The tracking initiation problem is examined in the context of autonomous bearings-only-tracking (BOT) of a single appearing/disappearing target in the presence of clutter measurements. In general, this problem suffers from a combinatorial explosion in the number of potential tracks resulted from the uncertainty in the linkage between the target and the measurement. In addition, the nonlinear measurements lead to a non-Gaussian posterior probability density function in the optimal Bayesian sequential estimation framework. The consequence of this nonlinear/non-Gaussian context is the absence of a closed-form solution. 

HAO Chengpeng from the Institute of Acoustics, Chinese Academy of Sciences, and LIU Bin from the School of Computer Science and Technology, Nanjing University of Posts and Telecommunications propose a particle filtering algorithm for sequential BOT initiation.  

The measurement origin uncertainty, target presence uncertainty, and the nonlinear non-Gaussian factors are handled jointly within a Bayesian sequential estimation framework. Based on such Bayesian formalism, the sequential Monte Carlo algorithm is derived. Performance of the proposed approach is evaluated via numerical simulations and related methods are involved for performance comparison. It is shown that the proposed algorithm provides a remarkable performance improvement in target detection, compared with the commonly used probabilistic data association based methods. Moreover, it gives accurate estimation of the target’s state, as indicated by a comparison with the posterior Cramer-Rao lower bounds. 

This research was supported by the National Natural Science Foundation (NSF) of China (Grants nos. 61302158, 61172166, 61100135, 61170065, and 61373017), Provincial Science and Technology Plan (NSF) of Jiangsu province (Grant no. BK20130869), and Natural Science Research Project for Colleges and Universities in Jiangsu province (Grant no. 13KJB520019).   

The research with the title of “Sequential Bearings-Only-Tracking Initiation with Particle Filtering Method” has been published on the recently issued The Scientific World Journal (Volume 2013, Article ID 489121, 7 pages). 

Contact: 

HAO Chengpeng 

Institute of Acoustics, Chinese Academy of Sciences, Beijing, 100190, China 

Email:  haochengp@mail.ioa.ac.cn