032] Step S40: performing matched filtering on the echo signal to generate a time-frequency detection graph.

[0033] It's important to note that after receiving the echo signal reflected from the underwater area, it needs to be processed using a filter that matches the transmitted signal. The matched filter is designed based on the combined transmitted waveform (linear frequency modulation (PTFM) and windowed single-frequency (CW) signal) to maximize the signal-to-noise ratio (SNR), 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 conjugated template signal (i.e., the transmitted signal).

[0034] Specifically, the entire combined signal is 1 second long, with a PTFM segment of 0.5 seconds. It contains five equal-length LFM signals with a frequency range of 500-700 Hz. The CW segment is 0.5 seconds long and uses a Hamming window. This combined signal is first used to generate a 10-second reverberation signal. A 2-8-second reverberation signal is then intercepted as the received signal. A signal with a signal-to-mix ratio of -34dB is added to the 6-7 second segment of the received signal, simulating a target speed of -10m / s. The received signal is then processed using a matched filter bank (MFB), a detection sample set consisting of a series of replica signals obtained by adding different frequency offsets to the transmitted signal. By correlating the replica signals with different frequency offsets with the received signal, the maximum gain is achieved on the detected time-frequency plot when the frequency offset of the echo signal in the received signal corresponds to the frequency offset replica in the MFB.

[0035] Furthermore, step S40 includes selecting multiple matched filters to construct a filter bank, each corresponding to a different Doppler frequency shift replica of the transmitted signal. The echo signal is then passed through the filter bank to obtain multiple time-domain output sequences. Fourier transforms are then performed on the multiple time-domain output sequences to obtain multiple time-frequency distribution maps. Finally, the multiple time-frequency distribution maps are arranged in order of frequency shift to obtain a time-frequency detection map. Specifically, in active sonar systems, to effectively address the Doppler shift 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 replica of the transmitted signal, 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 are adjusted according to the expected Doppler frequency shift. When the received echo signal passes through this filter bank, each filter outputs a time-domain output sequence. These output sequences reflect the correlation between the echo signal and the corresponding Doppler frequency shifted version of the transmitted signal. Theoretically, if a filter matches the Doppler shift of an actual target, its output will exhibit a significant energy peak, indicating the target's exact location and relative velocity. Next, the multiple time-domain output sequences from each matched filter are Fourier transformed (FFT). The Fourier transform is a mathematical tool that converts a time-domain signal into a frequency-domain representation, revealing the signal's frequency components. For active sonar systems, this translates into converting the energy distribution over time into a time-frequency energy distribution map—a time-frequency distribution map. These time-frequency distribution maps provide richer information about the echo signal, including but not limited to the target's range, velocity, and surrounding environmental characteristics. The multiple time-frequency distribution maps are then arranged in order of 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 target detection accuracy and reduces false alarms. As the intensity of reverberation decays over time, higher correlation values ​​appear in the front part of the time-frequency diagram, and the target echo will be submerged in these areas with strong correlation.

[0036] Step S50: Perform dynamic threshold screening on the time-frequency detection graph and output a candidate peak set.

[0037] It should be noted that after generating the time-frequency detection graph, we find that it contains a large amount of energy information from different time and frequency points. The initial candidate peak results contain a total of 6161 peak structures that meet the criteria. This information includes both valid echo signals from the target and various forms of interference, such as background noise and reverberation. Therefore, directly using a fixed threshold to screen candidate peaks may result in a large number of false positives or false negatives. To address this issue, a dynamic threshold screening method is necessary. The core concept of dynamic threshold screening is to adaptively adjust the threshold based on the local background noise level. Specifically, a sliding window technique is used to traverse the entire time-frequency detection graph and calculate an estimated background noise value within each window. Based on this estimated value, a relative threshold can be set to determine whether a peak within the current window should be considered a candidate peak. The advantage of this method is that it can effectively distinguish weak target echoes in areas with strong reverberation from actual noise peaks.

