Bearing-only Target Tracking using a Bank of MAP Estimators
Nonlinear estimation problems, such as bearingonly tracking, are often addressed using linearized estimators, e.g., the extended Kalman filter (EKF). These estimators generally suffer from linearization errors as well as the inability to track multimodal probability density functions (pdfs). In this paper, we propose a bank of batch maximum a posteriori (MAP) estimators as a general estimation framework that provides relinearization of the entire state history, multi-hypothesis tracking, and an efficient hypothesis generation scheme. Each estimator in the bank is initialized using a locally optimal state estimate for the current time step. Every time a new measurement becomes available, we convert the nonlinear cost function corresponding to this relaxed one-step subproblem into polynomial form, allowing to analytically and efficiently compute all stationary points. This local optimization generates highly probable hypotheses for the target trajectory and greatly improves the quality of the overall MAP estimate. Additionally, pruning and marginalization are employed to control the computational cost. Monte Carlo simulations and real-world experiments show that the proposed approach significantly outperforms the EKF, the standard batch MAP estimator, and the particle filter (PF), in terms of accuracy and consistency.
Guoquan P. Huang Ke X. Zhou Nikolas Trawny Stergios I. Roumeliotis
Department of Computer Science and Engineering,University of Minnesota,Minneapolis,MN 55455,USA Department of Electrical and Computer Engineering,University of Minnesota,Minneapolis,MN 55455,USA NASA Jet Propulsion Laboratory,Pasadena,CA 91109,USA
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
4998-5005
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）