Multi-Target Joint Detection; Tracking and Classification Based on Marginal GLMB Filter and Belief Function Theory

Sensors

Minzhe Li¹  Jun Liang¹  Han Pan¹  Zhongliang Jing¹ 

[1] School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China;

关键词: joint detection;    tracking and classification;    continuous transferable belief model;    marginal GLMB filter;    multi-model;   

DOI  :  10.3390/s20154235

来源: DOAJ

【 摘 要 】

This paper proposes a new solution to multi-target joint detection, tracking and classification based on labeled random finite set (RFS) and belief function theory. A class dependent multi-model marginal generalized labeled multi-Bernoulli (MGLMB) filter is developed to analytically calculate the multi-target number, state estimates and model probabilities. In addition, a two-level classifier based on continuous transferable belief model (cTBM) is designed for target classification. To make full use of the kinematic characteristics for classification, both the dynamic modes and states are considered in the classifier, the model dependent class beliefs are computed on the continuous state feature subspace corresponding to different dynamic modes and then fused. As a result that the uncertainty about the classes is well described for decision, the classification results are more reasonable and robust. Moreover, as the estimation and classification problems are jointly solved, the tracking and classification performance are both improved. In the simulation, a scenario contains multi-target with miss detection and dense clutter is used. The performance of multi-target detection, tracking and classification is better than traditional methods based on Bayesian classifier or single model. Simulation results are illustrated to demonstrate the effectiveness and superiority of the proposed algorithm.

【 授权许可】

Unknown