Multiple-moving target tracking method

Abstract

The method for tracking mobile by multiple targets uses the associated algorithm of the simplified probability data based on decomposing the cluster matrix. Firstly, the cluster matrix composed of the target signals and the echo signals is decomposed into the set of some small matrices, which is corresponding to composing the entire target tracking area into some irrelevant small tracking areas. The simplified probability data associations and the self-adaptation tracking states are carried out in each small matrix. The target information traced is fed back to the next frame as the target information. The invention decreases the amount of calculation greatly so as to prevent the problem so called the explosion of combination calculation and to meet the requirement of the on-line tracking.

Description

Technical field: [0001] The invention relates to an intelligent mobile multi-target tracking method, which is used for positioning and prediction of multi-maneuvering targets in intelligent transportation, robots, avionics, anti-ballistic missile defense and precision guidance systems, and belongs to the technical field of intelligent information processing. Background technique: [0002] In multi-target tracking systems for national defense and civilian use, the main method is the Joint Probabilistic Data Association (JPDA) method proposed by Bar-Shalom in 1974. Since this method fully considers all the information of data association, it lays the foundation for multi-target data association. However, due to the phenomenon of combinatorial explosion in its calculations, although the algorithm has completely solved the problem of multi-target data association in theory, it is difficult to apply in practice. Since then, many scholars have tried to push JPDA into practical applicat...

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Problems solved by technology

However, due to the combinatorial explosion phenomenon in its calculation amount, although the algorithm has completely solved the problem of multi-objective data association in theory, it is difficult to be practically applied.

Since then, many scholars have been trying to push JPDA into practical applications, such as introducing Hopfield network (Sengupta D, Iltis R. Neural soiution to the multitarget tracking data association problem. IEEE Transcations on Aerospace and Electronic System25(1), 1989: 96~108 ) and simplification under specific conditions (J.A.Roecker, GL.Phillis.Suboptimal jointprobabilistic data association.IEEE Transcation on Aerospace and Electronic System, 29(2), 1993), however, this cannot fundamentally solve this problem, making Previous multi-target tracking methods are difficult to meet the practical requirements

Although the introduction of neural network can overcome the problem of combinatorial explosion of calculation amount, its starting point is based on the most complex overall multi-objective data association calculation, and the simplified algorithm under specific conditions can only be applied to specific conditions.

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