Localization is one of the key techniques in wireless sensor network. One of the main problems in indoor mobile localization is non-line-of-sight (NLOS) propagation. And the NLOS effects will lead to a large localization error. So the NLOS problem is the biggest challenge for accurate mobile location estimation in WSN. In this paper, we propose a likelihood matrix correction based mixed Kalman and -infinity filter (LC-MKHF) method. A likelihood matrix based correction method is firstly proposed to correct the LOS and NLOS measurements. This method does not need the prior information about the statistical properties of the NLOS errors. So it is independent of the physical measurement ways. And then a mixed Kalman and -infinity filter method is proposed to improve the range measurement. Simulation results show that the LC-MKHF algorithm has higher estimate accuracy in comparison with no-filter, Kalman filter, and -infinity filter methods. And it is robust to the NLOS errors.
Cheng et al. REF integrated the Kalman filter with the H-infinity filter in IMM to improve range measurement.
Indoor Mobile Localization in Wireless Sensor Network under Unknown NLOS Errors
International Journal of Distributed Sensor Networks
Computer Science