[0038] Furthermore, a dynamic threshold is generated based on a background estimate using a median filter. This calculation involves traversing the time-frequency detection graph using a median filter window. This process involves selecting an appropriate sliding window size, determined based on the temporal characteristics of the matching peaks in the combined waveform. This ensures that the impact of strong reverberation regions is effectively suppressed while preserving the characteristics of the matching peaks. The specific steps include: applying a median filter window to the time-frequency detection graph to obtain the median of the pixel values ​​within the window; using the median as the background estimate at the window center; 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 produce a more accurate estimate of the background noise level. Next, the calculated median is used as the background noise estimate at the window center, and a preset offset is added to this value to generate the dynamic threshold. The choice of this preset offset is crucial, as it determines the algorithm's sensitivity to the target signal. If the offset is too small, excessive 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 through empirical data to ensure that false alarms are reduced while maintaining a high target detection rate in complex environments. Through the above steps, each point on the time-frequency detection graph is assigned a dynamic threshold based on the local background noise level. This approach allows the threshold to be flexibly adjusted based on the actual background noise level, whether in areas of strong reverberation or relatively quiet, thereby improving target detection accuracy.

[0039] Furthermore, through dynamic threshold screening, the threshold can be flexibly adjusted according to changes in background noise, allowing even weaker target echoes located behind strong reverberation areas to be correctly identified, ultimately resulting in a candidate peak set consisting of the target's main peak and interference peaks. Step S50 specifically includes obtaining the pixel value corresponding to a pixel in the time-frequency detection map, comparing the pixel value with a dynamic threshold, and obtaining a comparison result. When the comparison result shows that the pixel value is greater than the dynamic threshold, the target pixel corresponding to the pixel value is output, and the position of the target pixel in the time-frequency detection map is recorded to obtain a candidate peak combination. Specifically, the pixel value corresponding to each pixel in the time-frequency detection map is obtained. The time-frequency detection map is essentially a Fourier transform that converts the received echo signal into an energy distribution map in time and frequency. 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 obtained pixel value is compared with the dynamic threshold previously generated based on the median filter background estimation. The introduction of dynamic thresholds is to adapt to the complexity and variability of the ocean environment, ensuring that weak target signals can be effectively identified even in strong reverberation or noise backgrounds. Specifically, for each pixel point, if its corresponding pixel value exceeds the dynamic threshold calculated at the position of the point, then the point is considered to be a potential target echo signal. This comparison operation can be regarded as a local adaptive screening mechanism, which can flexibly adjust the threshold according to changes in the background noise level to avoid excessive false alarm rates or omissions caused by fixed thresholds. When the comparison result shows that the pixel value is greater than the dynamic threshold, the target pixel point corresponding to the pixel value is output. This means that an energy peak containing target information is found in the time-frequency detection map. Recording the position of the target pixel point usually includes two dimensions of information: one is time (or distance), and the other is frequency (or Doppler shift). These two dimensions of data together determine the specific characteristics of a candidate peak. For example, a significant energy peak appears at a certain time point and frequency point, which may mean that at this moment a target moving at a specific speed reflects the sound wave signal. By recording all such target pixel points, we can obtain a set of candidate peaks, which will be used for subsequent fine screening and verification steps. As Figure 4 The schematic diagram of the candidate peak results after dynamic threshold screening is shown. The number of detected candidate peaks is only 1159. By comparison, it can be found that most of the reduced areas are mainly in the strong reverberation area in the front section, while the number of candidate peaks at the position where the target appears in the back has not decreased. On the other hand, it also reflects that the background estimation based on dynamic threshold can reduce the interference of reverberation.

[0040] Step S60 , constructing a two-dimensional search window for the candidate peak set according to the theoretical secondary peak interval to search for the secondary peaks.

[0041] It should be noted that a two-dimensional search window is constructed based on the theoretical secondary peak interval, and secondary peaks are searched and verified on this basis. This method can effectively distinguish the true target echo signal from the complex reverberation background.

