Online Indoor Localization Using DOA of Wireless Signals

TL;DR

This paper introduces a novel particle filter-based method for indoor wireless localization using DOA estimates, achieving high accuracy and efficiency without requiring offline fingerprinting.

Contribution

It presents a new online localization approach leveraging DOA and a particle filter, outperforming traditional fingerprinting methods in accuracy and computational speed.

Findings

High meter-level localization accuracy achieved

Method is computationally efficient for online use

Outperforms standard state-of-the-art approaches

Abstract

Localization of a wireless mobile device or a robot in indoor and GPS-denied environments is a difficult problem, particularly in dynamic scenarios where traditional cameras and LIDAR-based alternative sensing and localization modalities may fail. We propose a method for estimating the location of a mobile robot in relation to static wireless sensor nodes (WSN) deployed in the environment. The method employs a novel particle filter that updates its weights using a Gauss probability over Direction of Arrival (DOA) estimate in conjunction with the mobile robot's mobility model. We evaluate and validate the proposed method in terms of accuracy and computational efficiency through extensive simulations and public real-world measurement datasets, comparing with standard state-of-the-art localization approaches. The results show considerably high meter-level localization accuracy balanced by the high computational efficiency, enabling it to use online without a need for a dedicated offline phase as in typical fingerprint-based localization algorithms.