Abstract
As one of the key technologies of wireless sensor networks (WSNs), the localization of mobile nodes (MN) is one of the most significant research topics in WSNs. When a line-of-sight (LOS) channel is available, accuracy localization result can be obtained. Motivated by the fact that the non-line-of-sight (NLOS) propagation of signal is ubiquitous and decreases the accuracy of localization, we propose a MN localization algorithm in mixed LOS/NLOS environments. Considering the characteristics of NLOS error, we propose a localization algorithm based on vote selection mechanisms to filter the distance measurements and reserve the reliable measurements. Then a modified probabilistic data association algorithm is proposed to fuse the multiple measurements reserved from the vote selection. The position of the MN is finally determined by a linear least squares algorithm based on reference nodes selection. This algorithm effectively mitigates various kinds of NLOS errors and largely improves the localization accuracy of the MN in mixed LOS/NLOS environments. The simulation and experiments results show that the proposed algorithm has better robustness and higher localization accuracy than other methods.
Highlights
Wireless sensor networks (WSNs) have attracted significant attention in recent years
Methods for mitigating negative impact from Non-line of sight (NLOS) in existing localization algorithms fall into two major categories: Some researchers perform line of sight (LOS) reconstruction with the distance measurement
This paper presents a novel localization algorithm for NLOS mobile node based on voting selection mechanisms
Summary
Wireless sensor networks (WSNs) have attracted significant attention in recent years. The NLOS error is one of the challenges for accurate mobile location estimation in WSNs. Methods for mitigating negative impact from NLOS in existing localization algorithms fall into two major categories: Some researchers perform LOS reconstruction with the distance measurement. An NLOS identification and probability generation algorithm is proposed by Hammes and Zoubir [23] In this method, the M-estimate based robust KF is used to reduce the NLOS effect and the algorithm yields positioning accuracy similar to the EKF in the LOS environments and even significantly outperforms the REKF in the NLOS environments. They propose the likelihood matrix based correction to correct the measurements, using the mixed Kalman and H-infinity Filter method to improve the range measurement This method does not need the prior information about the statistical properties of the NLOS errors. The proposed algorithm is referenced from the high-frequency distance measurement data processing in base station localization. (4) Localization: Using the filtered distance measurement ri (t) from step (3) and linear least square algorithm based on the selection of BN to complete the localization calculation
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