Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Wireless Location in CDMA Cellular Radio Systems
Wireless Location in CDMA Cellular Radio Systems
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Convex Optimization
Robust estimator for non-line-of-sight error mitigation in indoor localization
EURASIP Journal on Applied Signal Processing
A Survey on Wireless Position Estimation
Wireless Personal Communications: An International Journal
Editorial: signal processing for location estimation and tracking in wireless environments
EURASIP Journal on Advances in Signal Processing
Joint Particle Filter and UKF Position Tracking Under Strong NLOS Situation
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Robust mobile terminal tracking in NLOS environments using interacting multiple model algorithm
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Relative location estimation in wireless sensor networks
IEEE Transactions on Signal Processing
Hidden Markov Models for Radio Localization in Mixed LOS/NLOS Conditions
IEEE Transactions on Signal Processing
A Jump Markov Particle Filter for Localization of Moving Terminals in Multipath Indoor Scenarios
IEEE Transactions on Signal Processing - Part I
Nonline-of-sight error mitigation in mobile location
IEEE Transactions on Wireless Communications
Robust Mobile Location Estimator with NLOS Mitigation using Interacting Multiple Model Algorithm
IEEE Transactions on Wireless Communications
Overview of radiolocation in CDMA cellular systems
IEEE Communications Magazine
Robust MT Tracking Based on M-Estimation and Interacting Multiple Model Algorithm
IEEE Transactions on Signal Processing
Hi-index | 35.68 |
An algorithm for mobile terminal (MT) tracking based on time-of-arrival measurements in non-line-of-sight (NLOS) environments is proposed. It is based on NLOS detection together with a modified probabilistic data association approach where different subgroups of range measurements are constructed. Each of the subgroups provide a position estimate of the MT with it's corresponding covariance matrix that are both used in a hypothesis test for NLOS detection. The accepted position estimates are weighted with different probabilities in a Kalman filter framework. Simulation results show a significant increase in positioning accuracy in NLOS environments with respect to both, the extended Kalman filter (EKF) and a NLOS mitigation algorithm from the literature. In LOS environments similar performance to the EKF is achieved. The proposed method does not assume any statistical knowledge of the NLOS errors and only assumes the sensor noise variance to be known.