Target motion element resolving method based on sonar beam domain line spectrum matching

Abstract

The invention relates to a target motion element resolving method based on sonar beam domain line spectrum matching. The method comprises the following steps: acquiring navigation data of an observation platform, beam azimuth data of a tracking target and beam time domain data of the tracking target; performing time domain accumulation and line spectrum frequency point extraction on the wave beam time domain data to obtain a candidate line spectrum frequency point set; the candidate line spectrum frequency point set comprises line spectrum frequency points of a tracking target and interference line spectrum frequency points; based on preset Doppler factor parameter data, calculating an approximate value of a Doppler factor corresponding to each line spectrum frequency point in the candidate line spectrum frequency point set to obtain an initial Doppler spectrum approximate value; and calculating a frequency spectrum corresponding to each target course according to a preset number of target courses, the navigation data, the beam azimuth data and the initial Doppler frequency spectrum approximate value, matching the frequency spectrum with the candidate line spectrum frequency point set, confirming line spectrum frequency points of the tracking target, and obtaining state data of the tracking target. By adopting the method, the target line spectrum frequency measurement accuracy and the calculation result reliability can be improved.

Description

Technical Field

[0001] This invention belongs to the field of target motion analysis, and in particular relates to a method for solving target motion elements using sonar beam domain line spectrum matching. Background Technology

[0002] Target motion analysis is a research direction within the field of passive positioning. While methods for calculating target motion elements based solely on azimuth require less observation information and are simple in principle, they demand that the observation platform undergo at least one change of direction or speed maneuver. In practical applications, this observability condition is not frequently met, making its application relatively demanding. Combining azimuth with other information for simultaneous calculation can significantly reduce the observability requirements. The target radiation noise received by passive sonar often contains stable line spectra. When the observation platform maintains uniform linear motion, pure azimuth calculation fails. However, by utilizing the line spectrum frequency information within the target tracking beam, the conditions for calculating target motion elements are still met. Therefore, when the observation platform maintains uniform linear motion, the method of calculating target azimuth combined with beam domain frequency has strong application value.

[0003] Mechanistically, after extracting the line spectrum frequency within the sonar beam domain, azimuth observation equations and frequency measurement equations can be established separately. These two equations, when combined, can solve for the target motion elements, eliminating the need for the observation platform to perform directional or speed-changing maneuvers. Traditionally, this approach involves first obtaining accurate line spectrum frequencies in the beam domain, and then combining azimuth and frequency measurement information to calculate the target motion elements. However, it's worth noting that under the influence of marine environmental noise interference, platform self-noise interference, and Doppler motion, the above-mentioned traditional method faces two significant practical problems: First, the target beam domain line spectrum often exhibits intermittent and discontinuous fluctuations, making it difficult to obtain stable and accurate line spectrum frequencies. This is especially true under low signal-to-noise ratio conditions, leading to large estimation errors in the received line spectrum frequencies, resulting in long calculation times for target motion elements, and often causing divergence and non-convergence. Second, the number of line spectra within the beam domain is often not unique; the measurement platform and environmental noise also contain interfering line spectra. Quickly identifying the target line spectrum from numerous unknown attribute line spectra is not easy, which compromises the accuracy and reliability of the calculation results.

[0004] In summary, the biggest challenge in applying this method is determining the target spectrum frequency. Since the frequency of the target spectrum cannot be known in advance, traditional methods cannot guarantee the accuracy of the target spectrum frequency measurement under low signal-to-noise ratio and interference conditions. This often leads to unreliable or even divergent results, which is currently the biggest limitation on the application of this method. Summary of the Invention

[0005] Therefore, it is necessary to provide a target motion element calculation method using sonar beam domain line spectrum matching that can improve the accuracy of target line spectrum frequency measurement and the reliability of calculation results, in order to address the above-mentioned technical problems.

[0006] In a first aspect, this application provides a method for solving target motion elements using sonar beam domain line spectrum matching, including:

[0007] Acquire navigation data from the observation platform, beam azimuth data of the tracked target, and beam time-domain data of the tracked target; the initial sampling time of the beam azimuth data and the beam time-domain data is the same;

[0008] Time-domain accumulation and line spectrum frequency point extraction are performed on the beam time-domain data to obtain a candidate line spectrum frequency point set; the candidate line spectrum frequency point set includes the line spectrum frequency points of the tracked target and the interference line spectrum frequency points;

[0009] Based on the preset Doppler factor parameter data, the approximate value of the Doppler factor corresponding to each line spectrum frequency point in the candidate line spectrum frequency point set is calculated to obtain the initial Doppler spectrum approximate value.

[0010] Based on a preset number of target headings, navigation data, beam azimuth data, and initial Doppler spectrum approximations, the spectrum corresponding to each target heading is calculated, and the spectrum is matched with the candidate line spectrum frequency point set to confirm the line spectrum frequency point of the tracked target and obtain the status data of the tracked target.

[0011] Furthermore, based on the preset Doppler factor parameter data, approximate values ​​of the Doppler factors corresponding to each line spectrum frequency point in the candidate line spectrum frequency point set are calculated to obtain the initial Doppler spectrum approximation, including:

[0012] Based on each Doppler factor parameter, time-domain linear interpolation is performed on the beam time-domain data to obtain the interpolated time-domain data corresponding to each Doppler factor parameter.

[0013] The interpolated time-domain data is transformed into the frequency domain to obtain the interpolated frequency-domain data;

[0014] The interpolated frequency domain data corresponding to each Doppler factor parameter are accumulated according to the frequency points of each line spectrum in the candidate line spectrum frequency point set to obtain the interpolated energy spectrum set.

[0015] The estimated Doppler factor value corresponding to the initial sampling time is obtained from the interpolated energy spectrum set and used as the initial Doppler spectrum approximation.

[0016] Furthermore, the estimated Doppler factor values ​​corresponding to the initial sampling time are obtained from the interpolated energy spectrum set, including:

[0017] The Radon transform is performed on the energy spectrum corresponding to each Doppler factor parameter in the interpolated energy spectrum set to obtain the Radon transform dataset.

[0018] Extract the peak coordinates of the Radon transform data corresponding to each Doppler factor parameter in the Radon transform dataset, and calculate the estimated Doppler factor value corresponding to the initial sampling time based on the peak coordinates using the following formula:

[0019] β0·cosθ+t0·sinθ=d

[0020] Where β0 is the estimated value of the Doppler factor at the initial sampling time, θ is the angle data in the peak coordinates, t0 is the initial sampling time, and d is the distance from the origin in the peak coordinates.

[0021] Furthermore, based on a preset number of target headings, navigation data, beam azimuth data, and initial Doppler spectrum approximations, the spectrum corresponding to each target heading is calculated, and the spectrum is matched with a set of candidate line spectrum frequency points to confirm the line spectrum frequency points of the tracked target, thereby obtaining the status data of the tracked target, including:

[0022] Based on beam azimuth data, navigation data, and initial Doppler spectrum approximations, the predicted speed values ​​for each target heading at the initial sampling time are calculated to obtain a set of predicted speed values.

[0023] By combining beam azimuth data, airspeed prediction value set and navigation data, the Doppler factor search value of each target heading is calculated to obtain the Doppler factor search value set;

[0024] Based on the Doppler factor search value set, time-domain linear interpolation and time-domain coherent accumulation are performed on the beam time-domain data of each target heading to obtain the heading prediction spectrum corresponding to each target heading;

[0025] Based on the predicted course spectrum and candidate line spectrum frequency point set corresponding to each target course, the real-time course, real-time speed and monitoring line spectrum frequency point set of the tracked target are determined;

[0026] Based on real-time heading, real-time speed, beam azimuth data, and navigation data, the initial position of the tracked target is calculated, and the initial distance between the tracked target and the observation platform is obtained.

