TWS trace point data rate estimation technology based on histogram statistics
By constructing a TWS (Trajectory Sounder) location map and using histogram statistics, the problem of insufficient trace data rate for different platforms and radar sensors was solved, achieving high efficiency and accuracy in track processing.
Patent Information
- Application Number
- CN202511059122.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-30
- Publication Date
- 2025-11-07
AI Technical Summary
In point fusion processing, the lack of point data rate information from radar sensors on different platforms, with different radars, and in different bands leads to increased false associations of points, a heavier computational burden on track processing, and inconsistent evaluation scales.
By constructing a TWS (Trajectory Sounder) azimuth map and using histogram statistics, the data rate of traces on different platforms, radars, and arrays is estimated. This includes initialization, time interval sequence statistics, histogram merging, and data rate calculation, providing data rate information to support track processing.
It improves the performance of track processing, reduces misassociations of points and the computational burden of track processing, and provides a unified data rate evaluation standard.
Smart Images
Figure CN120908757A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of radar data processing technology, and in particular to a TWS trace data rate estimation technique based on histogram statistics. Background Technology
[0002] When performing point track fusion processing, it is necessary to fuse point tracks from different platforms, radars, and wavebands. However, due to differences in system architecture, development period, and operating mode, some of the radar sensors involved in the fusion process lack point track data rate (scan cycle) information in their output. This lack of point track data rate leads to the following three problems in track fusion processing: 1) In point-to-point association, the lack of time dimension information increases the likelihood of false associations between points, causing track initiation errors; 2) In point-to-track association, the lack of a time-dimensional association gate increases the probability of association between tracks and false points. This increases the risk of tracks being misled by false points and also exacerbates the computational burden of track processing due to the increased computational load of point-to-track pairing; 3) In point track information management and maintenance, the lack of a scan cycle as a reference leads to inconsistent probabilistic evaluation scales when using the same maintenance time under high and low data rates.
[0003] Current techniques for estimating radar antenna scanning periods are concentrated in the field of radar countermeasures. This involves detecting radar echoes and extracting characteristic parameters from the pulse sequences to obtain the radar antenna's scanning period. However, in the field of data processing, research on methods for estimating scanning periods at the point track dimension is relatively limited. Summary of the Invention
[0004] To address the existing technical problems, this invention provides a TWS (True Wireless Stereo) data rate estimation technique based on histogram statistics, comprising the following steps:
[0005] S1, Initialization of the point location map: The point location map is initialized according to the parameters;
[0006] S2, Dot reception: Based on the received dot pattern, derive the first... All of the location maps A sequence of time intervals for dots in a directional raster;
[0007] S3, Timed statistical analysis of point time interval distribution: Establish a time interval statistical histogram, the first... All of the location maps The time interval sequence of points in each azimuth grid is placed into the corresponding interval of the histogram. For a histogram interval, the number of time intervals is counted and the average value of these time intervals is calculated.
[0008] S4, histogram adjacent interval merging: merging adjacent statistical histograms to obtain the merged number and average time difference;
[0009] S5, track data rate estimation: according to the average time difference of each interval in the histogram, the track data rate is obtained by calculating the greatest common divisor.
[0010] Further, in S1, the parameters include the maximum azimuth range , azimuth interval , azimuth map type , azimuth map attribute , azimuth map maintenance time interval , data filtering estimation lower limit and upper limit .
[0011] Further, in S2, the method for calculating the track time interval sequence is:
[0012] For each received track, according to the track attribute and azimuth, find the corresponding azimuth map and the corresponding azimuth grid in the azimuth map , subtract the time of the previous track at the same time from the current track time to obtain the time difference between the two adjacent points in the grid , if the time difference is greater than and less than , record the time difference in this azimuth grid , after a period of time, a series of track time difference sequences are obtained , n is a positive integer, the track time interval sequence in the th azimuth grid of the th azimuth map is:
[0013] .
[0014] Further, in S3, the timing statistical track time interval distribution includes: establishing a time interval statistical histogram, the time range is , the time step for statistics is , the track time interval sequence in the th azimuth grid of the th azimuth map is put into the corresponding interval of the histogram, for an interval of the histogram, the number of time intervals in it is counted and the average value of these time intervals is calculated, the calculation method of the average value of the time interval is: , K is the number of histograms.
