Fast prototyping of an SDR WLAN 802.11b receiver for an indoor positioning system

TL;DR

This paper presents a rapid prototyping SDR WLAN 802.11b receiver for indoor positioning, enhancing fingerprint-based IPS with channel estimates to improve accuracy in low-AP scenarios, using LabVIEW and SVM classification.

Contribution

It introduces a cost-effective SDR-based WLAN beacon receiver capable of real-time RSS and channel estimate measurements for improved indoor positioning accuracy.

Findings

Achieved 9 packets/sec measurement rate per AP.

Enhanced fingerprinting accuracy with channel estimates.

Validated system performance in cluttered indoor environments.

Abstract

Indoor positioning systems (IPS) are emerging technologies due to an increasing popularity and demand in location based service (LBS). Because traditional positioning systems such as GPS are limited to outdoor applications, many IPS have been proposed in literature. WLAN-based IPS are the most promising due to its proven accuracy and infrastructure deployment. Several WLAN-based IPS have been proposed in the past, from which the best results have been shown by so-called fingerprint-based systems. This paper proposes an indoor positioning system which extends traditional WLAN fingerprinting by using received signal strength (RSS) measurements along with channel estimates as an effort to improve classification accuracy for scenarios with a low number of Access Points (APs). The channel estimates aim to characterize complex indoor environments making it a unique signature for fingerprinting-based IPS and therefore improving pattern recognition in radio-maps. Since commercial WLAN cards offer limited measurement information, software-defined radio (SDR) as an emerging trend for fast prototyping and research integration is chosen as the best cost-effective option to extract channel estimates. Therefore, this paper first proposes an 802.11b WLAN SDR beacon receiver capable of measuring RSS and channel estimates. The SDR is designed using LabVIEW (LV) environment and leverages several inherent platform acceleration features that achieve real-time capturing. The receiver achieves a fast-rate measurement capture of 9 packets per second per AP. The classification of the propose IPS uses a support vector machine (SVM) for offline training and online navigation. Several tests are conducted in a cluttered indoor environment with a single AP in 802.11b legacy mode. Finally, navigation accuracy results are discussed.