[0042] Based on the matched filtering characteristics of the combined waveform, we know that a series of secondary peaks, distributed according to a specific pattern, surround the primary peak. The energy of these secondary peaks exhibits a gradually decreasing trend, and they have a clear positional relationship in the time dimension. Specifically, the time intervals between these 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 locations of its secondary peaks can be predicted using theoretical formulas. Based on this information, a two-dimensional search window can be constructed for each candidate peak (i.e., potential primary peak) to the left and right of it, based on the theoretical secondary peak spacing. When constructing the two-dimensional search window, various interference factors in the actual ocean environment, such as multipath effects and sea surface fluctuations, should be taken into account, which may cause the actual positions of secondary peaks to deviate from the theoretical values. Therefore, when determining the specific range of the search window, in addition to the theoretically calculated time interval, a certain tolerance range should be incorporated. For example, if theoretical calculations indicate that a secondary peak should appear at 0.5 seconds, a tolerance of ±0.03 seconds can be set, meaning the search should be conducted within the time range of 0.47 to 0.53 seconds. At the same time, a certain tolerance must also be considered in the velocity dimension to cope with the possible influence of Doppler frequency shift.

[0043] Once the two-dimensional search window is established, the next task is to find the maximum point within each window as a secondary peak. This means selecting the point with the highest energy within each search area to represent the presence of a secondary peak. This process not only helps us confirm whether there are secondary peaks that meet the expected pattern, but also effectively eliminates false peaks caused by background noise or non-target scatterers. In this embodiment, two two-dimensional search boxes of different sizes are provided. The main peak is marked with a five-pointed star, and the maximum value within the two-dimensional search box is selected as the secondary peak, marked with a cross. In combination with actual application scenarios, the matching peaks of the same target on the time-frequency graph are not much different in the speed dimension, so the width of the search box is selected to be 0.02m / s. However, considering the complex background of reverberation, the search interval in the time dimension is selected. Considering that the energy of the two secondary peaks near the main peak is relatively high, the search box length on the left and right of the main peak is 0.06s, but the center positions of the search boxes are separated by 0.1s. This is because two two-dimensional search boxes are constructed on both sides of the comb space on the matching peak.

[0044] Ultimately, all qualified secondary peaks obtained through the search will be used in the subsequent constraint verification stage, including energy attenuation verification, symmetry verification, and continuity verification, thereby further improving the reliability and accuracy of target detection.

[0045] Step S70 , verifying the secondary peak based on a preset attenuation law, and outputting the secondary peak that satisfies the preset attenuation law.

[0046] It should be noted that, based on the characteristics of the combined waveform after matched filtering, we know that there will be a series of secondary peaks around the main peak with energy decreasing step by step according to a specific rule. The energy attenuation pattern of these secondary peaks is determined in advance through theoretical calculations and experiments. Specifically, the energy of the secondary peaks usually shows a trend of decreasing step by step, 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 close to the main peak may be 50% of the energy of the main peak, while the second secondary peak may be 50% of the energy of the first secondary peak, and so on. Next, for each candidate peak (that is, 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 the preset attenuation law. This step includes several key preset attenuation laws: Energy decay verification: Checks whether the energy of each secondary peak conforms to the expected gradual decrease pattern. For example, if the energy of a secondary peak is significantly higher or lower than the expected value, it is likely that the secondary peak is not caused by the target echo.

[0047] Symmetry verification: Ensure that the number of secondary peaks on either side of the main peak is consistent and that their energy distribution is symmetrical. Asymmetrical energy distribution may indicate the presence of interference or other non-target factors.

[0048] Continuity Verification: Confirm that secondary peaks appear in order of distance from the primary peak, without skipping peaks. For example, the second secondary peak should not appear before the first secondary peak is detected.

[0049] By verifying these conditions, we can effectively filter out true secondary peaks that meet the preset attenuation patterns. Only those secondary peaks that fully meet all the constraints are considered valid and used in the final target detection results. This approach significantly improves target recognition accuracy, especially in complex marine environments, and can more reliably distinguish true target signals from background noise or reverberation interference.

[0050] Step S80 , superimposing the energy value of the secondary peak onto the target main peak to generate a target detection result.

[0051] 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 value of the main peak in a linear or nonlinear manner. Linear superposition is to directly add the energy value of the secondary peak to the main peak, while nonlinear superposition can assign different weights according to the energy attenuation law of the secondary peak to optimize the effect of energy superposition. 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; when the signal-to-noise ratio is low, a more relaxed strategy needs to be adopted to avoid erroneous suppression of the target signal. In addition, the results after energy superposition need to be normalized to ensure the uniformity and comparability of the detection results.