[0027] Status data is obtained based on real-time heading, real-time speed, initial distance, and a set of monitoring line spectrum frequency points.

[0028] Furthermore, based on beam azimuth data, navigation data, and initial Doppler spectrum approximations, the predicted speed values ​​for each target heading at the initial sampling time are calculated, resulting in a set of predicted speed values, including:

[0029] For each target heading, the predicted speed is calculated using the following formula, based on beam azimuth data, navigation data, and initial Doppler spectrum approximations:

[0030]

[0031] Among them, V r It is the predicted speed value for the r-th target heading. This is an approximation of the initial Doppler spectrum, where c is the speed of sound in water. It is the speed of the observation platform in the navigation data. F0 is the observation platform's heading in the navigation data, and F0 is the beam azimuth data at the initial moment;

[0032] Based on the predicted speed values ​​corresponding to each target heading, a set of predicted speed values ​​is obtained.

[0033] Furthermore, based on the predicted course spectrum and candidate line spectrum frequency point set corresponding to each target course, the real-time course, real-time speed, and monitoring line spectrum frequency point set of the tracked target are determined, including:

[0034] Match each line spectrum frequency point in the candidate line spectrum frequency point set with the heading prediction spectrum corresponding to each target heading, select the frequency intensity of each line spectrum frequency point in the heading prediction spectrum corresponding to each target heading, and obtain the frequency energy intensity data of the line spectrum frequency point.

[0035] From the frequency energy intensity data corresponding to each line spectrum frequency point, select the line spectrum frequency points where the frequency intensity is the extreme value in the corresponding heading prediction spectrum to obtain the monitoring line spectrum frequency point set;

[0036] The target heading is obtained by predicting the heading spectrum based on the frequency intensity extreme value.

[0037] Real-time speed is obtained based on real-time heading, navigation data, and beam azimuth data.

[0038] Furthermore, time-domain accumulation and line spectrum frequency point extraction are performed on the beam time-domain data to obtain a candidate line spectrum frequency point set, including:

[0039] Based on the preset time window parameters, the beam time domain data is accumulated in time sequence to obtain the accumulated beam time domain data.

[0040] Short-time Fourier transform is performed on the time-domain data of the accumulated beam to obtain the frequency-domain accumulated beam data;

[0041] Select line spectrum frequency points that meet the preset line spectrum frequency requirements from the frequency domain cumulative beam data to obtain a candidate line spectrum frequency point set.

[0042] Secondly, this application also provides a target motion element calculation device using sonar beam domain line spectrum matching, comprising:

[0043] The data acquisition module is used to acquire navigation data from the observation platform, beam azimuth data of the tracked target, and beam time domain data of the tracked target; the initial sampling time of the beam azimuth data and the beam time domain data is the same;

[0044] The spectrum calculation module is used to perform time-domain accumulation and line spectrum frequency point extraction on the beam time-domain data to obtain a candidate line spectrum frequency point set; the candidate line spectrum frequency point set includes the line spectrum frequency points of the tracking target and the interference line spectrum frequency points;

[0045] The Doppler value calculation module is used to calculate the approximate value of the Doppler factor corresponding to each line spectrum frequency point in the candidate line spectrum frequency point set based on the preset Doppler factor parameter data, so as to obtain the initial Doppler spectrum approximate value.

[0046] The target state calculation module is used to calculate the spectrum corresponding to each target's heading based on a preset number of target headings, navigation data, beam azimuth data, and initial Doppler spectrum approximations. It then matches the spectrum with a set of candidate line spectrum frequency points to confirm the line spectrum frequency points of the tracked target and obtain the target's state data.

[0047] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any of the target motion element calculation methods using sonar beam domain line spectrum matching described in the first aspect of this application.

[0048] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the target motion element calculation methods described in the first aspect of this application using sonar beam domain line spectrum matching.

[0049] The aforementioned method for calculating target motion elements using sonar beam domain line spectrum matching involves acquiring navigation data from the observation platform, beam azimuth data of the tracked target, and beam time-domain data of the tracked target. The initial sampling times of the beam azimuth data and beam time-domain data are the same. Time-domain accumulation and line spectrum frequency point extraction are performed on the beam time-domain data to obtain a candidate line spectrum frequency point set. This candidate set includes the line spectrum frequencies of the tracked target and interference line spectrum frequencies. Based on preset Doppler factor parameter data, the corresponding line spectrum frequencies of each line spectrum frequency point in the candidate set are calculated. The approximate value of the Doppler factor is used to obtain the initial Doppler spectrum approximation. Based on a preset number of target headings, navigation data, beam azimuth data, and the initial Doppler spectrum approximation, the spectrum corresponding to each target heading is calculated, and the spectrum is matched with the candidate line spectrum frequency point set to confirm the line spectrum frequency point of the tracked target, thus obtaining the status data of the tracked target. This achieves automatic matching of the target line spectrum frequency of the tracked target without the need for accurate confirmation of the target line spectrum frequency and under conditions of low signal-to-noise ratio and interference, effectively improving the accuracy of target line spectrum frequency measurement and the reliability of the solution results. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 A flowchart illustrating a method for solving target motion elements using sonar beam domain line spectrum matching, provided as an embodiment of this application;

[0052] Figure 2 A flowchart illustrating a method for solving target motion elements using beam domain line spectrum, provided as an embodiment of this application;

[0053] Figure 3 A schematic diagram of the motion geometry model of a tracking target and an observation platform provided in one embodiment of this application;

[0054] Figure 4 A flowchart illustrating the principle of rapid estimation of Doppler factors provided in one embodiment of this application;

[0055] Figure 5 A schematic diagram of a Doppler factor matching search and temporal coherent accumulation principle provided in one embodiment of this application;

[0056] Figure 6 This is a coherent cumulative matching result provided in one embodiment of this application;

[0057] Figure 7 A graph showing the change of the target initial distance solution value over time, provided as an embodiment of this application;

[0058] Figure 8 A graph showing the change of a target velocity solution value over time, provided as an embodiment of this application;

[0059] Figure 9 A graph showing the change of target heading solution value over time, provided as an embodiment of this application;

[0060] Figure 10 This is a schematic diagram of a target motion element solving device that utilizes sonar beam domain line spectrum matching, provided as an embodiment of this application. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0062] First, a brief introduction to the terms used in the embodiments of this application will be given.

[0063] The Doppler effect refers to the phenomenon where, when there is relative motion between a wave source and an observer, the frequency of the wave received by the observer differs from the frequency emitted by the wave source. When the two sources are closer, the received frequency is higher; when they are farther apart, the received frequency is lower. The physical principle behind this is that relative motion alters the effective wavelength of the wave during propagation, thus changing the number of wave crests reaching the observer per unit time, i.e., the frequency.

[0064] Doppler factor: In the calculation of Doppler frequency shift, the Doppler factor is a very important dimensionless parameter that quantitatively describes the degree to which relative motion changes the frequency. It is defined as the relative rate of change between the frequency received by the observer and the frequency emitted by the wave source, and is approximately equal to the ratio of radial velocity (the velocity component along the line connecting the observer and the wave source, positive when closer and negative when farther away) to the wave propagation speed.

[0065] In one embodiment, such as Figure 1 As shown, a method for calculating target motion elements using sonar beam domain line spectrum matching is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps S101-S104, wherein:

[0066] S101, acquire navigation data of the observation platform, beam azimuth data of the tracked target, and beam time domain data of the tracked target; the initial sampling time of the beam azimuth data and the beam time domain data is the same.

