Method for determining sea surface target track initiation parameters based on multi-band energy feature fusion
By using a multi-band energy feature fusion method, Hough parameter space partitioning and multi-scale clustering are employed to eliminate low-quality data, achieving rapid accuracy and anti-false alarm capability for the initiation of sea surface target tracks. This solves the problems of low track initiation accuracy and slow convergence speed in existing technologies.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- LEIHUA ELECTRONICS TECH RES INST AVIATION IND OF CHINA
- Filing Date
- 2022-10-19
- Publication Date
- 2026-07-21
Smart Images
Figure CN115905769B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of radar signal processing technology, specifically to a method for determining the initial parameters of a sea surface target trajectory by multi-band energy feature fusion. Background Technology
[0002] Utilizing airborne radar sensors to detect ships and other sea surface targets in complex environments is a crucial supporting technology for enhancing maritime reconnaissance, surveillance, search, and early warning capabilities, acquiring maritime situational information, and establishing a competitive advantage. Based on their different functions, maritime airborne radars can be categorized into early warning radars, search radars, and fire control radars: Early warning radars have the longest detection range but lower accuracy, and their sea clutter models generally use a Rayleigh distribution; search radars offer a compromise in detection range compared to early warning and fire control radars, and their sea clutter models generally use a Rayleigh or K-distribution; fire control radars have a shorter detection range than the former two but higher accuracy, and their sea clutter models generally use a K-distribution.
[0003] In the process of airborne radar detection of the sea, the sea surface target tracking technology has always been a hot topic of research for scientific researchers and engineers. Target tracking technology mainly includes three stages: track initiation, track maintenance, and track termination. As the primary stage of target tracking, the performance of track initiation directly affects the execution efficiency of sea radar detection missions. Therefore, track initiation has always been one of the hot topics in sea surface target tracking technology research.
[0004] Traditional methods for initiating tracking paths in sea surface target tracking technologies have the following main problems:
[0005] 1) The coordinated initiation of radar sensors in different wavebands for sea observation was not considered;
[0006] 2) The impact of data quality differences caused by energy characteristics measured in different bands on track initiation performance was not comprehensively considered;
[0007] 3) Low initial accuracy leads to slow track convergence. Summary of the Invention
[0008] In view of this, the present invention provides a method for determining the starting parameters of a sea surface target track by multi-band energy feature fusion. The method uses the energy likelihood ratio feature of dual-band measurements to eliminate measurements with relatively low data quality, thereby ensuring that the measurements used for track initiation have relatively good data quality, high initiation accuracy, fast convergence speed, strong anti-false alarm capability, and improving the reliability of track initiation parameters.
[0009] A method for determining the initial parameters of a sea surface target track by multi-band energy feature fusion includes the following steps:
[0010] Step 1: Convert the radar echo information into Hough space and divide the corresponding Hough parameter space into cell spaces;
[0011] Step 2: Based on the energy likelihood ratio of candidate measurements in radar echo information, perform knowledge feature-assisted Hough parameter space accumulation for different bands to obtain the cell space accumulation value.
[0012] Step 3: Perform low-threshold initial track selection based on the accumulated value of the cell space, and correct the initial track parameters based on the energy characteristics of different bands.
[0013] Step 4: Perform iterative filtering correction on the modified initial trajectory parameters based on multi-scale clustering;
[0014] Step 5: Optimize the track parameters after iterative filtering correction to determine the optimal number of target track start points and track start parameters.
[0015] Furthermore, the Hough parameter space partitioning method in step 1 is as follows: Radar echo information from different bands is converted to Hough space, and the corresponding Hough parameter space is partitioned into segments of size [missing information]. In the cell space, the sub-cell in the k-th row and m-th column can be represented as ,
[0016]
[0017]
[0018] It is a constant; and They are the unit lengths The k-th row and m-th sub-unit Parameter center coordinates and Parameter center coordinates.
[0019] Furthermore, the method for accumulating Hough parameter space assisted by knowledge features of different bands in step 2 includes the following steps:
[0020] Regarding the first Calculate the energy likelihood ratio of the corresponding band based on the energy information of each measurement. :
[0021]
[0022] In the formula, The target's measured position coordinates in the radar's NED. For the first Each measurement uses energy information. The energy probability density function of the effective measurements generated for target t follows a Rayleigh distribution. Signal-to-noise ratio; The energy probability density function of false alarm measurements with only noise follows a Rayleigh distribution; For the corresponding detection threshold, and satisfying , This represents the probability of a false alarm.
