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Sensors 2011, 11(2), 1641-1656;

Mobile Location with NLOS Identification and Mitigation Based on Modified Kalman Filtering

1,2,* and 1
School of Information Science and Engineering, Southeast University, Nanjing 210096, China
School of Physics and Technology, Nanjing Normal University, Nanjing 210097, China
Author to whom correspondence should be addressed.
Received: 25 December 2010 / Revised: 20 January 2011 / Accepted: 21 January 2011 / Published: 27 January 2011
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In order to enhance accuracy and reliability of wireless location in the mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments, a robust mobile location algorithm is presented to track the position of a mobile node (MN). An extended Kalman filter (EKF) modified in the updating phase is utilized to reduce the NLOS error in rough wireless environments, in which the NLOS bias contained in each measurement range is estimated directly by the constrained optimization method. To identify the change of channel situation between NLOS and LOS, a low complexity identification method based on innovation vectors is proposed. Numerical results illustrate that the location errors of the proposed algorithm are all significantly smaller than those of the iterated NLOS EKF algorithm and the conventional EKF algorithm in different LOS/NLOS conditions. Moreover, this location method does not require any statistical distribution knowledge of the NLOS error. In addition, complexity experiments suggest that this algorithm supports real-time applications. View Full-Text
Keywords: extended Kalman filter (EKF); mobile location; LOS; NLOS; identification extended Kalman filter (EKF); mobile location; LOS; NLOS; identification

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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Ke, W.; Wu, L. Mobile Location with NLOS Identification and Mitigation Based on Modified Kalman Filtering. Sensors 2011, 11, 1641-1656.

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