[0067] Specifically, the terminal acquires navigation data from the observation platform in real time, including the platform's heading angle and speed. The heading angle refers to the platform's direction of movement, typically expressed as an angle, with the reference direction usually being true north (geographic North Pole). Speed ​​refers to the platform's speed relative to the water column, usually measured in knots, which can be converted to meters per second. Both the heading angle and speed are obtained from instruments on the observation platform. In addition, the terminal acquires beam azimuth data and beam time-domain data of the tracked target; these are obtained through the observation platform's sonar system. The beam azimuth data characterizes the target azimuth angle determined by beamforming between the observation platform and the tracked target, while the beam time-domain data characterizes a segment of acoustic time-domain signal recorded in the beam at the aforementioned target azimuth angle. Furthermore, the initial sampling times for both the beam azimuth data and the beam time-domain data are the same.

[0068] S102, perform time-domain accumulation and line spectrum frequency point extraction on the beam time-domain data to obtain a candidate line spectrum frequency point set; the candidate line spectrum frequency point set includes the line spectrum frequency points of the tracking target and the interference line spectrum frequency points.

[0069] Specifically, the terminal stitches together the beam time-domain data according to a preset time window parameter to form a longer continuous time-domain signal. It then performs frequency domain conversion on this continuous time-domain signal and extracts potential line spectrum frequencies generated by the tracking target, forming a candidate line spectrum frequency set containing multiple line spectrum frequencies. This candidate line spectrum frequency set includes line spectrum frequencies emitted by the tracking target and interference line spectrum frequencies generated by noise. For example, the criteria for extracting the candidate line spectrum frequency set can be set according to actual work requirements, and the time window parameter can be set according to the sampling time length of the beam time-domain data in actual work.

[0070] S103, based on the preset Doppler factor parameter data, calculate the approximate value of the Doppler factor corresponding to each line spectrum frequency point in the candidate line spectrum frequency point set, and obtain the initial Doppler spectrum approximate value.

[0071] Specifically, the preset Doppler factor parameter data includes a preset number of Doppler factors. The terminal resamples and transforms the aforementioned beam time-domain data for each Doppler factor, then sums the resampled spectrum at the frequency points of the aforementioned candidate line spectrum frequency point set to obtain a two-dimensional matrix representing the total energy. Finally, a Radon transform is performed on this matrix. By detecting the coordinates of its energy peak in the parameter space, and using geometric relationship equations, the approximate initial Doppler factor value that best represents the overall Doppler change trend of the target at the initial moment is calculated. The Radon transform is an integral transform, the core of which lies in calculating the line integral of a two-dimensional function (such as an image) along a series of specified straight lines. If there is a clear bright line in the original image, then all points corresponding to the lines crossing this bright line in the parameter space will have high values, thus forming a peak point. Illustratively, the preset Doppler factor parameter data can be set according to actual work requirements.

[0072] S104: Based on a preset number of target headings, navigation data, beam azimuth data, and initial Doppler spectrum approximations, calculate the spectrum corresponding to each target heading, match the spectrum with the candidate line spectrum frequency point set, confirm the line spectrum frequency point of the tracked target, and obtain the status data of the tracked target.

[0073] Specifically, the terminal iterates through a preset number of target headings based on the aforementioned initial Doppler spectrum approximation. For each target heading, the terminal calculates a unique corresponding assumed target speed value based on the Doppler effect equation. Then, for the beam time-domain data at each moment, the terminal compensates for the theoretical Doppler factor calculated from the assumed target heading and target speed value and calculates the spectrum. The compensated spectra at all moments are then coherently accumulated in the frequency domain. The accumulated energy spectrum of each target heading is matched with the aforementioned set of candidate line spectrum frequency points. If, under a certain target heading, the accumulated energy spectrum exhibits significant and stable sharp peaks at several target line spectrum frequency points in the aforementioned set of candidate line spectrum frequency points, then the assumed target speed value is determined to be the true motion state of the tracked target, and the several line spectrum frequency points matching the peaks are the line spectrum frequency points of the tracked target. The initial distance between the observation platform and the tracked target is then calculated based on geometric relationships. Illustratively, the preset number of target headings can be set according to the division of the 0–360° search range in actual work.

[0074] This embodiment provides a target motion element calculation method using sonar beam domain line spectrum matching. Through Doppler matching search and temporal coherent accumulation, it calculates an initial Doppler spectrum approximation and a candidate line spectrum frequency point set. The initial Doppler spectrum approximation is then used to compensate for the Doppler effect and calculate the spectrum of a preset number of target headings. This is then matched with the candidate line spectrum frequency point set to obtain the line spectrum frequency points of the tracked target. The method also calculates the target's state data, including heading, speed, and initial distance. This effectively adapts to complex underwater acoustic environments with strong interference lines and low signal-to-noise ratios. Through intelligent search and verification in the parameter space, it quickly and reliably outputs the target's heading, speed, initial distance, and true line spectrum frequency information simultaneously. This achieves automatic matching of the target's line spectrum frequency without needing accurate confirmation of the target line spectrum frequency, and even under conditions of low signal-to-noise ratio and interference, effectively improving the accuracy of target line spectrum frequency measurement and the reliability of the calculation results.

[0075] In one embodiment, based on preset Doppler factor parameter data, approximate values ​​of the Doppler factors corresponding to each line spectrum frequency point in the candidate line spectrum frequency point set are calculated to obtain an initial Doppler spectrum approximation, including:

[0076] S201, based on each Doppler factor parameter, perform time-domain linear interpolation on the beam time-domain data to obtain the interpolated time-domain data corresponding to each Doppler factor parameter.

[0077] Specifically, the terminal performs time-domain linear interpolation resampling on the aforementioned beam time-domain data using various Doppler factor parameters. The core principle is based on the formula f' s =f s (1+β l ), where f s It is the sampling rate of the beam time-domain data, f' s It is the compensated sampling rate, β l For any given Doppler factor value, the frequency shift effect caused by the relative motion velocity corresponding to the Doppler factor is canceled out by time-domain linear interpolation resampling, resulting in interpolated time-domain data corresponding to each Doppler factor parameter.

[0078] S202 converts the interpolated time-domain data to the frequency domain to obtain the interpolated frequency-domain data.

[0079] Specifically, the terminal converts the interpolated time-domain data corresponding to each Doppler factor parameter obtained above from the time-domain data into frequency-domain data, thus obtaining the interpolated frequency-domain data corresponding to each Doppler factor parameter. Illustratively, a Fast Fourier Transform can be used for frequency-domain conversion; this application does not further limit this frequency-domain conversion method.

[0080] S203, the interpolated frequency domain data corresponding to each Doppler factor parameter are accumulated according to the frequency points of each line spectrum in the candidate line spectrum frequency point set to obtain the interpolated energy spectrum set.

[0081] Specifically, based on the aforementioned candidate line spectrum frequency point set, the terminal accumulates the interpolated frequency domain data corresponding to each Doppler factor parameter according to the line spectrum frequency points included in the candidate line spectrum frequency point set. Specifically, this involves calculating the sum of the energy values ​​of the interpolated frequency domain data corresponding to each Doppler factor parameter across all the aforementioned line spectrum frequency points, obtaining a scalar value of the total energy corresponding to each Doppler factor parameter. Each Doppler factor parameter and its corresponding total energy scalar value form a two-dimensional set, resulting in the interpolated energy spectrum set. Schematic, the vertical axis of the interpolated energy spectrum set represents the Doppler factor parameters, the horizontal axis represents the sampling time series, and the pixel values ​​represent the total energy values ​​under the corresponding conditions.

