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

Active Publication Date: 2019-11-05
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0004] The technical problem to be solved by the present invention is that the system computing resource occupied by the existing random set multi-target tracking method for non-standard measurement models increases super-exponentially with the number of targets, which is not conducive to its practical engineering application

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  • A Multi-target Tracking Method Based on Random Set Theory
  • A Multi-target Tracking Method Based on Random Set Theory
  • A Multi-target Tracking Method Based on Random Set Theory

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Embodiment Construction

[0044] In order to describe the content described in the present invention for convenience, at first do following term definition:

[0045] Random set: refers to a given state space Depend on All finite subsets of form a hyperspace then defined in A random variable on is called a random set.

[0046] Set cardinality: refers to the number of elements in the set.

[0047] The present invention mainly adopts the method of computer simulation for verification, and all steps and conclusions are verified correctly on MATLAB-R2014b. The specific implementation steps are as follows:

[0048] Step 1. Initialize system parameters.

[0049] Initialization system parameters include: radar monitoring range [0,60m]×[0,60m], radar distance resolution △r=1m, radar scanning cycle T=1s, total number of observation frames K=28; target survival probability Birth target model as multi-objective Bernoulli distribution in vector of measurements for the current frame Indicates t...

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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.

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...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01S13/72
CPCG01S13/726
Inventor 易伟王佰录李帅李溯琪孔令讲杨晓波崔国龙
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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