自动化学报2017，Vol.43Issue(12)：2100-2108,9.DOI:10.16383/j.aas.2017.c160430
Pairwise马尔科夫模型下的势均衡多目标多伯努利滤波器
Cardinality Balanced Multi-target Multi-Bernoulli Filter for Pairwise Markov Model
摘要
Abstract
Because the Markovian and independence assumptions,which are implicitly implied in hidden Markov model (HMM), may not be satisfied by the target model in some practical applications, a more general pairwise Markov model (PMM)has been proposed. PMM relaxes the structural limitations of HMM and can effectively deal with more complex target tracking scenarios. In this paper, a cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter in the framework of PMM is proposed for multi-target tracking in clutter environment, and a closed-form solution to the CB-MeMBer filter under linear Gaussian PMM is presented. Finally,the proposed algorithm is compared with the probability hypothesis density (PHD) filter via simulations using a particular linear Gaussian PMM, which keeps the local physical properties of HMM.Simulation results show that the tracking performance of the proposed algorithm is better than that of the PHD filter.关键词
隐马尔科夫模型/Pairwise马尔科夫模型/多目标跟踪/随机有限集/多伯努利密度/高斯混合Key words
Hidden Markov model (HMM)/pairwise Markov model (PMM)/multi-target tracking/random finite set/multi-Bernoulli density/Gaussian mixture(GM)引用本文复制引用
张光华,韩崇昭,连峰,曾令豪..Pairwise马尔科夫模型下的势均衡多目标多伯努利滤波器[J].自动化学报,2017,43(12):2100-2108,9.基金项目
国家重点基础研究发展计划(973计划)(2013CB329405),国家自然科学基金创新研究群体(61221063),国家自然科学基金(61573271,61473217,61370037)资助Supported by National Basic Research Program of China(973 Program)(2013CB329405),Foundation for Innovative Research Groups of the National Natural Science Foundation of China(61221063),and National Natural Science Foundation of China(61573271,61473217,61370037) （973计划）
