Distributed Multiple-Model Estimation for Simultaneous Localization and Tracking With NLOS Mitigation

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.