[0052] Furthermore, based on the superimposed and normalized energy values, an enhanced detection result is generated. This result not only includes the location information of the target main peak, but also includes the new energy value after energy superposition. Figure 5 The target detection results time-frequency diagram and Figure 6 The energy convergence result time-frequency diagram is used to converge the secondary peak energy onto the main peak. The convergence method is to first map the entire time-frequency diagram to a linear value, and then superimpose the linear value on the secondary peak onto the main peak. Then, the maximum value of the entire time-frequency diagram is taken for normalization. Finally, the processed result is displayed in dB. The final target appearance time is 7s, and the target speed is -10m / s, both of which meet the pre-set target parameters. Moreover, by converging the energy of the secondary peak onto the main peak, the main peak gain before energy convergence is -5.6dB, and the gain after energy convergence is -0.89dB, which is a gain increase of 4.7dB.

[0053] Next, based on these enhanced detection results, a detailed target detection result is generated. This target detection result not only includes the target's specific location (such as time-frequency coordinates) but also a preliminary assessment of the target's 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, the velocity information provided by the Doppler effect can be combined to further refine the target's motion state and trajectory.

[0054] This embodiment obtains a linear frequency modulated pulse signal and a single frequency signal; combines the linear frequency modulated 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 area and receives an echo signal fed back from the underwater area; performs matched filtering on the echo signal to generate a time-frequency detection map; performs dynamic threshold screening on the time-frequency detection map to output a candidate peak set, where the candidate peak set includes a target main peak and interference peaks; constructs a two-dimensional search window based on the theoretical secondary peak interval for searching the candidate peak set to obtain secondary peaks; verifies the secondary peaks based on a preset attenuation law and outputs secondary peaks that meet the preset attenuation law; and superimposes the energy value of the secondary peak on the target main peak to generate a target detection result. By transmitting a combined waveform of a linear frequency modulated pulse signal and a single frequency signal to an underwater area, receiving an echo signal and performing matched filtering, a time-frequency detection map is generated. A dynamic threshold is used to screen 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 on the target main peak to generate target detection results, effectively suppressing reverberation interference and improving the underwater target detection accuracy in shallow water strong reverberation environment.

[0055] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 7 The method for generating underwater target detection results based on combined waveforms in step S60 further includes steps S201 to S204: Step S201 : Calculate based on the combined waveform signal in the candidate peak set to obtain a theoretical secondary peak interval.

[0056] It's important to note that after a matched filter is applied to a combined waveform signal (such as a linear frequency modulated (PTFM) pulse train and a windowed single-frequency (CW) signal), a specific pattern is exhibited on a time-frequency plot. Specifically, this combined waveform forms a series of secondary peaks with decreasing energy around the primary peak. The presence and spacing of these secondary peaks are determined by the design parameters of the combined waveform, such as the number of subpulses in the PTFM segment, the frequency variation range, and the frequency of the CW segment. To calculate the theoretical secondary peak spacing, the structure of the combined waveform must first be analyzed. For example, a PTFM signal containing multiple LFM pulses produces a corresponding peak in the matched filtered output. The spacing between these peaks depends on the duration and frequency variation rate of the LFM pulses.

[0057] Step S202: construct a two-dimensional search window according to the theoretical secondary peak interval.

[0058] It's important to note that when constructing a two-dimensional search window, two dimensions must 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 tolerance (e.g., ±0.03 seconds) is typically added to the theoretical interval to ensure that secondary peaks are captured even with slight offsets. In the velocity dimension, while velocity variations for the same target echo are relatively small, an appropriate tolerance must still be set to account for possible Doppler shifts.

[0059] Through this method, potential secondary peaks can be effectively located in complex time-frequency detection maps, thereby improving the accuracy and reliability of target detection.

[0060] Step S203: Search the two-dimensional search window to obtain image points with energy equal to a preset value.

[0061] It's important to note that after constructing two-dimensional search windows around candidate primary peaks, the specific location and size of each window must be determined based on the theoretically calculated secondary peak spacing and tolerance. Each two-dimensional search window effectively defines a local region within which one or more secondary peaks may reside. The next task is to find energy peaks within these windows—image points whose energy reaches a preset value.

