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Static fusion method based on Gaussian mixture probability hypothesis density filter

A Gaussian mixture probability and hypothesis density technology, applied in the field of information processing, can solve the problems of low computational efficiency, poor fusion performance, and insufficient use of measured covariance, and achieve the effect of improving fusion performance.

Pending Publication Date: 2021-02-02
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

However, although the existing methods are simple to implement, the calculation efficiency is low and the covariance of the measurement is not fully utilized, and the fusion performance is poor when the number of sensors is small.

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  • Static fusion method based on Gaussian mixture probability hypothesis density filter
  • Static fusion method based on Gaussian mixture probability hypothesis density filter
  • Static fusion method based on Gaussian mixture probability hypothesis density filter

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[0096] 1. Specify a fixed reference coordinate for sensor nodes and target positions in space, and specify a unified reference direction for orientation measurement. Each sensor returns a bearing measurement and a delay measurement of the target, subject to measurement errors related to the performance of the sensor node. The geometric relationship between the source-target-receiver is used for measurement conversion, and the azimuth and delay measurements are converted into corresponding two-dimensional Cartesian coordinates.

[0097] 2. The present invention regards the multi-sensor static fusion problem as a target tracking problem in the sensor dimension, so in this scene, the time interval of each scan is 0, and the motion state of the target within the sensor observation range is static, and there is no target However, there may be new targets within the observation range of the sensor, so mathematical modeling can be carried out to create corresponding target state mode...

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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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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/25G06F18/2415
Inventor 刘梦凡韩一娜赵伟康杨坤德杨益新李钢虎
Owner NORTHWESTERN POLYTECHNICAL UNIV
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