NLOS identification and mitigation based on CIR with particle filter

As key factors to guarantee accurate localization for ultra-wide band system (UWB), Non-line-of-sight (NLOS) identification and mitigation attract lots of attentions. One of the most effective methods for NLOS detection is based on the different characters of channel impulse response (CIR) under Line-of-sight (LOS) and NLOS condition. Features (such as kurtosis, standard deviation, energy, etc.) extracted from CIR are used for classification with the help of machine learning algorithm. Different from existing approaches, the NLOS and LOS probability density functions (PDF) of the correlation coefficient are calculated with the training data. The probability that the CIR is measured under LOS or NLOS is determined based on the PDF. A weighted particle filter is proposed to reduce the localization error, caused by NLOS. The weights for the proposed approach are obtained with the help of the measured variance of the LOS error and the NLOS/LOS probability. The weighted least squares (WLS) and standard particle filter are used as comparison. The real field test results show that the proposed weighted particle filter has better accuracy compared to the standard particle filter and the WLS.