摘要
针对未知探测概率下多目标跟踪问题,提出一种基于时变滤波算法的多目标概率假设密度(PHD)滤波器.算法推导了未知探测概率PHD递推式,提出了将未知探测概率转化为目标的丢失与接收事件,并依此建立了目标跟踪的马尔科夫模型,给出了该模型下时变卡尔曼滤波最优解,进而在高斯混和PHD(GMPHD)框架下推导了算法闭集解.仿真实验表明,所提出算法在未知且随时间变化的探测概率情形下,仍能实时地跟踪各目标,具有良好的工程应用前景.
According to the general problem of unknown detection probability in the probability hypothesis density（PHD） filter, a PHD algorithm based on the time-varying Kalman filter（TVKF） is proposed. Firstly, PHD recursions without the knowledge of the detection probability are derived. Secondly, the measurements of loss events are modeled as Markov processes, and the optimal estimator with missing sensor data samples is given by using time-varying Kalman filter. Furthermore, the closed form solutions are calculated under the framework of the Gaussian sum based probability hypothesis（GMPHD） filter. The simulation results show that the improved algorithm has better performance in terms of state estimation under the unknown detection probability, and has good application prospects.
出处
《控制与决策》
EI
CSCD
北大核心
2014年第1期57-63,共7页
Control and Decision
基金
国家863计划项目(2010AA7010422
2011AA7014061)
国家自然科学基金项目(60901069)
中国博士后科学基金项目(200902671)
关键词
多目标跟踪
概率假设密度滤波
马尔科夫模型
时变卡尔曼滤波
multi-target tracking
probability hypothesis density filter
Markov processes
time-varying Kalman filter
