DOA-based localization algorithms under NLOS conditions

Abstract

Localization schemes based on direction of arrival (DOA) in none-line-of-sight (NLOS) environments are developed. The proposed kernel-based machine learning method is innovative and can provide accurate position estimation under none-line-of sight (NLOS) conditions. The proposed kernel-based method is compared with the Weighted K-nearest neighborhood (WKNN) fingerprinting method using simulated DOA data in practical rural environment. It shows that the kernel-based method gives more accurate localization results.