[0062] Specifically, this goal can be achieved by traversing 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 image point corresponding to the pixel is obtained and considered to be a potential secondary peak.

[0063] Step S204: taking the image point of the preset value as the secondary peak.

[0064] This embodiment calculates the theoretical secondary peak interval of the combined waveform, constructs a two-dimensional search window, and searches for image points whose energy reaches a preset value to identify secondary peaks, thereby improving target detection accuracy and reliability and effectively distinguishing target echoes from background noise.

[0065] Based on the first embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the first embodiment can be referred to the above introduction and will not be described in detail later. Figure 8 The step S80 of generating underwater target detection results based on the combined waveform further includes steps S301 to S303: Step S301 : The energy of the secondary peak is added to the energy value of the target main peak in a linear or nonlinear superposition manner to obtain a superimposed energy value.

[0066] It should be noted that linear superposition is the most direct method, in which the energy value of each secondary peak is directly added to the energy value of the corresponding target primary peak. This method is suitable for situations where the energy difference between secondary peaks and primary peaks is small and background noise is relatively low. For example, if a primary peak is surrounded by three significant secondary peaks, the energy values ​​of these three secondary peaks are added to the energy value of the primary peak, significantly enhancing the signal strength of the primary peak. This simple superposition method can effectively improve the signal-to-noise ratio and make the target echo more prominent. Nonlinear superposition is more flexible and sophisticated. It weights the energy of the secondary peaks before superimposing them onto the energy value of the primary peak. This method allows for the weighting of the secondary peaks to be adjusted based on their importance 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 the primary peak, so they can be given a higher weight. Furthermore, the weighting can be dynamically adjusted based on the energy decay of the secondary peaks to ensure that the energy distribution after superposition is more accurate.

[0067] Regardless of which superposition method is used to superimpose the energy and obtain the superimposed energy value, this method not only improves the accuracy of target recognition, but also can better deal with interference factors in the complex and changeable marine environment.

[0068] Step S302: performing normalization processing on the superimposed energy values ​​to obtain enhanced detection results.

[0069] It should be noted that after superimposing the energy of the secondary peaks onto the primary peak, the resulting enhanced energy values ​​may be distributed over a wide range of values. To ensure that these energy values ​​can be compared and analyzed under a unified standard, they need to be normalized. Normalization generally involves mapping the data to a specific range (e.g., between 0 and 1) so that energy values ​​at different locations are comparable. This can be achieved through linear transformations or other nonlinear methods.

[0070] Step S303: Generate a target detection result based on the enhanced detection result.

[0071] It should be noted that the normalized energy value provides an enhanced detection result, which not only includes the specific location information of the target main peak, but also includes the energy distribution after energy superposition and normalization. The final target detection result is obtained by processing according to the enhanced detection result.

[0072] Furthermore, step S303 also includes performing calculations based on the enhanced detection results to obtain target motion parameters, performing feature extraction 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 a target detection result, which includes the target type and target location. Specifically, calculations are performed based on the enhanced detection results to obtain target motion parameters. These parameters include, but are not limited to, the target's speed, direction, and trajectory. By analyzing the Doppler shift information in the echo signal, the target's speed can be accurately estimated; by combining the time-frequency distribution in the time-frequency graph, the target's direction of movement can also be inferred. Furthermore, by utilizing detection data from multiple consecutive time points, filtering and smoothing algorithms (such as Kalman filtering) can be used to optimize the estimation of the target's trajectory, thereby obtaining more stable and accurate target motion parameters. Next, feature extraction is performed based on the target motion parameters to generate a target feature vector. Feature extraction is the process of converting raw data into a representative feature representation. Common features used in sonar target recognition include spectral features, time-domain features, and features based on energy distribution. For example, the energy distribution of specific frequency bands can be extracted from the enhanced time-frequency graph as features. Alternatively, the autocorrelation function of the echo signal can be calculated to extract periodic and non-periodic components as features. These features together form a feature vector that describes the target's characteristics. The extracted target feature vector is then input into a pre-set neural network model to make a final determination of the target's type and location. This neural network model is trained based on a large amount of annotated data. Based on the feature vector input, the neural network outputs a probability distribution of target types and determines the most likely target category (such as a submarine, a school of fish, or other underwater object). Furthermore, based on previously calculated motion parameters, the target's position information can be further refined, providing more precise coordinate positioning.

