A Wireless Sensor Networks Positioning Method in NLOS Environment Based on TOA and Parallel Kalman Filter

A As an information acquisition and processing technology, wireless sensor networks (WSN) have broad application prospects in the fields of target tracking, condition monitoring and positioning. At the same time, these monitoring information needs to be accompanied by location information to play its role, otherwise it will lose the meaning of the collection. At present, most positioning algorithms can achieve higher positioning accuracy in the line-of-sight (LOS) environment. However, in an indoor non-line-of-sight (NLOS) environment, the ideal positioning accuracy cannot be achieved due to the obstruction of obstacles. Therefore, how to reduce the positioning error in the line-of-sight and non-line-of-sight environments is particularly important. A novel TOA-based parallel Kalman filter is proposed in this paper to solve the problem of node localization under line-of-sight and non-line-of-sight conditions. In the proposed algorithm, we calculate the probabilities in the LOS/NLOS states firstly, and then combine the state probabilities with the corresponding parallel Kalman filtering results to obtain the estimation results. By comparing with the original data and a single LOS Kalman filter algorithm, the algorithm can drastically reduce the positioning error caused by the NLOS effect, and greatly improve the positioning accuracy in the LOS/NLOS environments.