[0023] By voting on the parameter space units using the Hough transform, we obtain... The voting results of the unit;
[0024] Traverse the radar voting results, accumulate the voting results for all sub-cells in the divided parameter space, and obtain the accumulated value of the cell space.
[0025] Furthermore, the voting accumulation rule in step 2 is: if the sub-unit center error And the corresponding radar-measured energy likelihood is higher than or The corresponding accumulated value Add 1, otherwise don't add. Empirical values for the energy likelihood ratio threshold to be measured in the P-band. Empirical values for the energy likelihood ratio threshold for X-band measurements.
[0026] Furthermore, the initial selection method for low-threshold tracks in step 3 is as follows: set a low threshold T, and select tracks based on the accumulated voting results. An election was conducted to obtain a preliminary set of trajectory parameters. The election conditions are as follows: if If the election is successful, it is recorded as . Otherwise, the election fails and is recorded as 0; among which, Starting window length Detection probability , It is an election factor.
[0027] Furthermore, the method for correcting the initial trajectory parameters in step 3 based on the energy characteristics of different wavebands is as follows:
[0028] For the initial set of trajectory parameters of The parameters are initially corrected, and the correction value is...
[0029]
[0030] In the formula, This is an indicator function, taking the value 0 or 1. =1 indicates that the measurement originated from the P-band; otherwise, it originated from the X-band.
[0031] get Parameter correspondence The correction value is
[0032]
[0033] Then for the initial set of trajectory parameters The initial correction value is .
[0034] Furthermore, the trajectory parameter iterative filtering correction method based on multi-scale clustering in step 4 includes the following steps:
[0035] The distribution function of the revised initial track parameter set is:
[0036]
[0037] in, Let be the initial parameters for the j-th trajectory, and ; n is the number of initially selected paths; according to scale space theory, The multi-scale clustering representation is as follows:
[0038]
[0039] Given the iteration tolerance error For each given scale For each initial selection parameter of the trajectory , , For a single correction parameter set The number of;
[0040] make = ,
[0041] in As the initial value for iteration, perform iterative calculations according to equation (12) until... The iterative calculation ends, and the initial trajectory selection parameters are obtained. The convergence value of the iterative filter is denoted as . ;
[0042] For any two trajectory parameters , The corresponding convergence value of the iterative filter is and ,like Then the two clusters are merged, and the number of clusters is... The set of cluster centers is ; Representing scale parameters The number of target tracks below scale parameter The trajectory parameters are iteratively filtered and corrected.
[0043] Furthermore, the method for determining the optimal number of target tracks and track start parameters in step 5 includes the following steps:
[0044] Based on the cluster analysis results, the optimal number of clusters is determined by the number of clusters with the longest survival time. The optimal clustering scale is the cluster center with the smallest cluster center drift velocity. ,but The corresponding cluster center is the optimal cluster center;
[0045] Optimal number of clusters for,
[0046]
[0047] in, , , , Number of clusters The life cycle;
[0048] Optimal clustering scale ,in,
[0049]
[0050] The drift velocity of the cluster centers. For the first The first cluster center Dimensional components;
[0051] Then the optimal number of clusters That is, the true number of target trajectories, and the optimal clustering scale. Corresponding clustering parameters These are the actual parameters of the target trajectory.
[0052] Compared with the prior art, the beneficial effects of the present invention are as follows:
[0053] 1. This method utilizes the energy likelihood ratio characteristics of measurements in different bands to eliminate measurements with relatively low data quality, thereby ensuring that the measurements used for track initiation have relatively good data quality and improving the reliability of track initiation parameters.
[0054] 2. This method uses the energy likelihood ratio characteristics measured in different bands to correct the initial trajectory selection parameters, which improves the accuracy of the initial value based on multi-scale clustering iterative filtering. It can effectively reduce the number of iterations and achieve rapid and accurate acquisition of the actual number of trajectory parameters and trajectory parameters at the beginning of the target trajectory.
[0055] 3. This invention can be applied to both single-platform multi-array multi-band cooperative tracking situational awareness scenarios and multi-platform multi-band cooperative tracking situational awareness scenarios. The method has strong versatility, broad market prospects, and huge economic potential. Attached Figure Description
[0056] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the 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.
[0057] Figure 1 This is a flowchart of the method for determining the initial parameters of a sea surface target track by multi-band energy feature fusion in Example 2;
[0058] Figure 2 This is a diagram showing the initial effect of the Hough parameter space in Example 2;
[0059] Figure 3 This is the initial effect diagram of the Cartesian coordinate system in Example 2. Detailed Implementation
[0060] The embodiments of this application will now be described in detail with reference to the accompanying drawings.
