High-speed weak target flight path detection method of image field

Abstract

The invention discloses a high-speed weak target flight path detection method of an image field, relating to the field of moving object tracking, and mainly solving the problem of extracting a moving object flight path and a target kinematic parameter from data with a low signal-to-noise ratio. The detection process is as follows: firstly, obtaining a time-Doppler image by mixing and accumulating target multiframe data; carrying out edge extracting on the basis of phase equalization model; carrying out skeletonization and binary image de-noising treatment on the edge detection result with a morphology method to realize edge center positioning as well as noise inhibition; and finally, using a least square method to detect the target flight path and estimate a target parameter. The invention can accurately estimate the advantages of a curve flight path parameter, and can be used for the data processing field for monitoring systems, such as radars and the like to realize high-speed weak target detection and tracking.

Description

Technical field [0001] The invention relates to the field of moving target tracking, in particular to a method for detecting high-speed and weak moving target tracks, and can be used in monitoring systems such as radars to detect and track high-speed and weak targets. Background technique [0002] In modern warfare under the high-tech background, continuous monitoring of the battlefield can provide rich strategic and tactical information for situation assessment, command and other applications, and play a key role in winning the war. Radar has always been the core of the battlefield surveillance system for its all-time and all-weather work characteristics. The operating range of traditional ground-based radars is limited by the curvature of the earth, making it difficult to achieve continuous monitoring of a large area. Airborne or spaceborne radars have greatly extended their operating range due to the launch of the observation platform and have the ability to continuously moni...

AI Technical Summary

Problems solved by technology

Therefore, the disadvantage of this method is that the detection speed is too slow to achieve real-time control; and the accuracy is not high enough, resulting in not only the expected information not being detected, but wrong judgments will be made, resulting in a large amount of redundant data

In reality, because the image is disturbed by external noise and the signal-to-noise ratio is relatively low, the performance of the Hough transform will drop sharply at this time, making it difficult to determine the threshold when searching for the maximum value of the parameter space, and often appear "virtual peaks" and "missed detections". "The problem

Images