[0073] This embodiment obtains enhanced detection results by linearly or nonlinearly superimposing the secondary peak energy to the main peak and normalizing it, and finally generates a target detection report, thereby improving target detection accuracy and signal-to-noise ratio, effectively distinguishing targets from background noise, and improving system reliability and adaptability.

[0074] Based on the first embodiment of the present application, the present application also provides a device for generating underwater target detection results based on a combined waveform, please refer to Figure 9 , the device comprises: The acquisition module 10 is used to acquire a linear frequency modulation pulse signal and a single frequency signal.

[0075] The combining module 20 is used to combine the linear frequency modulation pulse signal and the single frequency signal in the time domain to obtain a combined waveform signal.

[0076] 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.

[0077] The processing module 40 is used to perform matched filtering on the echo signal to generate a time-frequency detection graph.

[0078] The screening module 50 is used to perform dynamic threshold screening on the time-frequency detection graph and output a candidate peak set, where the candidate peak set includes a target main peak and interference peaks.

[0079] The search module 60 is used to construct a two-dimensional search window based on the theoretical secondary peak interval to search the candidate peak set and obtain secondary peaks.

[0080] The verification module 70 is used to verify the secondary peak based on a preset attenuation law and output the secondary peak that meets the preset attenuation law.

[0081] The result module 80 is used to add the energy value of the secondary peak to the target main peak to generate a target detection result.

[0082] The combined waveform-based underwater target detection result generation device provided in this application, which employs the combined waveform-based underwater target detection result generation method of the aforementioned embodiment, can solve the technical problem of how to improve the accuracy of target detection in shallow water environments. Compared with the prior art, the combined waveform-based underwater target detection result generation device provided in this application has the same beneficial effects as the combined waveform-based underwater target detection result generation method provided in the aforementioned embodiment. Other technical features of the combined waveform-based underwater target detection result generation device are the same as those disclosed in the aforementioned embodiment method and are not further elaborated here.

[0083] In one embodiment, the combination module 20 is further used to combine the linear frequency modulation pulse signal to obtain a linear frequency modulation pulse train signal; perform window weighting processing on the single frequency signal to obtain a windowed single frequency signal; and perform time domain combination on the linear frequency modulation pulse train signal and the windowed single frequency signal to obtain a combined waveform signal.

[0084] In one embodiment, the processing module 40 is further used to select multiple matched filters to construct a filter group, where the matched filters correspond to different Doppler frequency offset copies of the transmitted signal; pass the echo signal through the filter group to obtain multiple groups of time domain output sequences; perform Fourier transform on the multiple time domain output sequences to obtain multiple time-frequency distribution graphs; and arrange the multiple time-frequency distribution graphs in order of frequency offset to obtain a time-frequency detection graph.

[0085] In one embodiment, the screening module 50 is further used to obtain a pixel value corresponding to a 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 a 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.

[0086] In one embodiment, the search module 60 is further used to calculate based on the combined waveform signal in the candidate peak set to obtain a theoretical secondary peak interval; construct a two-dimensional search window based on the theoretical secondary peak interval; search the two-dimensional search window to obtain image points with energy of a preset value; and use the image points with the preset value as secondary peaks.

[0087] In one embodiment, the result module 80 is also used to linearly superimpose the energy of the secondary peak on the energy value of the target main peak or nonlinearly superimpose it 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 by weighted superposition. Normalization processing is performed according to the superimposed energy value to obtain an enhanced detection result, and the enhanced detection result 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.

[0088] In one embodiment, the result module 80 is further used to perform calculations based on the enhanced detection results to obtain target motion parameters; perform feature extraction based on the target motion parameters to obtain a target feature vector; and input the target feature vector into a preset neural network model to obtain a target detection result, which includes a target type and a target position.

[0089] The present application provides a device for generating underwater target detection results based on a combined waveform. The device for generating underwater target detection results based on a combined waveform includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for generating underwater target detection results based on a combined waveform in the above-mentioned embodiment one.

