A Multi-target Tracking Method Based on Random Set Theory

A multi-target tracking, random set technology, applied in the direction of reflection/re-radiation of radio waves, instruments, measuring devices, etc., can solve problems such as unfavorable engineering applications
CN106707272BActive Publication Date: 2019-11-05UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
UNIV OF ELECTRONICS SCI & TECH OF CHINA
Publication Date
2019-11-05

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Abstract

In order to realize effective tracking of multi-target states in complex scenes under the condition of limited resources, the present invention provides a multi-target tracking method based on random set theory. In the track prediction stage, the method first predicts each surviving track in one step according to the Bayesian criterion, and then adapts the birth target track according to the target prior information; in the track update stage, first calculates Corresponding existence weight probability and joint multi-target probability density function, and then calculate the posterior probability parameters of each target label, including existence probability and corresponding probability density function. This method has the advantages of low approximation cost, excellent performance under limited resources, and robustness applicable to any measurement model. In addition, since the algorithm of the invention occupies less system computing resources, it has good engineering application prospects.
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Description

technical field

[0001] The invention belongs to the field of multi-target tracking, and in particular relates to the technical field of multi-target tracking under random set theory. Background technique

[0002] In recent years, multi-target detection and tracking technology based on random set statistics theory has received extensive attention. This type of method avoids the data association of traditional multi-target tracking, and can handle the situation where the number of targets is unknown and time-varying. At present, most random set tracking algorithms, such as probability hypothesis density filter, radix probability hypothesis density filter, labeled multi-objective Bernoulli filter, etc., are all aimed at standard measurement models (see literature R.Mahler, Statistical Multisource-Multitarget Information Fusion, Norwood, MA: Artech House, 2007.). However, in the actual multi-target tracking scenario, the standard measurement model has many limitations, and many...

Claims

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