[0082] S204. Obtain the estimated value of the Doppler factor corresponding to the initial sampling time from the interpolated energy spectrum set, and use it as the initial Doppler spectrum approximation value.

[0083] Specifically, the terminal uses Radon transform to analyze the obtained interpolated energy spectrum set, extracting the most significant linear feature from the set. This means the output peak coordinates correspond to the line with the highest energy concentration in the interpolated energy spectrum set. Then, based on the parameterized equation of this line, the estimated Doppler factor at the initial sampling time is calculated, serving as the initial Doppler spectrum approximation. This initial Doppler spectrum approximation is used to characterize the optimal estimate of the relative radial velocity between the observation platform and the tracked target at the initial moment.

[0084] This embodiment provides a method for using sonar beam domain line spectrum matching to perform Doppler assumption compensation, frequency domain transformation, target line spectrum frequency point energy convergence on the beam time domain data, and finally using Radon transform to extract a unique and optimal initial Doppler spectrum approximation value at the initial moment. This method enables the estimation of a high-quality initial state value of target motion without prior identification of the precise target line spectrum, providing reliable data for subsequent precise matching search of the target's line spectrum frequency points.

[0085] In one embodiment, obtaining the estimated Doppler factor value corresponding to the initial sampling time from the interpolated energy spectrum set includes:

[0086] S301, perform Radon transform on the energy spectrum corresponding to each Doppler factor parameter in the interpolated energy spectrum set to obtain the Radon transform dataset.

[0087] Specifically, the terminal performs a Radon transform on the interpolated energy spectrum set obtained above. The specific operation is to treat the interpolated energy spectrum set as an image with the Doppler factor parameter as the vertical axis, the sampling time series as the horizontal axis, and the pixel value as the total energy value under the corresponding conditions. The projection integral value of the image along the straight line along all possible directions and distances is calculated. By systematically traversing all angle and distance parameters, the interpolated energy spectrum set is transformed from the "time-Doppler factor" coordinate system to the "distance-angle" parameter coordinate system to obtain the Radon transform dataset, where the value of each point represents the sum of the pixel energies in the interpolated energy spectrum set that satisfy the corresponding straight line equation.

[0088] S302, extract the peak coordinates of the Radon transform data corresponding to each Doppler factor parameter in the Radon transform dataset, and calculate the estimated Doppler factor value corresponding to the initial sampling time based on the peak coordinates using the following formula:

[0089] β0·cosθ+t0·sinθ=d

[0090] Where β0 is the estimated value of the Doppler factor at the initial sampling time, θ is the angle data in the peak coordinates, t0 is the initial sampling time, and d is the distance from the origin in the peak coordinates.

[0091] Specifically, the terminal finds the global maximum point from the aforementioned Radon transform dataset to obtain the peak coordinates. Based on the peak coordinates, it uses geometric formulas to calculate the Doppler factor estimate that best matches the overall Doppler variation trend of the target at the initial sampling time. The peak coordinates correspond to the most significant and energetic linear feature trajectory in the original interpolated energy spectrum. Schematic, the Doppler factor estimate β0 at the initial sampling time is used to characterize the best estimate of the relative radial velocity between the observation platform and the tracked target at the initial time. Optionally, the angle data θ in the peak coordinates is used to characterize the tilt angle of the detected optimal linear trend in the interpolated energy spectrum. Schematic, the initial sampling time t0 is the initial sampling time of the aforementioned known beam azimuth data and beam time domain data. Optionally, the distance d from the origin in the peak coordinates is used to characterize the normal distance from the origin to the straight line.

[0092] This embodiment provides a target motion element calculation method using sonar beam domain line spectrum matching. It extracts a unique optimal path from a two-dimensional energy distribution containing target frequency and noise through Radon transform, and transforms the abstract transform peak coordinates into concrete initial moment motion parameter estimates based on physical geometry formulas. This yields the Doppler factor estimate corresponding to the initial sampling moment, enabling the estimation of a high-quality initial state value of target motion without prior identification of the precise target line spectrum. This provides reliable data for subsequent precise matching and searching of the target's line spectrum frequency points.

[0093] In one embodiment, based on a preset number of target headings, navigation data, beam azimuth data, and initial Doppler spectrum approximations, the spectrum corresponding to each target heading is calculated, and the spectrum is matched with a candidate line spectrum frequency point set to confirm the line spectrum frequency points of the tracked target, thereby obtaining the status data of the tracked target, including:

[0094] S401, based on beam azimuth data, navigation data and initial Doppler spectrum approximation, calculates the predicted speed values ​​of each target heading at the initial sampling time, and obtains a set of predicted speed values.

[0095] Specifically, the terminal uses a formula to calculate a preset number of predicted speed values ​​for each target heading based on beam azimuth data, navigation data, and initial Doppler spectrum approximation. Each target heading and its corresponding predicted speed value form a set of predicted speed values.

[0096] S402, combining beam azimuth data, airspeed prediction value set and navigation data, calculates the Doppler factor search value for each target heading, and obtains the Doppler factor search value set.

[0097] Specifically, based on the aforementioned calculated set of predicted airspeed values ​​for each target heading, combined with beam azimuth data and navigation data from the observation platform, the Doppler factor search values ​​for each target heading at a preset time are calculated. These Doppler factor search values ​​for each target heading constitute the Doppler factor search value set. The terminal calculates the Doppler factor search values ​​for each target heading using the following formula, based on the beam azimuth data, the predicted airspeed value set, and the navigation data: β ir V is the Doppler factor search value of the target heading r at the preset time i. r H is the predicted speed value corresponding to the r-th target heading. r It is the r-th target heading, F i It is the beam azimuth data at the preset time i. It is the speed of the observation platform. This refers to the heading of the observation platform. Indicatively, the preset time can be set based on the actual tracking target and the acquisition of beam time-domain data.

[0098] S403, based on the Doppler factor search value set, performs time-domain linear interpolation and time-domain coherent accumulation on the beam time-domain data of each target heading to obtain the heading prediction spectrum corresponding to each target heading.

[0099] Specifically, the terminal performs precise compensation on the aforementioned beam time-domain data based on the Doppler factor search value set. For each target heading and its corresponding predicted speed, the beam time-domain data is resampled using the corresponding Doppler factor search value at each time step to cancel the Doppler effect under the assumption of the target heading. Then, the compensated signal spectrum at all times is coherently accumulated in the time domain to obtain the heading prediction spectrum corresponding to each target heading.

[0100] S404, based on the predicted spectrum of the heading and the candidate line spectrum frequency point set corresponding to each target heading, determines the real-time heading, real-time speed and monitoring line spectrum frequency point set of the tracked target.

[0101] Specifically, the terminal performs matching analysis between the predicted spectral frequencies corresponding to each target heading and the aforementioned candidate line spectrum frequency point set. By traversing all target headings and all line spectrum frequency points in the candidate line spectrum frequency point set, it searches for the heading prediction spectrum that causes a number of line spectrum frequency points in the candidate line spectrum frequency point set to exhibit significant, stable, and continuously increasing maxima. The target heading corresponding to this heading prediction spectrum is determined as the real-time heading, the predicted speed value corresponding to this target heading is determined as the real-time speed, and the aforementioned number of line spectrum frequency points exhibiting significant, stable, and continuously increasing maxima are determined as the line spectrum frequencies generated by the radiation from the tracking target, thus forming the monitoring line spectrum frequency point set.