[0090] Reference below Figure 10, which shows a schematic diagram of the structure of a device for generating underwater target detection results based on a combined waveform suitable for implementing the embodiments of the present application. The device for generating underwater target detection results based on a combined waveform in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), 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 device for generating underwater target detection results based on a combined waveform is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0091] like Figure 10 As shown, the device for generating underwater target detection results based on a combined waveform may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the device for generating underwater target detection results based on a combined waveform. Processing device 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 may be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, hard disk, etc.; and a communication device 1009. The communication device 1009 may allow the device for generating underwater target detection results based on a combined waveform to communicate wirelessly or wired with other devices to exchange data. While the figure shows various devices for generating underwater target detection results based on a combined waveform, it should be understood that not all of the illustrated devices are required to be implemented or present. More or fewer of the devices may alternatively be implemented or present.

[0092] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0093] The combined waveform-based underwater target detection result generation device provided in this application, which employs the combined waveform-based underwater target detection result generation method of the aforementioned embodiment, can solve the technical problem of how to improve the accuracy of target detection in shallow water environments. Compared with the prior art, the combined waveform-based underwater target detection result generation device provided in this application has the same beneficial effects as the combined waveform-based underwater target detection result generation method provided in the aforementioned embodiment, and the other technical features of the combined waveform-based underwater target detection result generation device are the same as those disclosed in the method of the aforementioned embodiment, and are not further described here.

[0094] 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 one or more embodiments or examples in a suitable manner.

[0095] 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.

[0096] The present application provides a computer-readable medium having computer-readable program instructions (ie, a computer program) stored thereon, and the computer-readable program instructions are used to execute the method for generating underwater target detection results based on a combined waveform in the above-mentioned embodiment.

[0097] 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: an electrical connection with one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable medium may be any tangible medium that contains or stores 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.

[0098] The computer-readable medium may be included in the device for generating underwater target detection results based on a combined waveform; or may exist independently without being incorporated into the device for generating underwater target detection results based on a combined waveform.

[0099] The computer-readable medium carries one or more programs. When executed by a device for generating underwater target detection results based on a combined waveform, the device can write computer program code for performing the operations of the present 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++, as well as conventional procedural programming languages ​​such as C 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 the case of a remote computer, 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).

[0100] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based implementation that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0101] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0102] The computer-readable medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned method for generating underwater target detection results based on combined waveforms. This computer-readable medium addresses the technical problem of improving the accuracy of target detection in shallow water environments. Compared to the prior art, the beneficial effects of the computer-readable medium provided in this application are similar to those of the method for generating underwater target detection results based on combined waveforms provided in the aforementioned embodiments, and are not further elaborated here.

[0103] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for generating underwater target detection results based on combined waveforms.

[0104] The computer program product provided in this application can solve the technical problem of improving the accuracy of target detection in shallow water 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 combined waveform-based underwater target detection result generation method provided in the above-mentioned embodiment, and will not be further elaborated here.

[0105] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for generating underwater target detection results based on combined waveforms, characterized in that: The method comprises: Obtain linear frequency modulated pulse signal and single frequency signal; Combining the linear frequency modulation 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 area and receiving an echo signal fed back from the underwater area; Performing matched filtering on the echo signal to generate a time-frequency detection graph; Performing dynamic threshold screening on the time-frequency detection graph to output a candidate peak set, wherein the candidate peak set includes a target main peak and interference peaks; Constructing a two-dimensional search window for the candidate peak set according to the theoretical secondary peak interval to search and obtain secondary peaks; Verifying the secondary peak based on a preset attenuation law, and outputting a secondary peak that satisfies the preset attenuation law; The energy value of the secondary peak is superimposed on the target main peak to generate a target detection result.