[0061] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0062] Example 1
[0063] A method for determining the initial parameters of a sea surface target track by multi-band energy feature fusion includes the following steps:
[0064] Step 1: Convert the radar echo information into Hough space and divide the corresponding Hough parameter space into cell spaces;
[0065] Step 2: Based on the energy likelihood ratio of candidate measurements in radar echo information, perform knowledge feature-assisted Hough parameter space accumulation for different bands to obtain the cell space accumulation value.
[0066] Step 3: Perform low-threshold initial track selection based on the accumulated value of the cell space, and correct the initial track parameters based on the energy characteristics of different bands.
[0067] Step 4: Perform iterative filtering correction on the modified initial trajectory parameters based on multi-scale clustering;
[0068] Step 5: Optimize the track parameters after iterative filtering correction to determine the optimal number of target track start points and track start parameters.
[0069] In this embodiment, based on the energy information provided by batch measurements in different radar bands, the energy likelihood ratio originating from the corresponding band measurements is calculated. Then, using the calculated energy likelihood ratio of the corresponding band measurements as a reference to corresponding empirical values, measurement values with relatively low data quality are eliminated to assist in the accumulation of Hough spatial parameters. The initial track parameters after the Hough transform parameter spatial accumulation are then corrected by combining the energy likelihood ratio of the corresponding band measurements. Multi-scale clustering is then performed on the corrected initial track. Finally, an iterative filtering method is used to analyze and obtain the actual number of initial tracks and the initial track parameters of the target. This method has high initial accuracy, fast convergence speed, and strong anti-false alarm capability.
[0070] Example 2
[0071] See Figure 1-3 This embodiment uses the energy information provided by P / X multi-band batch measurements to calculate the corresponding energy likelihood ratios from P-band and X-band measurements as an example to illustrate the process and effect of the method for determining the initial parameters of sea surface target tracks by multi-band energy feature fusion of the present invention. The specific operation steps are as follows:
[0072] 1) Hough parameter space partitioning
[0073] The radar echo information in the P / X band is converted to Hough space, and the corresponding Hough parameter space is divided into segments of size [missing information]. In the cell space, the sub-cell in the k-th row and m-th column can be represented as ,
[0074]
[0075]
[0076] in It is a constant; and They are the unit lengths The k-th row and m-th sub-unit Parameter center coordinates and Parameter center coordinates;
[0077] The Hough transform process is essentially a voting process for parameter space cells; the higher the number of votes a cell receives, the higher the reliability of the corresponding trajectory parameters.
[0078] 2) Dual-band knowledge features assist in the accumulation of Hough parameter space.
[0079] For candidate measurements, the energy likelihood ratio based on energy information is denoted as clustering if the measurement originates from the P-band. , For clustering The Middle The energy likelihood ratio of the P-band measurement is calculated based on the energy information obtained from the measurement:
[0080]
[0081] In the formula, The target's measured position coordinates in the radar's NED (Navigation Coordinate System). The energy probability density function of the effective measurements generated for target t follows a Rayleigh distribution. Signal-to-noise ratio; The energy probability density function of false alarm measurements with only noise follows a Rayleigh distribution; For the corresponding detection threshold, and satisfying , This represents the probability of a false alarm.
[0082] Similarly, if the measurement originates from the X-band, clustering... Then clustering The Middle Individual measurements are obtained through energy information. The calculated X-band measurement energy likelihood ratio is:
[0083]
[0084] In the formula, Indicates correction v -1 order Bessel function, when At this point, the K-distribution transforms into a Rayleigh distribution. v Let be the order of the Bessel function. b for K Distribution constant parameter, This represents the probability of a false alarm. For detection probability, This refers to the signal-to-noise ratio.
[0085] Batch measurement of radar traversal and For all sub-units in the parameter space obtained in step 1), a voting accumulation is performed, using the sub-unit in row k and column m as the basis. For example, the voting accumulation rule is: if the sub-unit center error And the corresponding radar-measured energy likelihood is higher than or The corresponding accumulated value Add 1, otherwise don't add. Empirical values for the energy likelihood ratio threshold to be measured in the P-band. Empirical values for the energy likelihood ratio threshold for X-band measurements.
[0086] in Represented as,
[0087]
[0088] In the formula, and For the first Each measurement in the NED coordinate system Xianghe Location information.
