Mobile Location Estimator in a Rough Wireless Environment Using Extended Kalman-Based IMM and Data Fusion

• Published in: IEEE transactions on vehicular technology 2009-03, Vol.58 (3), p.1157-1169
• Main Authors: CHEN, Bor-Sen, YANG, Chang-Yi, LIAO, Feng-Ko, LIAO, Jung-Feng
• Format: Article
• Language: English
• Subjects: Applied sciences; Base stations; Capacitors. Resistors. Filters; Circuit properties; Data fusion; Data integration; Detection, estimation, filtering, equalization, prediction; Electric, optical and optoelectronic circuits; Electrical engineering. Electrical power engineering; Electronic circuits; Electronics; Estimators; Exact sciences and technology; extended Kalman-based interacting multiple model (EK-IMM) smoother; Filtering; Information theory; Information, signal and communications theory; Intelligent transportation systems; Interactive; Kalman filtering; Kalman filters; line-of-sight (LOS); Markov processes; Microcell networks; mobile location estimation; non-LOS (NLOS); Position (location); received signal strength (RSS); Robustness; Signal and communications theory; Signal, noise; Stations; Switches; Switching, multiplexing, switched capacity circuits; Telecommunications and information theory; Time measurement; time of arrival (TOA); Various equipment and components; Wireless communication; Wireless communications; Wireless networks
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2008.928649

An extended Kalman-based interacting multiple model (EK-IMM) smoother is proposed for mobile location estimation with the data fusion of the time of arrival (TOA) and the received signal strength (RSS) measurements in a rough wireless environment. The extended Kalman filter is used for nonlinear estimation. The IMM is employed as a switch between the line-of-sight (LOS) and non-LOS (NLOS) states, which are considered to be a Markov process with two interactive modes. Combining extended Kalman filtering with the IMM scheme for accurately smooth range estimation between the corresponding base station (BS) and mobile station (MS) in the rough wireless environment, the proposed robust mobile location estimator, in association with data fusion, can efficiently mitigate the NLOS effects on the measurement range error. Simulation results illustrate that the performance of the proposed method has been significantly improved in the LOS/NLOS transition case. Moreover, the performance of the EK-IMM smoother with data fusion is also better than that with a single measurement used alone.