Abstract
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.
Original language | English |
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Article number | 6461426 |
Pages (from-to) | 2824-2830 |
Number of pages | 7 |
Journal | IEEE Transactions on Vehicular Technology |
Volume | 62 |
Issue number | 6 |
DOIs | |
Publication status | Published - 2013 |
Externally published | Yes |
Keywords
- Distributed estimation
- jump Markov nonlinear system
- simultaneous localization and tracking (SLAT)