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Sensors 2011, 11(2), 1641-1656; doi:10.3390/s110201641
Article

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

1,2,*  and 1
Received: 25 December 2010 / Revised: 20 January 2011 / Accepted: 21 January 2011 / Published: 27 January 2011
(This article belongs to the Section Physical Sensors)
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Abstract

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.
Keywords: extended Kalman filter (EKF); mobile location; LOS; NLOS; identification extended Kalman filter (EKF); mobile location; LOS; NLOS; identification
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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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