δ-GLMB filter based on DI in a clutter

The Journal of Engineering

Hao Qiu¹  Hua-fu Peng¹  Wei Tian¹  Gao-ming Huang¹ 

[1] Naval University of Engineering;

关键词: filtering theory;    probability;    monte carlo methods;    set theory;    tracking filters;    target tracking;    sequential monte carlo implementation method;    di probability hypothesis density filter;    multitarget tracking algorithm;    novel doppler information assistant δ-generalised labelled multi-bernoulli filter;    glmb filter framework;    measurement likelihood function;    di-δ - glmb;    dense clutter environment;    δ-glmb filter;   

DOI  :  10.1049/joe.2019.0471

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown