NLOS Ranging Mitigation with Neural Network Model for UWB Localization

TL;DR

This paper introduces a neural network-based method to identify LOS/NLOS conditions in UWB measurements, significantly enhancing indoor localization accuracy without requiring channel information.

Contribution

It presents a novel neural network approach to classify LOS/NLOS in low-cost UWB modules, improving localization accuracy in NLOS environments.

Findings

Localization accuracy improved by 16.93% in lobby tests.

Localization accuracy improved by 27.97% in corridor tests.

Neural network effectively distinguishes LOS from NLOS measurements.

Abstract

Localization of robots is vital for navigation and path planning, such as in cases where a map of the environment is needed. Ultra-Wideband (UWB) for indoor location systems has been gaining popularity over the years with the introduction of low-cost UWB modules providing centimetre-level accuracy. However, in the presence of obstacles in the environment, Non-Line-Of-Sight (NLOS) measurements from the UWB will produce inaccurate results. As low-cost UWB devices do not provide channel information, we propose an approach to decide if a measurement is within Line-Of-Sight (LOS) or not by using some signal strength information provided by low-cost UWB modules through a Neural Network (NN) model. The result of this model is the probability of a ranging measurement being LOS which was used for localization through the Weighted-Least-Square (WLS) method. Our approach improves localization accuracy by 16.93% on the lobby testing data and 27.97% on the corridor testing data using the NN model trained with all extracted inputs from the office training data.