A multi-sensor maneuvering target tracking method based on the principle of maximum entropy

Abstract

The invention provides a multi-sensor maneuvering target tracking method based on the principle of maximum entropy. The method comprises the steps of establishing a multi-sensor measurement data set from the same target; calculating the degree of membership between data in the multi-sensor measurement data set, obtaining the class center of the measurement data set through clustering processing to complete the data merging of the multi-sensor measurement data set; performing filtering tracking treatment on the merged data by using the interactive multiple model to update the target; output target track data after update and processing. The method performs data clustering processing based on the principle of fuzzy maximum entropy when merging data of the same target from multiple sensors, and proposes the vector analysis method for decoupled solution of the degree of membership to solve the problems of conventional clustering algorithms that errors are caused because the calculation of the degree of membership is coupled with a clustering center and the initialization of a class center is improper. Thus, the method guarantees full utilization of data of multiple sensors and can realize accurate tracking of a maneuvering target based on the data and the interactive multiple model.

Description

technical field [0001] The invention belongs to the field of multi-sensor target tracking and relates to a multi-sensor maneuvering target tracking method based on the maximum entropy criterion. Background technique [0002] In recent years, fuzzy theory has been widely used in the field of target tracking and recognition. The present invention applies fuzzy clustering algorithm to multi-sensor maneuvering target tracking based on maximum entropy criterion. In the classic clustering algorithm, the membership degree and the clustering center enter the set threshold to realize the solution after several iterations, which is faced with the selection of the iterative threshold and multiple iterative operations. In a real-time system, in order to avoid iterative calculations, the method of directly initializing the class center is adopted, but this will cause a large clustering error due to the improper initial selection of the clustering center; , it can also avoid large cluste...

AI Technical Summary

Problems solved by technology

The present invention regards the same target data from multiple sensors as a fuzzy set, adopts the criterion based on fuzzy maximum entropy to carry out data clustering processing when merging the data in the set, and at the same time aims at the membership degree calculation and clustering center in the traditional clustering algorithm Coupling, and the introduction of error problems caused by improper initialization of the class center, a vector analysis method is proposed to decouple and solve the degree of membership

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