δ-GLMB filter based on DI in a clutter

For the problem that the performance of existing multi-target tracking algorithm's serious degrades in a dense clutter environment, a novel Doppler information assistant δ-generalised labelled multi-Bernoulli (DI-δ-GLMB) filter is proposed. By introducing DI, a new measurement likelihood functi...

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Bibliographic Details
Published in:Journal of engineering (Stevenage, England) England), 2019-10, Vol.2019 (20), p.7000-7005
Main Authors: Peng, Hua-fu, Huang, Gao-ming, Tian, Wei, Qiu, Hao
Format: Article
Language:English
Subjects:
dense clutter environment
DI probability hypothesis density filter
DI‐δ ‐ GLMB
filtering theory
GLMB filter framework
IET International Radar Conference (IRC 2018)
measurement likelihood function
Monte Carlo methods
multitarget tracking algorithm
novel Doppler information assistant δ‐generalised labelled multi‐Bernoulli filter
probability
sequential Monte Carlo implementation method
set theory
target tracking
tracking filters
δ-glmb filter
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Summary:For the problem that the performance of existing multi-target tracking algorithm's serious degrades in a dense clutter environment, a novel Doppler information assistant δ-generalised labelled multi-Bernoulli (DI-δ-GLMB) filter is proposed. By introducing DI, a new measurement likelihood function is established, and the improved update equation based on the δ-GLMB filter framework is derived. In addition, a sequential Monte Carlo implementation method is given under the non-linear model. Simulation results show that compared with the DI probability hypothesis density filter and the standard δ-GLMB filter, the estimation of the proposed algorithm is more accurate and stable.
ISSN:2051-3305
2051-3305
DOI:10.1049/joe.2019.0471