Static fusion method based on Gaussian mixture probability hypothesis density filter

Abstract

The invention discloses a static fusion method based on a Gaussian mixture probability hypothesis density filter, and the method comprises the steps of migrating a multi-target tracking algorithm in atime dimension to a sensor dimension, and enabling a multi-source target static fusion problem to be regarded as a multi-target tracking problem with a time interval of 0; modeling a state model andan observation model of a target on the basis, performing static fusion after a high-speed effective tracking algorithm, namely Gaussian mixture probability hypothesis density (GMPHD) filtering, is modified according to the models, obtaining a fusion result with higher precision, and therefore, the operation efficiency is greatly improved. The method is suitable for input measurement under variousconditions, and can effectively improve the positioning precision and the operation efficiency of a fusion result.

Description

technical field

[0001] The invention belongs to the field of information processing, and in particular relates to a static fusion method of filters. Background technique

[0002] When performing underwater target tracking, it is usually hoped that multiple sensors can be used to improve tracking performance by expanding and strengthening the observation space, that is, a multi-base target tracking system. In order to integrate measurement data from all sources, multi-site data fusion is often required. By fusing the measurement data of different sensors obtained in a single scan, preprocessing and screening of information is carried out to obtain an equivalent measurement set with higher precision and fewer false alarms, thereby improving the accuracy of subsequent target tracking. One process is called static fusion. Currently commonly used static fusion methods include Scan box method, grid screening, and Monte Carlo sampling static fusion methods. However, although the...

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