[0089] Represented as,
[0090]
[0091] In the formula, For the voting indicator function, if ,but =1; otherwise, =0.
[0092] 3) Initial selection of low-threshold tracks
[0093] Set a low threshold T for the accumulated voting results obtained in step 2). An election was conducted to obtain a preliminary set of trajectory parameters. The election conditions are as follows: if If the election is successful, it is recorded as . Otherwise, the election fails and is recorded as 0. Among them, Starting window length Detection probability , It is an election factor.
[0094] 4) Correction of initial trajectory parameters based on dual-band energy characteristics
[0095] Step 3) Obtain the initial set of trajectory parameters The corresponding radar measurement clustering. For and Each radar measurement calculates the initial set of trajectory parameters. Sub-unit center error ,
[0096]
[0097] For the initial set of trajectory parameters of The parameters are initially corrected, and the correction value is...
[0098]
[0099] In the formula, This is an indicator function, taking the value 0 or 1. =1 indicates that the measurement originated from the P-band; otherwise, it originated from the X-band.
[0100] Correction value according to formula get Parameter correspondence The correction value is
[0101]
[0102] Then for the initial set of trajectory parameters The initial correction value is And there is,
[0103]
[0104] 5) Iterative filtering correction of trajectory parameters based on multi-scale clustering
[0105] The distribution function of the initial selected track parameter set obtained in step 4) is:
[0106]
[0107] in, Let be the initial parameters for the j-th trajectory, and n represents the number of initially selected paths. According to scale-space theory, The multi-scale clustering representation is as follows:
[0108]
[0109] in, The convolution factor, for
[0110]
[0111] Given the iteration tolerance error For each given scale Initial parameters for each flight path , , For a single correction parameter set The number of;
[0112] make = ,
[0113] in As the initial value for iteration, according to Multi-scale clustering performs iterative computation until... The iterative calculation ends, and the initial trajectory selection parameters are obtained. The convergence value of the iterative filter is denoted as . .
[0114] For any two trajectory parameters , The corresponding convergence value of the iterative filter is and ,like Then the two clusters are merged, and the number of clusters is... The set of cluster centers is . Representing scale parameters The number of target tracks below scale parameter The trajectory parameters are iteratively filtered and corrected.
[0115] 6) Determine the optimal number of target tracks and track initial parameters.
[0116] Based on the cluster analysis results obtained in step 5), the optimal number of clusters is determined by the number of clusters with the longest survival time. The optimal clustering scale is the cluster center with the smallest cluster center drift velocity. ,but The corresponding cluster center is the optimal cluster center.
[0117] Optimal number of clusters for,
[0118]
[0119] in,
[0120] , ,
[0121] Number of clusters The life cycle.
[0122] Optimal clustering scale for,
[0123]
[0124] in,
[0125]
[0126] in, The drift velocity of the cluster centers. For the first The first cluster center Dimensional components.
[0127] Then the optimal number of clusters That is, the true number of target trajectories, and the optimal clustering scale. Corresponding clustering parameters These are the actual parameters of the target trajectory.
[0128] This method is implemented in a scenario where multiple sea surface targets are moving at a constant velocity in a straight line. The initial effect diagram of this method for multi-target tracking on the sea surface is shown below. Figure 2 , 3 As shown, where Figure 2 This is the initial effect diagram of the Hough parameter space. Figure 3 This is the initial effect diagram. In this embodiment, the data quality difference caused by the energy information measured in the auxiliary array P-band and the main array X-band is represented by the energy likelihood ratio. The energy likelihood ratio is used to eliminate relatively low-quality measurements to assist in the accumulation of Hough spatial parameters. A simple iterative filtering method based on multi-scale clustering is used to obtain the target's true trajectory initial parameters with less computation while suppressing false tracks. This invention has high initial accuracy, fast convergence speed, and strong anti-false alarm capability.
