Hough Transform Fast Track Initiation Method Based on Velocity Constraint

Abstract

The invention provides a velocity restraint-based new Hough transformation fast track starting method under dense cluttered environment. The method comprises the following steps: performing combination pairing and velocity restraint on data measured by a sensor in all scanning periods, deleting a part of pairs formed by clutter, resolving the accurate intersection points of pairs in a parameter space by utilizing a Hough transformation formula, extracting public intersection points through the segmentation and threshold setting of the parameter space, to obtain a candidate track, and screening the following track by utilizing the speed restraint, to obtain a final confirmation track. According to the method, the false track formed by the clutter can be effectively inhibited, the fast starting of the track can be guaranteed, and better robustness on the clutter interference can be achieved.

Description

technical field [0001] The invention belongs to sensor network information fusion technology, relates to the problem of multi-target track initiation in data fusion, and provides a new method for quickly starting track in dense clutter environment. Background technique [0002] Track initiation is the primary problem of multi-target track processing. The so-called track initiation is to use various correlation algorithms and evaluation criteria to quickly form the real track of the target and reduce the false track at the same time. Since the target distance is far away at the beginning of the track, the resolution of sensor detection is low, and the measurement accuracy is poor. In addition, there is no real statistical law for the appearance of true and false targets. Therefore, in the multi-target track processing, the track initial is a difficult problem to deal with. [0003] The current track initiation algorithm mainly includes two types: sequential processing method...

AI Technical Summary

Problems solved by technology

However, the current methods based on Hough transform only roughly estimate the corresponding intersection points in the parameter space of the points that are on or approximately on the same straight line in the data space, and do not give the points in the data space that are on the same straight line in the parameter space. Accurate intersection points, so in the initial stage of the track, although this type of method can correctly start the track of the target, it will also start many false tracks generated by clutter, which is not conducive to subsequent target tracking

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