Trace point extraction and judgment method based on multi-dimensional target features

Abstract

The invention provides a trace point extraction and judgment method based on multi-dimensional target features. The method comprises the following steps: carrying out distance and orientation filtering and secondary filtering of times on an input video signal; setting a distance azimuth size frame and an amplitude threshold frame; sampling and quantifying the filtered signals, and extracting multi-dimensional feature information of the filtered signals; according to the quantized distance and azimuth feature information, carrying out preliminary joint judgment in combination with a distance and azimuth size frame, and identifying and marking traces with quality and traces without quality; in combination with an actual target, counting multi-dimensional feature information of various types of targets when a high-probability target trace point is judged to have quality, therefore, trace point extraction and judgment logic is formed, and the input trace point data is extracted and judged; and according to extraction and judgment results, adding credibility characteristic identification to the trace point data, and outputting the data to a back-end reference. By adopting the scheme of the invention, the processing performance of the system can be improved to a certain extent, and meanwhile, the correct probability of trace point extraction and judgment is improved.

Description

technical field [0001] The invention relates to the field of surface surveillance radar, in particular to a point trace extraction and judgment method based on multi-dimensional target features. Background technique [0002] With the development of the air transport industry, the scale of airlines is growing day by day. The annual throughput of the airport is increasing year by year. The means can no longer meet the requirements of airport scene surveillance. Coupled with severe weather conditions such as fog, smog, rain, snow, sand and dust, the airport urgently needs new monitoring methods to realize scene monitoring, and provide controllers with various airport surface traffic conditions under severe weather conditions, night vision, poor visibility, etc. situational awareness. [0003] Europe has proposed an important technical means of airport surface surveillance: surface surveillance radar, as an indispensable independent surveillance method for airport scenes, surf...

AI Technical Summary

Problems solved by technology

[0010] 1. Before the point track is associated with the track, the input data is simply extracted based on the threshold, resulting in some input data that is not within the range of system requirements but has a large range, or within the range of system requirements but not the target Data such as clutter, noise, and multipath interference, due to their large amplitude, also entered the judgment process of point-to-navigation association, resulting in a large amount of data processed by the backend, consuming more processing resources and time for non-system requirements The processing of data within the range, such as useless clutter, noise, multipath interference, etc., reduces the processing performance of the system to a certain extent;

[0011] 2. The extraction and judgment of the point traces only depend on whether it falls into the associated gate and the magnitude of the point traces. The establishment of the associated gate is usually based on the target characteristics such as distance and orientation, and the target characteristics based on it are very limited and large. The probability cannot filter out false dots, resulting in false dots being judged as valid dots;

[0012] 3. When false dots generated by clutter, noise, multipath interference, etc. fall into the correlation gate, if there are multiple dots in the correlation gate, it may lead to misjudgment of dots, especially false dots caused by multipath effects When the distance between the trace and the real trace is relatively close, there is a high probability of misjudgment of the trace; when the amplitude of the false trace is large, the probability of misjudgment of the trace is relatively high;

[0013] 4. The extraction and determination of point traces rely on the correlation gate established by track prediction, but the prediction of track is not absolute, and there are errors caused by measurement errors and different prediction algorithms, that is, the establishment of correlation gate itself also has errors, plus Simple extraction and judgment of the input traces only rely on the amplitude, which further increases the probability of false traces and error correlations

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