[0129] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A method for determining the initial parameters of a sea surface target trajectory based on multi-band energy feature fusion, characterized in that, Includes the following steps: Step 1: Convert the radar echo information into Hough space and divide the corresponding Hough parameter space into cell spaces; Step 2: Based on the energy likelihood ratio of candidate measurements in radar echo information, perform Hough parameter space accumulation assisted by knowledge features of different bands to obtain the cell space accumulation value; the method for Hough parameter space accumulation assisted by knowledge features of different bands includes the following steps: Regarding the first Calculate the energy likelihood ratio of the corresponding band based on the energy information of each measurement. : In the formula, For the first Each measurement uses energy information. The energy probability density function of the effective measurements generated for the target follows a Rayleigh distribution. Signal-to-noise ratio; The energy probability density function of false alarm measurements with only noise follows a Rayleigh distribution; For the corresponding detection threshold, and satisfying , This represents the probability of a false alarm. By voting on the parameter space units using the Hough transform, we obtain... The voting results of the unit; Traverse the radar voting results, accumulate the voting results for all sub-cells in the divided parameter space, and obtain the accumulated voting value of the cell space. The voting accumulation rule is: if the sub-unit center error And the corresponding radar-measured energy likelihood is higher than or The corresponding accumulated vote value Add 1, otherwise don't add, where Empirical values for the energy likelihood ratio threshold in the P-band measurement. Empirical values for the energy likelihood ratio threshold in the X-band; Step 3: Perform low-threshold initial track selection based on the accumulated value of the cell space, and correct the initial track parameters based on the energy characteristics of different bands. Step 4: Perform iterative filtering correction on the modified initial trajectory parameters based on multi-scale clustering; Step 5: Optimize the track parameters after iterative filtering correction to determine the optimal number of target track start points and track start parameters.
2. The method for determining the initial parameters of a sea surface target trajectory based on multi-band energy feature fusion according to claim 1, characterized in that, The Hough parameter space partitioning method in step 1 is as follows: Radar echo information from different bands is converted to Hough space, and the corresponding Hough parameter space is partitioned into segments of size [missing information]. In the cell space, the sub-cell in the k-th row and m-th column can be represented as , It is a constant; and They are the unit lengths The k-th row and m-th sub-unit Parameter center coordinates and Parameter center coordinates.
3. The method for determining the initial parameters of a sea surface target trajectory based on multi-band energy feature fusion according to claim 1, characterized in that, The initial selection method for low-threshold tracks in step 3 is as follows: Set a low threshold T, and select tracks based on accumulated voting values. An election was conducted to obtain a preliminary set of trajectory parameters. The election conditions are as follows: if If the election is successful, it is recorded as . Otherwise, the election fails and is recorded as 0; among which, Starting window length Detection probability , It is an election factor.
4. The method for determining the initial parameters of a sea surface target trajectory by multi-band energy feature fusion according to claim 3, characterized in that, The method for correcting the initial track parameters based on energy characteristics of different wavebands in step 3 is as follows: For the initial set of trajectory parameters of The parameters are initially corrected, and the correction value is... In the formula, This is an indicator function, taking the value 0 or 1. =1 indicates that the measurement originated from the P-band; otherwise, it originated from the X-band. get Parameter correspondence The correction value is Then for the initial set of trajectory parameters The initial correction value is .
5. The method for determining the initial parameters of a sea surface target trajectory by multi-band energy feature fusion according to claim 3, characterized in that, Step 4, the iterative filtering correction method for track parameters based on multi-scale clustering, includes the following steps: The distribution function of the revised initial track parameter set is: in, Let be the initial parameters for the j-th trajectory, and ; n is the number of initially selected paths; according to scale space theory, The multi-scale clustering representation is as follows: Given the iteration tolerance error For each given scale For each initial selection parameter of the trajectory , , For a single correction parameter set The number of; make = , in As the initial value for iteration, according to the formula Perform iterative calculations until... The iterative calculation ends, and the initial trajectory selection parameters are obtained. The convergence value of the iterative filter is denoted as . ; For any two trajectory parameters , The corresponding convergence value of the iterative filter is and ,like Then the two clusters are merged, and the number of clusters is... The set of cluster centers is ; Representing scale parameters The number of target tracks below scale parameter The trajectory parameters are iteratively filtered and corrected.
6. The method for determining the initial parameters of a sea surface target trajectory by multi-band energy feature fusion according to claim 3, characterized in that, Step 5, which determines the optimal number of initial target tracks and the initial track parameters, includes the following steps: Based on the cluster analysis results, the optimal number of clusters is determined by the number of clusters with the longest survival time. The optimal clustering scale is the cluster center with the smallest cluster center drift velocity. ,but The corresponding cluster center is the optimal cluster center; Optimal number of clusters for, in, , , , Number of clusters The life cycle; Optimal clustering scale ,in, The drift velocity of the cluster centers. For the first The first cluster center Dimensional components; Then the optimal number of clusters That is, the true number of target trajectories, and the optimal clustering scale. Corresponding clustering parameters These are the actual parameters of the target trajectory.