[0015] Further, in S4, the number of combined track time intervals is the sum of the number before combination, and the average of the combined track time intervals is the average of the two interval values before combination.
[0016] Further, in S5, the method for calculating the track data rate is that the track time difference in each histogram interval is an integer multiple of the track data rate, and the specific relationship is as follows:
[0017]
[0018] wherein, is an approximate integer multiple of the data rate, and the greatest common divisor of is the track data rate:
[0019] .
[0020] The present application estimates the track data rate of TWS tracks of different platforms, different radars, different arrays and different working modes by constructing a TWS track bearing histogram, and provides information support for subsequent track processing. BRIEF DESCRIPTION OF DRAWINGS
[0021] The present application will be further clarified by the following description with reference to the accompanying drawings.
[0022] Figure 1 is a schematic diagram of the distribution of tracks of the same period and different periods in time in the same bearing interval;
[0023] Figure 2 is a bearing histogram set established for different working modes;
[0024] Figure 3 is a histogram statistical result diagram;
[0025] Figure 4 is a histogram adjacent interval combination result diagram. DETAILED DESCRIPTION
[0026] Most radars work in TWS mode by adopting a periodic scanning mode. At this time, the bearing of the detected track of the radar is continuously changed with time. This means that in the same small bearing interval, the time interval of the track in the same scanning period is small, and the time interval of the tracks of different scanning periods is relatively large (approximately equal to an integer multiple of the scanning period), as shown in Figure 1 After accumulating a number of period times, the larger time interval is selected for statistical analysis, and the scanning period of the radar can be approximately estimated. Based on the above analysis, the present application proposes a TWS track data rate estimation method, which constructs a bearing histogram for tracks of different platforms, different radars, different arrays and different working modes, and estimates the data rate of the corresponding track by using the histogram to count the track information in a period of time.
[0027] In combination Figures 2-4 , the histogram statistics-based TWS track data rate estimation technique of the present application comprises the following steps:
[0028] S1, track azimuth map initialization: the algorithm completes the initialization of the track azimuth map according to parameters, including the maximum azimuth range , azimuth interval , azimuth map attribute , azimuth map maintenance time interval , data filtering estimation upper and lower limits , and . The azimuth map is shown in Figure 2 . In this embodiment, the maximum azimuth range , azimuth interval , azimuth map attribute , azimuth map maintenance time interval , data filtering estimation upper and lower limits , and .
[0029] S2, track reception: for each received track, find the corresponding azimuth map and the corresponding azimuth grid in the azimuth map according to the properties of the track and the azimuth, subtract the time of the previous time point track in the grid from the current track time to obtain the time difference between the two adjacent points in the grid . If the time difference is greater than and less than , record the time difference in this azimuth grid , and after a period of time, a series of track time difference sequences are obtained . The track time interval sequence in all azimuth grids of the i-th azimuth map is:
[0030] .
[0031] In this embodiment, the azimuth map is determined according to the received track platform number, radar number and other properties:
[0032]
[0033] The azimuth grid j of the track in the azimuth map i is determined according to the azimuth value of the track:
[0034]
[0035] wherein, The spacing between each sector square.
[0036] In the orientation grid, the time of the dots Time of the previous time point Subtraction to calculate the time difference of the dots ,
[0037]
[0038] If time difference Greater than the lower time limit And less than the time limit If so, the time difference will be cached.
[0039] S3, Timed statistical analysis of point time interval distribution: Establish a time interval statistical histogram, with a time range of [missing information]. The statistical time step is . No. All of the location maps The time interval sequence of points in each azimuth grid is placed into the corresponding interval of the histogram. The histogram statistical results are as follows: Figure 3 As shown. For a given histogram interval, count the number of time intervals and calculate the average of these time intervals. The method for calculating the average of the time intervals is as follows:
[0040]
[0041] In this embodiment, a histogram is constructed every 1 second, with a time interval of 0.3 seconds and a maximum time of 30 seconds. The time difference buffers in all grid cells of the orientation map are traversed, and each buffer is added to the corresponding time interval in the histogram for counting. The average time difference is then calculated based on the time difference of the points in each interval.
