Non-Line-of-Sight Identification and Mitigation Using Received Signal Strength

Indoor wireless systems often operate under non-line-of-sight (NLOS) conditions that can cause ranging errors for location-based applications. As such, these applications could benefit greatly from NLOS identification and mitigation techniques. These techniques have been primarily investigated for ultra-wide band (UWB) systems, but little attention has been paid to WiFi systems, which are far more prevalent in practice. In this study, we address the NLOS identification and mitigation problems using multiple received signal strength (RSS) measurements from WiFi signals. Key to our approach is exploiting several statistical features of the RSS time series, which are shown to be particularly effective. We develop and compare two algorithms based on machine learning and a third based on hypothesis testing to separate LOS/NLOS measurements. Extensive experiments in various indoor environments show that our techniques can distinguish between LOS/NLOS conditions with an accuracy of around 95%. Furthermore, the presented techniques improve distance estimation accuracy by 60% as compared to state-of-the-art NLOS mitigation techniques. Finally, improvements in distance estimation accuracy of 50% are achieved even without environment-specific training data, demonstrating the practicality of our approach to real world implementations.

I. Introduction

IN LIGHT of numerous emerging location-aware wireless applications like cooperative communications [1] and mobile queries [2], various localization approaches based on WiFi [3]– [5], UWB [6]– [8], ultrasound [9], etc. have been proposed. UWB and ultrasound-based techniques can provide high localization accuracy but require expensive and specialized infrastructure. In comparison, WiFi-based approaches are a better choice in terms of the cost because they make use of existing WiFi infrastructure including access points, mobile phones, laptops, etc. Therefore, WiFi-based approaches are regarded as the most convenient localization method in indoor environments. Example applications include finding victims in emergencies [10], equipment tracking in hospitals [11], and location-based commerce [12].