Abstract
As one of the most essential technologies, wireless sensor networks (WSNs) integrate sensor technology, embedded computing technology, and modern network and communication technology, which have become research hotspots in recent years. The localization technique, one of the key techniques for WSN research, determines the application prospects of WSNs to a great extent. The positioning errors of wireless sensor networks are mainly caused by the non-line of sight (NLOS) propagation, occurring in complicated channel environments such as the indoor conditions. Traditional techniques such as the extended Kalman filter (EKF) perform unsatisfactorily in the case of NLOS. In contrast, the robust extended Kalman filter (REKF) acquires accurate position estimates by applying the robust techniques to the EKF in NLOS environments while losing efficiency in LOS. Therefore it is very hard to achieve high performance with a single filter in both LOS and NLOS environments. In this paper, a localization method using a robust extended Kalman filter and track-quality-based (REKF-TQ) fusion algorithm is proposed to mitigate the effect of NLOS errors. Firstly, the EKF and REKF are used in parallel to obtain the location estimates of mobile nodes. After that, we regard the position estimates as observation vectors, which can be implemented to calculate the residuals in the Kalman filter (KF) process. Then two KFs with a new observation vector and equation are used to further filter the estimates, respectively. At last, the acquired position estimates are combined by the fusion algorithm based on the track quality to get the final position vector of mobile node, which will serve as the state vector of both KFs at the next time step. Simulation results illustrate that the TQ-REKF algorithm yields better positioning accuracy than the EKF and REKF in the NLOS environment. Moreover, the proposed algorithm achieves higher accuracy than interacting multiple model algorithm (IMM) with EKF and REKF.
Highlights
The most popular worldwide positioning system is the Global Positioning System (GPS), but it is not a viable option in some areas, especially in indoor environments, due to the fact that GPS positioning is based on multiple satellites and the positioner cannot acquire accurate signals in indoors environments because of obstructions such as reinforced concrete
An algorithm using a robust extended Kalman filter and a fusion method based on the weighted track quality is proposed to mitigate non-line of sight (NLOS) errors
The extended Kalman filter (EKF) equations have been reformulated as a linearized regression model, which allows us to apply robust estimation techniques
Summary
The most popular worldwide positioning system is the Global Positioning System (GPS), but it is not a viable option in some areas, especially in indoor environments, due to the fact that GPS positioning is based on multiple satellites and the positioner cannot acquire accurate signals in indoors environments because of obstructions such as reinforced concrete. According to whether the distance between beacon nodes and mobile nodes is measured during the positioning process, localization algorithms are normally divided into range-based and range-free algorithms. If the distance or angle measurement between a beacon node and a mobile node is acquired from line-of-sight (LOS) propagation, i.e., the radio propagates along a straight line, the position of the unknown node can be estimated by using conventional algorithms. The position estimates are combined based on the fusion algorithm with weighted track quality to obtain the position vector of the mobile node. The proposed algorithm fully combines the advantageous features of the two filters to obtain precise localization result It achieves both efficiency and robustness and even outperforms the EKF in LOS case and REKF in NLOS environment.
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