Push the limit of WiFi based localization for smartphones

Abstract

Highly accurate indoor localization of smartphones is critical to enable novel location based features for users and businesses. In this paper, we first conduct an empirical investigation of the suitability of WiFi localization for this purpose. We find that although reasonable accuracy can be achieved, significant errors (e.g., 6 ∼ 8m) always exist. The root cause is the existence of distinct locations with similar signatures, which is a fundamental limit of pure WiFibased methods. Inspired by high densities of smartphones in public spaces, we propose a peer assisted localization approach to eliminate such large errors. It obtains accurate acoustic ranging estimates among peer phones, then maps their locations jointly against WiFi signature map subjecting to ranging constraints. We devise techniques for fast acoustic ranging among multiple phones and build a prototype. Experiments show that it can reduce the maximum and 80-percentile errors to as small as 2m and 1m, in time no longer than the original WiFi scanning, with negligible impact on battery lifetime.

RGB-W: When Vision Meets Wireless

“…We evaluate the performance of W based method such as trilateration [27] and fingerprinting to get more insight on the localization error of the W data (without using our proposed ring images). Both methods perform poorly.…”

“…Signal trilateration and propagation models have been well documented and are able to estimate position within 2 meters [30,27]. To estimate distance from each antenna, a variety of models can be employed: Gaussian models [14], Monte Carlo [6], Bayesian [25], Hidden Markov Models [23], and radio propagation models [3] have been presented over the years.…”

Montage: Combine frames with movement continuity for realtime multi-user tracking

“…[19] presents a solution for achieving high speed 3D continuous pair-wise localization using two microphones and one speaker on the phone. Liu et al [13] uses acoustic ranging estimates among peer phones as constraints to reduce the significant errors of WiFi-based method. Centaur [15] fuses RF and acoustic ranging based localization techniques into a single systematic framework based on Bayesian inference.…”

Identification and Prediction of Large Pedestrian Flow in Urban Areas Based on a Hybrid Detection Approach

“…Indoor localization systems based on the approach have been developed with different flavors. Embedded sensors of mobile devices are exploited to improve accuracy of the location estimation [13,14], where crowdsourcing paradigm is introduced to reduce the cost of the site survey in the training phase [15]. Machine learning algorithms are also leveraged to shorten the delay of the localization process [16][17][18].…”