2. The method according to claim 1, wherein The step of combining the linear frequency modulation pulse signal and the single frequency signal in the time domain to obtain a combined waveform signal comprises: The linear frequency modulation pulse signal is combined to obtain a linear frequency modulation pulse train signal. The specific formula is: in, is a rectangular function, and are the lowest frequency and the highest frequency of the linear frequency modulation pulse signal respectively, is the pulse length of the linear frequency modulation pulse signal, is the total number of combinations of the linear frequency modulation pulse signals, is the number of combinations of the current linear frequency modulation pulse signal, Indicates the change of phase, the total duration of the linear frequency modulation pulse signal is ; The single-frequency signal is subjected to window weighting processing to obtain a windowed single-frequency signal. The specific formula is: in, is the window function, is the time domain length of the single frequency signal, is the frequency of the single-frequency signal; The linear frequency modulation pulse signal and the windowed single frequency signal are combined in time domain to obtain a combined waveform signal, the duration of which is .

3. The method according to claim 1, wherein The step of performing matched filtering on the echo signal to generate a time-frequency detection graph includes: Selecting a plurality of matched filters to construct a filter bank, wherein the matched filters correspond to different Doppler frequency offset copies of the transmitted signal; Passing the echo signal through the filter bank to obtain multiple groups of time domain output sequences; Performing Fourier transform on the multiple time-domain output sequences to obtain multiple time-frequency distribution graphs; Arrange the multiple time-frequency distribution graphs in the order of frequency deviation to obtain a time-frequency detection graph.

4. The method according to claim 1, wherein The step of performing dynamic threshold screening on the time-frequency detection graph and outputting a candidate peak set includes: Obtaining a pixel value corresponding to a pixel point in the time-frequency detection image; Comparing the pixel value with the dynamic threshold to obtain a comparison result; When the comparison result is that the pixel value is greater than the dynamic threshold, outputting the target pixel point corresponding to the pixel value; The position of the target pixel point in the time-frequency detection image is recorded to obtain a candidate peak combination.

5. The method according to claim 1, wherein The step of searching the candidate peak set to obtain secondary peaks includes: Calculating based on the combined waveform signal in the candidate peak set to obtain a theoretical secondary peak interval; constructing a two-dimensional search window according to the theoretical secondary peak interval; Searching the two-dimensional search window to obtain image points with energy values ​​of preset values; The image point of the preset value is taken as the secondary peak.

6. The method according to claim 1, wherein The step of superimposing the energy value of the secondary peak onto the target main peak to generate a target detection result includes: The energy of the secondary peak is added to the energy value of the target main peak in a linear superposition method or a nonlinear superposition method to obtain a superimposed energy value, wherein 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 by weighted superposition; performing normalization processing on the superimposed energy values ​​to obtain an enhanced detection result, wherein the enhanced detection result includes the position of the target main peak and the superimposed energy value; Generate a target detection result based on the enhanced detection result.

7. The method according to claim 6, wherein The step of generating a target detection result based on the enhanced detection result includes: Calculating according to the enhanced detection result to obtain target motion parameters; Perform feature extraction based on the target motion parameters to obtain a target feature vector; The target feature vector is input into a preset neural network model to obtain a target detection result, which includes a target type and a target position.

8. A device for generating underwater target detection results based on combined waveforms, characterized in that: The device comprises: An acquisition module, used for acquiring linear frequency modulation pulse signals and single frequency signals; A combining module, configured to perform time domain combination of the linear frequency modulation pulse signal and the single frequency signal to obtain a combined waveform signal; a receiving module, configured to transmit the combined waveform signal to an underwater area and receive an echo signal fed back from the underwater area; a processing module, configured to perform matched filtering on the echo signal to generate a time-frequency detection graph; a screening module, configured to perform dynamic threshold screening on the time-frequency detection graph and output a candidate peak set, wherein the candidate peak set includes a target main peak and interference peaks; A search module is used to construct a two-dimensional search window for searching the candidate peak set according to the theoretical secondary peak interval to obtain secondary peaks; a verification module, configured to verify the secondary peak based on a preset attenuation law and output a secondary peak that satisfies the preset attenuation law; The result module is used to add the energy value of the secondary peak to the target main peak to generate a target detection result.

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

10. A storage medium, characterized in that: The storage medium stores a program for generating underwater target detection results based on a combined waveform. When the program for generating underwater target detection results based on a combined waveform is executed by a processor, the steps of the method for generating underwater target detection results based on a combined waveform as described in any one of claims 1 to 7 are implemented.

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