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

A mobile target tracking, multi-sensor technology, applied in the direction of instruments, computer parts, character and pattern recognition, etc., to achieve the effect of accurate tracking

Inactive Publication Date: 2016-03-02
THE 28TH RES INST OF CHINA ELECTRONICS TECH GROUP CORP
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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 degre

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

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Embodiment Construction

[0028] The present invention adopts the maneuvering target tracking method based on fuzzy maximum entropy, such as image 3 Shown, the present invention realizes that maneuvering target is carried out tracking processing from following several steps:

[0029] Step 1, establish a multi-sensor measurement data set from the same target;

[0030] Step 2, calculate the degree of membership between the data in the multi-sensor measurement data set;

[0031] Step 3, obtain the cluster center through clustering processing, and complete the data merging of the multi-sensor measurement data set;

[0032] Step 4, use the interactive multi-model to filter and track the merged data to update the target;

[0033] Step 5, outputting the updated and processed target track data.

[0034] Among them, the derivation process of fuzzy clustering formula based on maximum entropy is as follows:

[0035] (1) Fuzzy clustering based on maximum entropy

[0036] Assume that at time k the fusion cent...

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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...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/23211
Inventor 刘唐兴孙裔申卜卿王妍妍沈海平张一博付强
Owner THE 28TH RES INST OF CHINA ELECTRONICS TECH GROUP CORP
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