Mobile location tracking based on NLOS identification in indoor environments
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Abstract
NLOS propagation is one of the key factors that affect tracking accuracy in indoor environments. An adaptive tracking algorithm was proposed to mitigate the NLOS error for indoor mobile localization. The correlation between adjacent NLOS errors in time was analyzed and exploited. A modified extended Kalman filter (MEKF) was presented which includes the NLOS error as part of the state variables. NLOS identification was achieved based on the state estimation of MEKF. MEKF and NLOS identification were combined to implement the adaptive tracking algorithm. Simulation results demonstrate that the proposed algorithm has better tracking accuracy and adaptability in indoor environments.
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