[0102] S405 calculates the initial position of the tracked target based on real-time heading, real-time speed, beam azimuth data, and navigation data, thus obtaining the initial distance between the tracked target and the observation platform.

[0103] Specifically, the terminal, based on the real-time heading and speed of the target being tracked, and in conjunction with beam azimuth data and navigation data, solves the straight-line distance between the target and the observation platform at the initial moment by establishing a geometric trigonometric relationship equation, thus obtaining the initial distance between the target and the observation platform.

[0104] S406 obtains status data based on real-time heading, real-time speed, initial distance, and a set of monitored line spectrum frequency points.

[0105] Specifically, the terminal integrates the real-time heading, real-time speed, initial distance, and monitoring spectrum frequency point set of the tracked target obtained above to obtain status data.

[0106] This embodiment provides a target motion element calculation method using sonar beam domain line spectrum matching. It derives the target heading-speed coupling relationship from initial Doppler estimates, calculates the Doppler factor for each target heading, performs multi-target heading Doppler compensation and coherent accumulation, and finally selects the correct heading and true line spectrum frequency of the tracking target from each target heading simultaneously through spectral peak detection. Based on this, it calculates the initial distance to form the target's state data. This method achieves automatic matching of the target line spectrum frequency of the tracking target without needing accurate confirmation of the target line spectrum frequency, and under conditions of low signal-to-noise ratio and interference, effectively improving the accuracy of target line spectrum frequency measurement and the reliability of the calculation results.

[0107] In one embodiment, based on beam azimuth data, navigation data, and initial Doppler spectrum approximations, the predicted speed values ​​for each target heading at the initial sampling time are calculated to obtain a set of predicted speed values, including:

[0108] S501, for each target heading, the predicted speed value corresponding to the target heading is calculated using the following formula based on beam azimuth data, navigation data, and initial Doppler spectrum approximation:

[0109]

[0110] Among them, V r It is the predicted speed value for the r-th target heading. This is an approximation of the initial Doppler spectrum, where c is the speed of sound in water. It is the speed of the observation platform in the navigation data. F0 is the observation platform's heading in the navigation data, and F0 is the beam azimuth data at the initial moment.

[0111] Specifically, the terminal calculates the predicted speed for each target heading using a formula, based on beam azimuth data, navigation data, and an initial Doppler spectrum approximation. Illustratively, the predicted speed V for the r-th target heading is... r The Doppler observations used to characterize the initial moment are used to constrain the target velocity vector. Optionally, the initial Doppler spectrum approximation... This is the estimated Doppler factor value corresponding to the initial sampling time obtained from the aforementioned calculation. Indicatively, the sound speed value *c* in water can be set according to the actual propagation speed of sound waves in water during operation. Optionally, the observation platform's moving speed in the navigation data... The heading of the observation platform in the navigation data is used to characterize the platform's movement speed. Used to characterize the direction angle of movement of the observation platform. Schematic, the beam azimuth data F0 is the data of the aforementioned beam azimuth data at the initial moment.

[0112] S502, based on the predicted speed values ​​corresponding to each target heading, obtains a set of predicted speed values.

[0113] Specifically, the terminal integrates the predicted airspeed values ​​corresponding to each target heading to obtain a set of predicted airspeed values.

[0114] This embodiment provides a target motion element calculation method using sonar beam domain line spectrum matching. The target heading-speed coupling relationship is derived from the initial Doppler estimate, providing reliable input data for the subsequent calculation of the Doppler factor of each target heading.

[0115] In one embodiment, based on the predicted course spectrum and candidate line spectrum frequency point set corresponding to each target course, the real-time course, real-time speed, and monitoring line spectrum frequency point set of the tracked target are determined, including:

[0116] S601, match each line spectrum frequency point in the candidate line spectrum frequency point set with the heading prediction spectrum corresponding to each target heading, select the frequency intensity of each line spectrum frequency point in the heading prediction spectrum corresponding to each target heading, and obtain the frequency energy intensity data of the line spectrum frequency point.

[0117] Specifically, the terminal matches each spectral frequency point in the candidate spectral frequency point set with the predicted spectral spectrum corresponding to each target heading. For each spectral frequency point in the candidate spectral frequency point set, it extracts the spectral intensity value at that spectral frequency point from the predicted spectrum of each target heading, forming a sequence containing a number of intensity values, thus obtaining the frequency energy intensity data for each spectral frequency point. The number of intensity values ​​in the frequency energy intensity data corresponds to the number of target headings.

[0118] S602, from the frequency energy intensity data corresponding to each line spectrum frequency point, select the line spectrum frequency points where the frequency intensity is the extreme value of the corresponding heading prediction spectrum to obtain the monitoring line spectrum frequency point set.

[0119] Specifically, the terminal performs peak analysis on the frequency energy intensity data corresponding to each line spectrum frequency point. For each line spectrum frequency point in the candidate line spectrum frequency point set, an algorithm for finding the maximum value is used to determine whether the energy of that line spectrum frequency point exhibits an abnormally significant peak under a certain target flight direction. Line spectrum frequency points that can show a clear and stable extreme value response under a certain target flight direction are selected and determined to be line spectrum frequencies generated by the radiation of the tracked target, forming a monitoring line spectrum frequency point set. The monitoring line spectrum frequency point set is used to represent all frequency points of line spectrum frequencies generated by the radiation of the tracked target.

[0120] S603, based on the target heading predicted spectrum corresponding to the extreme value of frequency intensity, obtains the real-time heading of the tracked target.

[0121] Specifically, the terminal selects the target heading corresponding to the heading prediction spectrum where the energy at each of the aforementioned monitored line spectrum frequency points shows an abnormally significant peak, and determines this as the real-time heading of the tracked target. The physical mechanism for this determination is that Doppler compensation is only accurate when the assumed target heading perfectly matches the real-time heading of the tracked target. Only then can the target line spectrum energy coherently superimpose in the frequency domain to form an extremum. Therefore, the heading assumption corresponding to the occurrence of this extremum is the real-time heading of the tracked target.

[0122] S604 obtains real-time speed based on real-time heading, navigation data, and beam azimuth data.

[0123] Specifically, the terminal uses the Doppler effect formula to inversely calculate the target's speed based on real-time heading, navigation data, and beam azimuth data, thus obtaining the real-time speed. The Doppler effect formula is the one shown in S501 above.

[0124] This embodiment provides a target motion element calculation method using sonar beam domain line spectrum matching. By matching the target line spectrum frequency points with the predicted spectrum of each target heading, and based on the principle that "the true line spectrum energy exhibits an extreme value under correct motion parameters", the set of line spectrum frequency points of the tracked target is extracted, and the real-time heading and real-time speed of the tracked target are obtained. This achieves accurate identification of the true target line spectrum and motion heading, and effectively automatically matches the target line spectrum frequency of the tracked target without the need for accurate confirmation of the target line spectrum frequency and under conditions of low signal-to-noise ratio and interference, thereby improving the accuracy of target line spectrum frequency measurement and the reliability of the calculation results.

[0125] In one embodiment, time-domain accumulation and line spectrum frequency point extraction are performed on the beam time-domain data to obtain a candidate line spectrum frequency point set, including:

[0126] S701 performs time-series accumulation of beam time-domain data according to preset time window parameters to obtain accumulated beam time-domain data.

[0127] Specifically, the terminal, based on a preset time window parameter, splices and accumulates the previously obtained beam time domain data in chronological order, stitching together the time point data corresponding to the time window parameter to generate a longer, continuous accumulated beam time domain data. Illustratively, the time window parameter is equal to a non-zero integer multiple of the sampling time interval of the beam azimuth data, and can be set according to actual work requirements.

