Method for detecting self-adaptive growth of a plurality of dim target tracks in image domain

Abstract

The invention discloses a method for detecting self-adaptive growth of a plurality of weak target tracks in an image domain, which relates to the field of tracking of moving targets and mainly solves the problem that the existing method is difficult to detect a plurality of moving target curve tracks under the condition of low signal-to-noise ratio. The method comprises the following detection processes: firstly, on the basis of processing radar echoes to acquire a distance-time image, extracting edges based on a phase consistency model; then, by adopting a digital image processing method for the edge detection result, realizing the purposes of positioning edge centers, removing fake edges and communicating the connected regions with the consistent edge trend through skeletonization and self-adaptive region growth technology; and finally, estimating target moving tracks and moving parameters by combining a clearing method and a iterative least squares method. The invention has the advantage of being capable of accurately estimating the track parameters of a plurality of curves, and can be applied to the data processing field of 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 multi-moving target tracking, in particular to a track detection method for high-speed and weak moving targets, which can be used for 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 applications such as situation assessment and command, and plays a key role in winning the war. With its all-day and all-weather working characteristics, radar has always been the core of the battlefield surveillance system. [0003] Aiming at the problem of accumulation detection of high-speed and weak targets, a combination of short-term coherent accumulation and long-term non-coherent accumulation is usually used. Track processing is an effective means of long-term non-coherent accumulation, and its core issues are track ini...

AI Technical Summary

Problems solved by technology

When detecting a straight line, two parameters are required, that is, the slope distance and the slope; if the curve is detected, even if the random Hough transform is used, the amount of calculation will increase significantly. In practical applications, it is difficult to use the Hough transform method to detect the curved track.

In addition, for images with low signal-to-noise ratio, it is difficult to determine the threshold value when using the Hough transform method to search for the maximum value in the parameter space, and problems such as "virtual peaks" and "missed detection" often occur, and the performance of the Hough transform will drop sharply at this time

Therefore, the disadvantage of this method is that the detection speed is slow; and the accuracy is not high enough

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