Abstract
This paper is devoted to 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 discrete-time stochastic systems with Markov jump parameters is used to describe the switching of LOS/NLOS. A robust algorithm–diagonal interacting multiple model algorithm based on H ∞ filtering (DIMMH) is presented for simultaneous refinement of sensors’ positions and target tracking when measurement noise is of unknown statistics. We use a measurement model from a real mine to handle all non-Gaussian uncertainties typical for mining environments, and analyze the performance of the classical interacting multiple model (IMM) algorithm, the DIMM algorithm and the cubature Kalman filter (CKF).
This work was supported by the National Natural Science Foundation of China (11178017, 61373090, 61303104, 61203238) and the Beijing Natural Science Foundation of China (4132014).
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Fu, X., Shang, Y., Ding, H., Zhou, X. (2014). Diagonal Interacting Multiple Model H ∞ Filtering for Simultaneuos Sensor Localization and Target Tracking with NLOS Mitigation. In: Pan, JS., Snasel, V., Corchado, E., Abraham, A., Wang, SL. (eds) Intelligent Data analysis and its Applications, Volume I. Advances in Intelligent Systems and Computing, vol 297. Springer, Cham. https://doi.org/10.1007/978-3-319-07776-5_7
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DOI: https://doi.org/10.1007/978-3-319-07776-5_7
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