Radar image based moving target morphology detecting and tracking method

Abstract

The invention discloses a radar image based moving target morphology detecting and tracking method, which is characterized by firstly dividing gray scales after collecting one frame data, adopting a frame differential method to remove most fixed clutter noises, then adopting a sliding window morphology matching algorithm to isolate the target, accurately positioning the target through target centralization, producing binary images, then combining multiframe binary images with the movement law of the target to carry out track correlation, further removing the fixed target and noise interference and adopting second matching and forecasting to solve the problems of weak signal searching and noise interference, thus realizing lossless tracking. The method features simpleness, less calculated amount, obvious de-noising effect, timely target capturing and high success rate, and can realize real-time detection and lossless tracking of the moving target.

Description

technical field [0001] The invention relates to a detection and tracking technology for a moving target in the air, and belongs to the technical field of image processing target segmentation. Background technique [0002] A certain type of aircraft landing radar uses the alternate work of the heading antenna and the glide antenna to locate the moving target, and its echo information is presented on the radar screen, including interference information such as moving targets, stationary objects, clutter and noise. However, the detection of moving targets cannot be detected automatically by radar, but must rely on the radar operator. At the same time, the video signal generated by radar has a large bandwidth, which is not conducive to transmission. Therefore, it is necessary to sample and quantize the radar echo signal into a digital signal, and generate a digital image containing distance and angle information, and transform the problem into the category of digital image proce...

AI Technical Summary

Problems solved by technology

[0007] After collecting a frame of data, first divide the gray level, use the frame difference method to remove most of the fixed object interference, and then use the sliding window shape matching algorithm to separate the target, use the target center to accurately locate the target position, and generate The binary image is then combined with multi-frame binary images and target motion rules for track correlation to further eliminate fixed targets and noise interference, and use secondary matching and prediction to solve the problem of weak signal search and noise interference to achieve no-loss tracking.

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