Accuracy characterization for metropolitan-scale Wi-Fi localization

Abstract

Location systems have long been identified as an important component of emerging mobile applications. Most research on location systems has focused on precise location in indoor environments. However, many location applications (for example, location-aware web search) become interesting only when the underlying location system is available ubiquitously and is not limited to a single office environment. Unfortunately, the installation and calibration overhead involved for most of the existing research systems is too prohibitive to imagine deploying them across, say, an entire city. In this work, we evaluate the feasibility of building a wide-area 802.11 Wi-Fi-based positioning system. We compare a suite of wireless-radio-based positioning algorithms to understand how they can be adapted for such ubiquitous deployment with minimal calibration. In particular, we study the impact of this limited calibration on the accuracy of the positioning algorithms. Our experiments show that we can estimate a user's position with a median positioning error of 13-40 meters (depending upon the characteristics of the environment). Although this accuracy is lower than existing positioning systems, it requires substantially lower calibration overhead and provides easy deployment and coverage across large metropolitan areas.

RGB-W: When Vision Meets Wireless

“…In [28], Miyaki et al used a particle filter approach to perform outdoor tracking using a distributed camera setup and a RSS centroid approach similar to [8]. Although Miyaki et al were successful at outdoor tracking, the use of a GPS to collect the ground truth and error of up to 18 meters makes their approach unfeasible for indoor environments.…”

Improving cooperative trajectory mapping applications with encounter-based error correction

“…The first approach is to use filtering techniques, such as the Kalman filter [17], [18] and particle filters [19], [20], to limit the effects of the measurement noise. The second approach is to apply noise cancellation methods using GPS, assisted GPS, or Wi-Fi [7], [8], [21], [22]. A key difference in our approach is that our technique is more flexible since we emphasize the related locations of each user rather than the physical locations.…”

“…This data is then transmitted to a central depository via a 3G or 4G connection, which, in turn, processes the data from multiple users to construct a trajectory map. This type of map can be used in various applications, such as traffic monitoring [3], public transportation tracking [4]- [6], and people localization [2], [7], [8].…”

Encounter-based noise cancelation for cooperative trajectory mapping

“…This data is then transmitted to a central depository via a 3G or 4G connection, which, in turn, processes the data from multiple users to create a trajectory map. This type of map can be used in various applications, such as traffic monitoring [3], public transportation tracking [4], [5], and people localization [2], [6], [7].…”