[0128] S702 performs a short-time Fourier transform on the time-domain data of the accumulated beam to obtain the frequency-domain accumulated beam data.

[0129] Specifically, the terminal uses the short-time Fourier transform to convert the cumulative beam time-domain data from the time domain to the frequency domain, obtaining the frequency-domain cumulative beam data. Among them, the short-time Fourier transform is a classic time-frequency analysis algorithm. By using a sliding window function to intercept short-time data in a long signal and performing Fourier transform on each short-time data segment separately, a three-dimensional spectrum (spectrum diagram) showing the change of the spectrum over time is obtained.

[0130] S703. Select line spectral frequency points that meet the preset line spectral frequency requirements from the frequency-domain cumulative beam data to obtain a set of candidate line spectral frequency points.

[0131] Specifically, select line spectral frequency points that meet the preset line spectral frequency requirements from the frequency-domain cumulative beam data to form a set of candidate line spectral frequency points. Schematically, the line spectral frequency requirements may include energy threshold limits, spectral line width limits, and stability requirements, which can be set according to actual work.

[0132] The method for solving the target motion elements using sonar beam domain line spectrum matching provided in this embodiment improves the signal-to-noise ratio through sequential accumulation, uses time-frequency transformation and filters out significant spectral peaks according to preset rules to obtain a set of candidate line spectral frequency points, providing a comprehensive and reliable set of candidate characteristic frequencies for subsequent tracking of the target state settlement, effectively improving the accuracy of target line spectral frequency measurement and the reliability of the settlement results.

[0133] To further illustrate the solution of the application embodiment in this embodiment, a specific example is given below for illustration:

[0134] This application provides a method for solving target motion elements using beam domain line spectra. Refer to Figure 2 , including the following steps:

[0135] S01. Obtain navigation and acoustic basic data.

[0136] Specifically, refer to Figure 3 , simplify the target water area into a two-dimensional coordinate system; at time t i , through the navigation equipment carried by the observation platform, obtain the heading speed (moving data) of the observation platform's movement; through the sonar equipment carried by the observation platform, obtain the beam azimuth sampling value F <o:p>< / o:p> i (beam azimuth data) of the tracked noise target, and extract a segment of acoustic time-domain signal (beam time-domain data) within the corresponding beam, denoted as x i (τ), 0 ≤ τ < T, i = 1, 2,... N, where i is the serial number of the sampling moment; T0 is the time length of the extracted beam data segment, and its value is equal to the time interval of azimuth sampling; τ is the time sampling point of the beam data.

[0137] S02, rough estimation of line spectrum frequency.

[0138] Specifically, this includes: from the initial time t0 to time tT, setting the observation platform to travel at a constant straight speed. The value is fixed. The data x from each beam is extracted sequentially. i (τ) is accumulated according to the sampling time sequence to form a continuous beam domain acoustic signal s0(t) of duration T (accumulated beam time domain data), where T is a non-zero integer multiple of T0. Short-time Fourier analysis is performed on s0(t) to roughly estimate k possible frequency points containing interference in the beam domain.

[0139] S03, Fast estimation of Doppler factors.

[0140] Specifically, this includes: reference Figure 4 Let t be the time of the i-th sonar orientation. i The following two steps are used to perform a fast approximate estimation of the Doppler factor:

[0141] Step 1: Within a certain range, divide the data into l possible Doppler factors β. l For each of the above beam domain data segments, different β values ​​are applied. l Perform time-domain linear interpolation to obtain a total of i×l interpolated compensated signals y. il (τ) (interpolated time-domain data) and its spectrum Y il (f k (Interpolated frequency domain data), the interpolation sampling rate is determined according to equation (1):

[0142] f' s =f s ·(1+β l (1)

[0143] Among them, f s f' is the sampling rate of the original beam domain data. s f is the interpolated sampling rate; k This represents the k-th frequency point of the signal spectrum after interpolation compensation.

[0144] Step 2: Calculate the interpolated spectrum Y... il (f k According to frequency point f k The addition is performed sequentially according to equation (2).

[0145] Y il =∑ k Y il (f k (2)

[0146] Step 3: Analyze the Y obtained above... il The Radon transform is performed on the interpolated energy spectrum set, and the result is denoted as R.y Searching for R y The peak coordinates (d, θ), according to the peak coordinates and t i Doppler factor β at time t i The linear relationship exists as shown in equation (3):

[0147] β i ·cosθ+t i sinθ=d (3)

[0148] Estimate the Doppler factor of the target An approximation of, denoted as This is the Doppler estimate at the initial time t0 (the initial Doppler spectrum approximation).

[0149] S04, Doppler factor matching search.

[0150] Specifically, this includes: reference Figure 5 The target heading is divided into R discrete values ​​(a preset number of target headings) according to the search range of 0 to 360°, and the Doppler factor is accurately estimated using the following two steps:

[0151] Step 1: Doppler estimation based on initial time t0 Based on the search value H for each target heading r The search value V corresponding to the target speed is obtained according to equation (4). r (Predicted speed):

[0152]

[0153] Where F0 is the target beam azimuth value at the initial time t0; and c is the sound speed in water.

[0154] Step 2: At any time t i (t i >t0), combined with the target beam azimuth value F i Based on the R discrete values ​​divided by the target heading, i×R target Doppler factor search values ​​(Doppler factor search values) are calculated according to equation (5):

[0155]

[0156] Step 3: For each of the above target beam azimuth values ​​F i The corresponding beam domain signal x i (τ) according to the above β ir Interpolation is performed to obtain the corresponding interpolated compensated signal y. ir (τ) and its spectrum Y ir (f k ), t iAll spectrum Y before time step ir (f k Perform time-domain coherent accumulation (heading prediction frequency) according to equation (6):

[0157] Y r (f k )=∑ i Y ir (f k ) i=1,2,…N; r=1,2,…R (6)

[0158] If in f k =f0, f1, ..., f p-1 (p is a non-zero integer), when r = m, Y r (f k All of them have a continuously and steadily increasing maximum value, representing t i At time 10, the target's Doppler factor was accurately matched, and the target's p line spectrum frequencies were also matched and confirmed. At this point, the target's heading H = H r|r＝m Furthermore, according to equation (4), the calculated value of the target velocity V = V r|r＝m (Real-time speed)

[0159] Otherwise, continue from step two above to the next time step t. i+1 The search matching, the continuous increase of the time-domain coherence accumulation time, until it reaches a certain spectral frequency f k Y r (f k It has a continuously and steadily increasing maximum value. The coherent cumulative matching results can be found by referring to... Figure 6 The speed estimation results can be referenced. Figure 7 The heading estimation results can be referred to Figure 8 .

[0160] S05, Target initial distance calculation.

[0161] Specifically, this includes: reference Figure 9 When the target Doppler factor is accurately matched, based on the target heading H and speed V determined in the previous step, the initial range vector D = [D1 D2 … D i The initial distance vector D of N×1 dimension is obtained by solving equation (7):

[0162] D=Y\A (7)

[0163] Where, Y = [sin F1 sin F2 … sin F i cos F1 cos F2 … cos F i ] T ,

[0164] i = 1, 2, ..., N, Δt i =t i -t i-1 The backslash "\" performs a division operation on the corresponding elements of a matrix.

