期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 62, 期 6, 页码 2824-2830出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2013.2247073
关键词
Distributed estimation; jump Markov nonlinear system; simultaneous localization and tracking (SLAT)
资金
- Chinese Ministry of Science and Technology through the National Basic Research Program of China (973 Program) [2012CB821200, 2012CB821201]
- National Natural Science Foundation of China [61134005, 60921001, 61203044, 90916024, 91116016]
- Beijing Natural Science Foundation [4132040]
This paper studies the problem of simultaneous localization and tracking (SLAT) in non-line-of-sight (NLOS) environments. By combining a target state and a sensor node location into an augmented vector, a nonlinear system with two jumping parameters is formulated in which two independent Markov chains are used to describe the switching of the target maneuvers and the transition of LOS/NLOS, respectively. To derive the state estimate of the proposed jump Markov nonlinear system for each sensor node, an interacting multiple-model (IMM) approach and a cubature Kalman filter (CKF) are employed. As the number of mode-conditioned filters exponentially grows with the increases in the number of active sensor nodes in the centralized fusion, a distributed scheme is adopted to reduce the computational burden, and a covariance intersection (CI) method is used to fuse sensor-based target-state estimates. A numerical example is provided, involving tracking a maneuvering target by a set of sensors, and simulation results show that the proposed filter can track the target and can estimate the positions of active sensor nodes accurately.
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