Centralized type multi-source generalized correlation tracker based on data compression

Abstract

The invention discloses a centralized type multi-source generalized correlation tracker based on data compression. The technique of the invention belongs to the field of radar data processing. The multiple targets tracking technique in clutter is an important problem difficult to be solved in the field of target tracking. The traditional multi-source multi-target tracking method is difficult to meet the system demand in the actual occasion having certain demands on both real-time algorithm and tracking precision. Through the method of the invention, multiple measurements corresponding to the same target are compressed into an equivalent measurement based on a plot trace merging method in data compression, interconnection hypothesises are generated according to the equivalent measurements and the remained measurements after compressing data, the score of each interconnection hypothesis is calculated according to a score function recursion formula of multi-source generalized correlation algorithm and the blending center state evaluation is realized by choosing the interconnection hypothesis having highest score. The tracker has better tracking precision and real-time property when tracking multiple targets in clutter. The tracker has high application value and can be applied to various centralized type multi-source information bending system.

Description

1. Technical field [0001] The invention relates to a centralized multi-source generalized correlation method based on data compression in the field of radar data processing, and is applicable to information fusion systems of various centralized structures. 2. Background technology [0002] In many practical centralized multi-source multi-target tracking systems, data compression methods are often used to improve the real-time processing speed of the system. Trace synthesis is the most basic data compression method, which combines the traces of multiple sensors on the same target at the same time, and synthesizes multiple detection data into one equivalent data. [0003] The generalized correlation method is a classic method in the maximum likelihood data interconnection technology. It uses the maximum likelihood method to realize the optimal interconnection and handover of the measurement and the track. The main feature is that a scoring function is defined, and through this...

AI Technical Summary

Problems solved by technology

In actual situations where there are certain requirements for system real-time performance and tracking accuracy, the parallel processing structure of the Centralized Multi-sensor Joint Probabilistic Data Association (CMS-JPDA) algorithm and sequence proposed by L.Y.Pao The CMS-JPDA algorithm of the processing structure has high tracking accuracy, but the calculation amount of the algorithm is too large, it is difficult to meet the real-time requirements of the system, and it is difficult to realize in engineering; other classic centralized multi-source and multi-target tracking methods also have the above problems. Such as the centralized multi-source multi-hypothesis method, the centralized multi-source generalized S-dimensional distribution method; the centralized multi-source multi-target tracking method based on the idea of ​​nearest neighbor and probabilistic nearest neighbor has better real-time When the tracking accuracy is low, it is difficult to meet the tracking accuracy requirements of the system

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