[0165] Sum the squares of all elements of the target initial distance vector D obtained above, and then take the square root to obtain the calculated target initial distance value D0 (initial distance):

[0166]

[0167] In the aforementioned method for calculating target motion elements using sonar beam domain line spectrum matching, navigation data from the observation platform, beam azimuth data of the tracked target, and beam time-domain data of the tracked target are acquired. The initial sampling times of the beam azimuth data and beam time-domain data are the same. Time-domain accumulation and line spectrum frequency point extraction are performed on the beam time-domain data to obtain a candidate line spectrum frequency point set. The candidate line spectrum frequency point set includes the line spectrum frequency points of the tracked target and interference line spectrum frequency points. Based on preset Doppler factor parameter data, the approximate values ​​of the Doppler factors corresponding to each line spectrum frequency point in the candidate line spectrum frequency point set are calculated to obtain the initial Doppler spectrum approximate values. According to a preset number of target headings, navigation data, beam azimuth data, and initial Doppler spectrum approximate values, the spectrum corresponding to each target heading is calculated, and the spectrum is matched with the candidate line spectrum frequency point set to confirm the line spectrum frequency points of the tracked target, thus obtaining the state data of the tracked target. This method achieves accurate capture of the target line spectrum frequency under low signal-to-noise ratio and interference conditions, effectively improving the accuracy of target line spectrum frequency measurement and the reliability of the calculation results.

[0168] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0169] Based on the same inventive concept, this application also provides a target motion element calculation device using sonar beam domain line spectrum matching to implement the aforementioned target motion element calculation method using sonar beam domain line spectrum matching. The solution provided by this device is similar to the implementation described in the above method. Therefore, the specific limitations of one or more embodiments of a target motion element calculation device using sonar beam domain line spectrum matching provided below can be found in the above-described limitations of the target motion element calculation method using sonar beam domain line spectrum matching, and will not be repeated here.

[0170] In one exemplary embodiment, such as Figure 10 As shown, a target motion element calculation device 200 using sonar beam domain line spectrum matching is provided, comprising:

[0171] The data acquisition module 201 is used to acquire navigation data of the observation platform, beam azimuth data of the tracked target, and beam time domain data of the tracked target; the initial sampling time of the beam azimuth data and the beam time domain data is the same;

[0172] The spectrum calculation module 202 is used to perform time-domain accumulation and line spectrum frequency point extraction on the beam time-domain data to obtain a candidate line spectrum frequency point set; the candidate line spectrum frequency point set includes the line spectrum frequency points of the tracking target and the interference line spectrum frequency points;

[0173] The Doppler value calculation module 203 is used to calculate the approximate value of the Doppler factor corresponding to each line spectrum frequency point in the candidate line spectrum frequency point set based on the preset Doppler factor parameter data, so as to obtain the initial Doppler spectrum approximate value.

[0174] The target state calculation module 204 is used to calculate the spectrum corresponding to each target's heading based on a preset number of target headings, navigation data, beam azimuth data, and initial Doppler spectrum approximations, and to match the spectrum with a set of candidate line spectrum frequency points to confirm the line spectrum frequency points of the tracked target and obtain the target's state data.

[0175] Furthermore, the Doppler value calculation module includes:

[0176] The Doppler time-domain interpolation unit is used to perform time-domain linear interpolation on the beam time-domain data according to each Doppler factor parameter, so as to obtain the interpolated time-domain data corresponding to each Doppler factor parameter.

[0177] The Doppler time-frequency conversion unit is used to convert the interpolated time-domain data into frequency-domain data to obtain the interpolated frequency-domain data.

[0178] The interpolation energy spectrum calculation unit is used to accumulate the interpolation frequency domain data corresponding to each Doppler factor parameter according to the frequency points of each line spectrum in the candidate line spectrum frequency point set to obtain the interpolation energy spectrum set.

[0179] The initial Doppler approximation calculation unit is used to obtain the estimated Doppler factor value corresponding to the initial sampling time from the interpolated energy spectrum set, as the initial Doppler spectrum approximation value.

[0180] Furthermore, the initial Doppler approximation calculation unit is also used for:

[0181] The Radon transform is performed on the energy spectrum corresponding to each Doppler factor parameter in the interpolated energy spectrum set to obtain the Radon transform dataset.

[0182] Extract the peak coordinates of the Radon transform data corresponding to each Doppler factor parameter in the Radon transform dataset, and calculate the estimated Doppler factor value corresponding to the initial sampling time based on the peak coordinates using the following formula:

[0183] β0·cosθ+t0·sinθ=d

[0184] Where β0 is the estimated value of the Doppler factor at the initial sampling time, θ is the angle data in the peak coordinates, t0 is the initial sampling time, and d is the distance from the origin in the peak coordinates.

[0185] Furthermore, the target state calculation module includes:

[0186] The speed prediction unit is used to calculate the predicted speed of each target heading at the initial sampling time based on beam azimuth data, navigation data and initial Doppler spectrum approximation, and obtain a set of predicted speed values;

[0187] The Doppler factor calculation unit is used to combine beam azimuth data, airspeed prediction value set and navigation data to calculate the Doppler factor search value of each target heading and obtain the Doppler factor search value set.

[0188] The heading spectrum calculation unit is used to perform time-domain linear interpolation and time-domain coherent accumulation on the beam time-domain data of each target heading based on the Doppler factor search value set, so as to obtain the heading prediction spectrum corresponding to each target heading.

[0189] The target data determination unit is used to determine the real-time heading, real-time speed, and monitoring line spectrum frequency point set of the tracked target based on the heading prediction spectrum and candidate line spectrum frequency point set corresponding to each target heading.

[0190] The target distance determination unit is used to solve for the initial position of the tracked target based on real-time heading, real-time speed, beam azimuth data and navigation data, and to obtain the initial distance between the tracked target and the observation platform.

[0191] The target state determination unit is used to obtain state data based on real-time heading, real-time speed, initial distance, and a set of monitoring line spectrum frequency points.

[0192] Furthermore, the speed prediction unit is also used for:

[0193] For each target heading, the predicted speed is calculated using the following formula, based on beam azimuth data, navigation data, and initial Doppler spectrum approximations:

[0194]

[0195] Among them, V r It is the predicted speed value for the r-th target heading. This is an approximation of the initial Doppler spectrum, where c is the speed of sound in water. It is the speed of the observation platform in the navigation data. F0 is the observation platform's heading in the navigation data, and F0 is the beam azimuth data at the initial moment;

[0196] Based on the predicted speed values ​​corresponding to each target heading, a set of predicted speed values ​​is obtained.

[0197] Furthermore, the target data determination unit is also used for:

[0198] Match each line spectrum frequency point in the candidate line spectrum frequency point set with the heading prediction spectrum corresponding to each target heading, select the frequency intensity of each line spectrum frequency point in the heading prediction spectrum corresponding to each target heading, and obtain the frequency energy intensity data of the line spectrum frequency point.

[0199] From the frequency energy intensity data corresponding to each line spectrum frequency point, select the line spectrum frequency points where the frequency intensity is the extreme value in the corresponding heading prediction spectrum to obtain the monitoring line spectrum frequency point set;

[0200] The target heading is obtained by predicting the heading spectrum based on the frequency intensity extreme value.

[0201] Real-time speed is obtained based on real-time heading, navigation data, and beam azimuth data.

[0202] Furthermore, the spectrum calculation module is also used for:

[0203] Based on the preset time window parameters, the beam time domain data is accumulated in time sequence to obtain the accumulated beam time domain data.

[0204] Short-time Fourier transform is performed on the time-domain data of the accumulated beam to obtain the frequency-domain accumulated beam data;

[0205] Select line spectrum frequency points that meet the preset line spectrum frequency requirements from the frequency domain cumulative beam data to obtain a candidate line spectrum frequency point set.

[0206] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of a target motion element solution method using sonar beam domain line spectrum matching as described above.

