Robust MT Tracking Based on M-Estimation and Interacting Multiple Model Algorithm

Abstract

An algorithm for mobile terminal (MT) tracking based on time-of-arrival measurements in non-line-of-sight (NLOS) environments where NLOS measurements are modeled as positive outliers is proposed. Standard filters such as the extended Kalman filter (EKF) fail because they are sensitive to outliers. In contrast, a robust EKF (REKF) always trades off efficiency in line-of-sight (LOS) versus robustness in NLOS environments and it is not possible to achieve both with the same filter. Instead, we propose to use two filters in parallel in a multiple model framework. An EKF yields high precision in LOS environments whereas an REKF provides robust state estimates when NLOS propagation comes into play. The state estimates of either filters are combined automatically based on the confidence we have for the underlying situation. It is shown via numerical studies that the proposed algorithm yields positioning accuracy similar to the EKF in LOS environments and even significantly outperforms the REKF in NLOS environments.

Mean Shift-Based Mobile Localization Method in Mixed LOS/NLOS Environments for Wireless Sensor Network

“…The second methods utilize all of the measurements with different weights to locate the target. The interacting multiple model (IMM) with different filter approaches such as the Kalman filer [12], the extended Kalman filer [6,13,14], the cubature Kalman filer [15], and the hidden Markov models [16] can be considered as the most classical soft-decision methods. These kinds of methods are practical when only a small number of measurements can be used for positioning.…”

Vote selection mechanisms and probabilistic data association-based mobile node localization algorithm in mixed LOS/NLOS environments

“…The proposed algorithm could significantly improve position accuracy. An NLOS identification and probability generation algorithm is proposed by Hammes and Zoubir [23]. In this method, the M-estimate based robust KF is used to reduce the NLOS effect and the algorithm yields positioning accuracy similar to the EKF in the LOS environments and even significantly outperforms the REKF in the NLOS environments.…”

“…The proposed algorithms can be used in outdoor environments in references [8,9,11,12,16,17,19,20,[22][23][24]. The proposed methods of references [10,13,15,18,25] focus on the indoor localization.…”

RSS-based joint detection and tracking in mixed LOS and NLOS environments

“…The results are further extended to approximate the nonlinear RSS measurement by using fuzzy estimation techniques in [41]. Similar idea has also been adopted to track a mobile terminal by using the M-estimation approach [42]. In [43], a distributed multiple model estimator has been developed for simultaneous localization and tracking.…”