Non-parametric location estimation in rough wireless environments for wireless sensor network

• The NLOS detection is introduced to identify the propagation condition of the beacon node.
• For the NLOS measurements, GMD algorithm is proposed to estimate the mean and variance of the measurements.
• The multiple modes combination method is proposed to mitigate the NLOS error.
• The proposed algorithm does not assume any statistical knowledge of the NLOS error.
• The proposed localization algorithm uses the TOA measurements but can easily extend to other signal features such as TDOA and RSS.

With the development of microelectronics, wireless communication and micro-electro-mechanical systems technologies, wireless sensor network (WSN) has received considerable attention. The location information is critical for application of WSN. The high accuracy localization result can be achieved when the propagation environment is line of sight. However, it is decreased extremely in non-line of sight (NLOS) environment. NLOS propagation which affects the accuracy of mobile localization is one of the most important challenges for WSN. In this paper, we propose a method to alleviate the influence of the NLOS error using the improved Kalman filter based on Gaussian mixture distributions (IKF-GMD). The NLOS detection is firstly introduced to identify the propagation condition of the beacon node. For the NLOS measurements, GMD algorithm is proposed to estimate the mean and variance of the measurements and multiple modes combination method is proposed to mitigate the NLOS error. The IKF-GMD approach can effectively mitigate the NLOS error and does not assume any statistical knowledge of the NLOS error. Simulation and experiment results demonstrate that the performance of the proposed IKF-GMD algorithm outperforms the Kalman filter and robust Kalman filter methods.