High-accuracy NLOS identification based on random forest and high-precision positioning on 60 GHz millimeter wave

Abstract

60 GHz millimeter wave (mmWave) system provides extremely high time resolution and multipath components (MPC) separation and has great potential to achieve high precision in the indoor positioning. However, the ranging data is often contaminated by non-line-of-sight (NLOS) transmission. First, six features of 60GHz mmWave signal under LOS and NLOS conditions are evaluated. Next, a classifier constructed by random forest (RF) algorithm is used to identify line-of-sight (LOS) or NLOS channel. The identification mechanism has excellent generalization performance and the classification accuracy is over 97%. Finally, based on the identification results, a residual weighted least squares positioning method is proposed. All ranging information including that under NLOS channels is fully utilized, positioning failure caused by insufficient LOS links can be avoided. Compared with the conventional least squares approach, the positioning error of the proposed algorithm is reduced by 49%.