Detection of direct path component absence in NLOS UWB channel

TL;DR

This paper introduces a novel SVM-based method for identifying NLOS conditions in UWB channels by detecting the presence or absence of the direct path component, improving accuracy over existing techniques.

Contribution

It presents a new NLOS identification approach that effectively distinguishes blocked direct paths using signal features and machine learning, with experimental validation.

Findings

The method accurately detects NLOS conditions in UWB channels.

Support Vector Machine effectively classifies line-of-sight vs. NLOS scenarios.

Experimental results demonstrate improved detection performance.

Abstract

In this paper a novel NLOS (Non-Line-of-Sight) identification technique is proposed. In comparison to other methods described in the literature, it discerns a situation when the delayed direct path component is available from when it's totally blocked and introduced biases are much higher and harder to mitigate. In the method, NLOS identification is performed using Support Vector Machine (SVM) algorithm based on various signal features. The paper includes description of the method and the results of performed experiment.