[0207] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0208] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0209] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. A method for calculating target motion elements using sonar beam domain line spectrum matching, characterized in that, The method includes: The system acquires navigation data from the observation platform, beam azimuth data of the tracked target, and beam time-domain data of the tracked target; the initial sampling times of the beam azimuth data and the beam time-domain data are the same. The time-domain data of the beam is accumulated in the time domain and the line spectrum frequency points are extracted to obtain a candidate line spectrum frequency point set; the candidate line spectrum frequency point set includes the line spectrum frequency points of the tracked target and the interference line spectrum frequency points; Based on the preset Doppler factor parameter data, the approximate value of the Doppler factor corresponding to each of the line spectrum frequency points in the candidate line spectrum frequency point set is calculated to obtain the initial Doppler spectrum approximate value. Based on a preset number of target headings, the navigation data, the beam azimuth data, and the initial Doppler spectrum approximation, the spectrum corresponding to each target heading is calculated, and the spectrum is matched with the candidate line spectrum frequency point set to confirm the line spectrum frequency point of the tracked target, thereby obtaining the status data of the tracked target.

2. The method according to claim 1, characterized in that, The process involves calculating approximate values ​​of the Doppler factors corresponding to each line spectrum frequency point in the candidate line spectrum frequency point set based on preset Doppler factor parameter data, thereby obtaining initial Doppler spectrum approximations, including: Based on each of the Doppler factor parameters, the time-domain data of the beam is linearly interpolated in the time domain to obtain the interpolated time-domain data corresponding to each of the Doppler factor parameters. The interpolated time-domain data is converted to the frequency domain to obtain interpolated frequency-domain data; The interpolated frequency domain data corresponding to each of the Doppler factor parameters are accumulated according to each of the line spectrum frequency points in the candidate line spectrum frequency point set to obtain the interpolated energy spectrum set. The estimated value of the Doppler factor corresponding to the initial sampling time is obtained from the interpolated energy spectrum set and used as the approximation value of the initial Doppler spectrum.

3. The method according to claim 2, characterized in that, The step of obtaining the Doppler factor estimate corresponding to the initial sampling time from the interpolated energy spectrum set includes: A Radon transform is performed on the energy spectrum corresponding to each Doppler factor parameter in the interpolated energy spectrum set to obtain a Radon transform dataset. Extract the peak coordinates of the Radon transform data corresponding to each Doppler factor parameter in the Radon transform dataset, and calculate the estimated Doppler factor value corresponding to the initial sampling time based on the peak coordinates using the following formula: β0·cosθ+t0·sinθ=d Where β0 is the estimated value of the Doppler factor at the initial sampling time, θ is the angle data in the peak coordinates, t0 is the initial sampling time, and d is the distance from the origin in the peak coordinates.

4. The method according to claim 1, characterized in that, The step involves calculating the spectrum corresponding to each target heading based on a preset number of target headings, the navigation data, the beam azimuth data, and the initial Doppler spectrum approximation, and matching the spectrum with the candidate line spectrum frequency point set to confirm the line spectrum frequency point of the tracked target, thereby obtaining the status data of the tracked target, including: Based on the beam azimuth data, the navigation data, and the initial Doppler spectrum approximation, the predicted speed values ​​for each target heading at the initial sampling time are calculated to obtain a set of predicted speed values. By combining the beam azimuth data, the airspeed prediction value set, and the navigation data, the Doppler factor search value for each target heading is calculated to obtain the Doppler factor search value set. Based on the Doppler factor search value set, the time-domain data of the beams for each target heading are subjected to time-domain linear interpolation and time-domain coherent accumulation to obtain the heading prediction spectrum corresponding to each target heading; Based on the predicted course spectrum corresponding to each target course and the candidate line spectrum frequency point set, the real-time course, real-time speed and monitoring line spectrum frequency point set of the tracked target are determined; Based on the real-time heading, real-time speed, beam azimuth data, and navigation data, the initial position of the tracked target is calculated, and the initial distance between the tracked target and the observation platform is obtained. The status data is obtained based on the real-time heading, the real-time speed, the initial distance, and the monitoring line spectrum frequency point set.

5. The method according to claim 4, characterized in that, Based on the beam azimuth data, the navigation data, and the initial Doppler spectrum approximation, the predicted speed values ​​for each target heading at the initial sampling time are calculated to obtain a set of predicted speed values, including: For each target heading, the predicted speed value corresponding to the target heading is calculated using the following formula, based on the beam azimuth data, the navigation data, and the initial Doppler spectrum approximation: Among them, V r It is the predicted speed value for the r-th target heading. This is an approximation of the initial Doppler spectrum, where c is the speed of sound in water. It is the speed of the observation platform in the navigation data. F0 is the observation platform's heading in the navigation data, and F0 is the beam azimuth data at the initial moment; The set of predicted speed values ​​is obtained based on the predicted speed values ​​corresponding to each of the target headings.

6. The method according to claim 4, characterized in that, The step of determining the real-time heading, real-time speed, and monitoring frequency point set of the tracked target based on the heading prediction spectrum corresponding to each of the target headings and the candidate line spectrum frequency point set includes: The frequency intensity of each line spectrum frequency point in the candidate line spectrum frequency point set is matched with the heading prediction spectrum corresponding to each target heading, and the frequency intensity of each line spectrum frequency point in the heading prediction spectrum corresponding to each target heading is selected to obtain the frequency energy intensity data of the line spectrum frequency point. From the frequency energy intensity data corresponding to each of the line spectrum frequency points, select the line spectrum frequency points where the frequency intensity is at an extreme value in the corresponding heading prediction spectrum to obtain the monitoring line spectrum frequency point set; The real-time heading of the tracked target is obtained based on the target heading predicted spectrum corresponding to the extreme value of the frequency intensity; The real-time speed is obtained based on the real-time heading, the navigation data, and the beam azimuth data.

7. The method according to claim 1, characterized in that, The step of performing time-domain accumulation and line spectrum frequency point extraction on the beam time-domain data to obtain a candidate line spectrum frequency point set includes: According to the preset time window parameters, the beam time domain data is accumulated in time sequence to obtain the accumulated beam time domain data. Perform a short-time Fourier transform on the accumulated beam time-domain data to obtain frequency-domain accumulated beam data; The candidate line spectrum frequency point set is obtained by selecting line spectrum frequency points that meet the preset line spectrum frequency requirements from the frequency domain cumulative beam data.

8. A target motion element calculation device utilizing sonar beam domain line spectrum matching, characterized in that, The device includes: The data acquisition module is used to acquire navigation data of the observation platform, beam azimuth data of the tracked target, and beam time domain data of the tracked target; the initial sampling time of the beam azimuth data and the beam time domain data is the same; The spectrum calculation module is used to perform time-domain accumulation and line spectrum frequency point extraction on the beam time-domain data to obtain a candidate line spectrum frequency point set; the candidate line spectrum frequency point set includes the line spectrum frequency points of the tracked target and the interference line spectrum frequency points; The Doppler value calculation module is used to calculate the approximate value of the Doppler factor corresponding to each of the line spectrum frequency points in the candidate line spectrum frequency point set based on the preset Doppler factor parameter data, so as to obtain the initial Doppler spectrum approximate value. The target state calculation module is used to calculate the spectrum corresponding to each target heading based on a preset number of target headings, the navigation data, the beam azimuth data, and the initial Doppler spectrum approximation, and to match the spectrum with the candidate line spectrum frequency point set to confirm the line spectrum frequency point of the tracked target, thereby obtaining the state data of the tracked target.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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