Robotics-Based Location Sensing Using Wireless Ethernet

Abstract

A key subproblem in the construction of location-aware systems is the determination of the position of a mobile device. This article describes the design, implementation and analysis of a system for determining position inside a building from measured RF signal strengths of packets on an IEEE 802.11b wireless Ethernet network. Previous approaches to location-awareness with RF signals have been severely hampered by non-Gaussian signals, noise, and complex correlations due to multi-path effects, interference and absorption. The design of our system begins with the observation that determining position from complex, noisy and non-Gaussian signals is a wellstudied problem in the field of robotics. Using only off-the-shelf hardware, we achieve robust position estimation to within a meter in our experimental context and after adequate training of our system. We can also coarsely determine our orientation and can track our position as we move. Our results show that we can localize a stationary device to within 1.5 meters over 80% of the time and track a moving device to within 1 meter over 50% of the time. Both localization and tracking run in real-time. By applying recent advances in probabilistic inference of position and sensor fusion from noisy signals, we show that the RF emissions from base stations as measured by off-the-shelf wireless Ethernet cards are sufficiently rich in information to permit a mobile device to reliably track its location.

Accurate GSM Indoor Localization

“…Unfortunately, GPS does not work well indoors, in urban canyons, or in similar areas with limited view of the sky. Instead, most research on indoor localization systems has been based on the use of short-range signals, such as 802.11 [3,7,13], Bluetooth [1], ultra sound [17], or infrared [18]. This paper shows that contrary to popular belief, an indoor localization system based on wide-area GSM fingerprints can achieve high accuracy, and is in fact comparable to an 802.11-based implementation.…”

“…This was the first fingerprinting system that showed that it is possible to localize a laptop in the hallways of a small office building within 2-3 m of its true location, using fingerprints from four 802.11 access points. There have been improvements to Radar's fingerprint matching algorithm that have improved accuracy [2,13,19] and were able to differentiate between floors of a building with a high degree of precision [8]. In addition, commercial localization products have been built using 802.11 fingerprinting [20].…”

Efficient self-adjusting, similarity-based location fingerprinting with passive UHF RFID

“…Bahl et al [2] developed the in-building system RADAR, in which WLAN signal strength measurements were used for combined localization via fingerprinting and signal propagation modeling. Ladd et al [3] extended RADAR on a robotic platform by Markov localization. Li et al [4] compared WKNN and Markov localization based on WLAN signal strength measurements.…”

DECODE : Detecting co-moving wireless devices

“…[8] uses Bayesian learning algorithm on RF fingerprints observed at three or more receivers to obtain a median 802.11 localization accuracy of 3-4 meters. The most accurate 802.11 location system to date is [12] which uses Hidden Markov Model and Bayesian inference derived from observations at nine different receivers yielding a median accuracy of one meter. Further, the average localization accuracy employing RSS in a 802.15.4 (Zigbee) network [4] and an active RFID system [3] is about the same with median errors around 3-4m when using four receivers.…”