Abstract
As one of the core technologies of the Internet of Things, wireless sensor network technology is widely used in indoor localization systems. Considering that sensors can be deployed to non-line-of-sight (NLOS) environments to collect information, wireless sensor network technology is used to locate positions in complex NLOS environments to meet the growing positioning needs of people. In this paper, we propose a novel time of arrival (TOA)-based localization scheme. We regard the line-of-sight (LOS) environment and non-line-of-sight environment in wireless positioning as a Markov process with two interactive models. In the NLOS model, we propose a modified probabilistic data association (MPDA) algorithm to reduce the NLOS errors in position estimation. After the NLOS recognition, if the number of correct positions is zero continuously, it will lead to inaccurate localization. In this paper, the NLOS tracer method is proposed to solve this problem to improve the robustness of the probabilistic data association algorithm. The simulation and experimental results show that the proposed algorithm can mitigate the influence of NLOS errors and achieve a higher localization accuracy when compared with the existing methods.
Highlights
Due to there being many obstacles, it is difficult to provide accurate localization indoors
We investigated how the positioning accuracy of the extended Kalman filter (EKF), interacting multiple model (IMM)-EKF, modified probabilistic data association (MPDA), and the proposed algorithm varied with the number of beacon nodes when the NLOS errors were exponential distribution (Figure 6a)
We explored the influence of different NLOS errors probabilities on the positioning results of the EKF, IMM-EKF, MPDA, and the proposed algorithm when NLOS errors were given as an exponential distribution
Summary
Due to there being many obstacles, it is difficult to provide accurate localization indoors. Based on the interrelation of multiple source nodes, the novel extended Kalman filter (EKF) integrated with semidefinite programming method is used for localization It solves the problem of the cooperative localization using multiple source nodes, and improves the localization performance compared with the classic EKF. Large outliers the can beon identified using distribution a generalizedoflikelihood ratioprobabilities test based onofthe distribution sometimes occur in the range measurements These outliers can seriously interfere with the of the different probabilities of the error. The improved recognition, the position estimation with the error is discarded, and the correct position probabilistic data association filter is used in the NLOS model. The proposed algorithm in this paper has the following corresponding correlation probability to obtain the final position estimation to reduce the NLOS error.
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