[0042] S4, Histogram Adjacent Interval Merging: Due to quantization, when calculating histograms, the same data rate may be counted in adjacent histograms. These adjacent histograms need to be merged. The merged result is as follows: Figure 4 As shown. The number of time intervals after merging is the sum of the number of intervals before merging, and the average value of the time intervals after merging is the average of the two interval data before merging.
[0043] S5, Data Rate Estimation: Based on the average time difference of the data points within each interval of the histogram, the data rate in the azimuth map is obtained by calculating their greatest common factor. The specific method is as follows:
[0044] Based on the combined histogram statistics, the time difference of the points in each histogram interval is an integer multiple of the point data rate, as shown in the following relationship:
[0045]
[0046] , wherein, is an approximate integer multiple of the data rate. The maximum common factor of is the track data rate:
[0047] .
[0048] The application realizes the estimation of the TWS track data rate of different platforms, different radars, different arrays and different working modes by constructing the TWS track bearing map, provides the data rate information in the track association, track association and track maintenance processing modules for the subsequent track processing, and improves the performance of the track processing.
[0049] In the above description, many specific details are set forth in order to provide a thorough understanding of the present application. However, the above description is only a preferred embodiment of the present application, and the present application can be implemented in many other ways different from the description, so the present application is not limited by the specific implementation disclosed above. Meanwhile, any person skilled in the art can make many possible changes and modifications to the technical solutions of the present application or modify them into equivalent embodiments with the above disclosed methods and technical contents without departing from the scope of the technical solutions of the present application. Any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application without departing from the scope of the technical solutions of the present application still belongs to the protection scope of the technical solutions of the present application.
Claims
1. A histogram statistics based TWS track data rate estimation technique, characterized by: The method comprises the following steps: S1, plot azimuth chart initialization: complete initialization of the plot azimuth chart according to parameters; S2, track reception: from the received track, a sequence of track time intervals in all the raster bins of the second bearing map is derived. S3, timing statistics point trace time interval distribution: establish time interval statistics histogram, the first of all of the point trace time interval sequence in the azimuth grid, put into the corresponding interval of the histogram, for a histogram interval, count the number of time intervals and calculate the average of these time intervals; S4, histogram adjacent interval merging: adjacent statistical histograms are merged to obtain a merged number and average time difference; S5, plot data rate estimation: according to the plot average time difference in each interval of the histogram, the plot data rate is obtained by calculating the greatest common divisor.
2. The histogram statistics based TWS track data rate estimation technique of claim 1, wherein: In S1, the parameters include maximum azimuth range , azimuth interval , azimuth map type , azimuth map attributes , azimuth map maintenance time interval , data filtering estimation lower bound , and upper bound .
3. The histogram statistics based TWS track data rate estimation technique of claim 1, wherein: In S2, the plot time interval sequence calculation method is: For each received dot, find the corresponding orientation map based on the dot's attributes and orientation. and the corresponding azimuth grid in the azimuth map The time difference between two adjacent points in the same grid is obtained by subtracting the current time of the current point from the time of the previous point in that grid. If the time difference is greater than and less than Then record the time difference in this azimuth grid. After accumulating for a period of time, a sequence of point time differences is obtained. n is a positive integer, the th All of the location maps The time interval sequence of the points in each azimuth grid is as follows: 。 4. The histogram statistics based TWS track data rate estimation technique of claim 3, wherein: In s3, the timing statistical point trace time interval distribution includes: establishing a time interval statistical histogram, the time range is , the statistical time step is , the point trace time interval sequence in all orientation grids of the first orientation map is put into the corresponding interval of the histogram, for an interval of the histogram, the number of time intervals therein is counted and the average of the time intervals is calculated, the calculation method of the average of the time intervals is: , K is the number of histograms.
5. The histogram statistics based TWS track data rate estimation technique of claim 1, wherein: In S4, the number of plot time intervals after merging is the sum of the number before merging, and the average of the plot time intervals after merging is the average of the values of the two intervals before merging.
6. The histogram statistics based TWS track data rate estimation technique of claim 1, wherein: In S5, the plot data rate calculation method is: the plot time difference in each histogram interval is an integer multiple of the plot data rate, and the specific relationship is as follows: ， where is an approximate integer multiple of the data rate, and The greatest common divisor of and is the trace data rate: 。