We describe and evaluate two methods for device pose classification and walking speed estimation that generalize well to new users, compared to previous work. These machine learning based methods are designed for the general case of a person holding a mobile device in an unknown location and require only a single low-cost, low-power sensor: a triaxial accelerometer. We evaluate our methods in straight-path indoor walking experiments as well as in natural indoor walking settings. Experiments with 14 human participants to test user generalization show that our pose classifier correctly selects among four device poses with 94% accuracy compared to 82% for previous work, and our walking speed estimates are within 12-15% (straight/indoor walk) of ground truth compared to 17-22% for previous work. Implementation on a mobile phone demonstrates that both methods can run efficiently online.

Many indoor localization methods are based on the association of 802.11 wireless RF signals from wireless access points (WAPs) with location labels. An “organic” RF positioning system relies on regular users, not dedicated surveyors, to build the map of RF fingerprints to location labels. However, signal variation due to device heterogeneity may degrade localization performance. We analyze the diversity of those signal characteristics pertinent to indoor localization - signal strength and AP detection - as measured by a variety of 802.11 devices. We first analyze signal strength diversity, and show that pairwise linear transformation alone does not solve the problem. We propose kernel estimation with a wide kernel width to reduce the difference in probability estimates. We also investigate diversity in access point detection. We demonstrate that localization performance may degrade significantly when AP detection rate is used as a feature for localization, and correlate the loss of performance to a device dissimilarity measure captured by Kullback-Leibler divergence. Based on this analysis, we show that using only signal strength, without incorporating negative evidence, achieves good localization performance when devices are heterogeneous.

We describe the design, implementation, and evaluation of Molé, a mobile organic localization engine. Unlike previous work on crowd-sourced WiFi positioning, Mole uses a hierarchical name space. By not relying on a map and by being more strict than uninterpreted names for places, Molé aims for a more flexible and scalable point in the design space of localization systems. Molé employs several new techniques, including a new statistical positioning algorithm to differentiate between neighboring places, a motion detector to reduce update lag, and a scalable “cloud”-based fingerprint distribution system. Molé's localization algorithm, called Maximum Overlap (MAO), accounts for temporal variations in a place's fingerprint in a principled manner. It also allows for aggregation of fingerprints from many users and is compact enough for on-device storage. We show through end-to-end experiments in two deployments that MAO is significantly more accurate than state-of-the-art Bayesian-based localizers. We also show that non-experts can use Molé to quickly survey a building, enabling room-grained location-based services for themselves and others.

Most current methods for 802.11-based indoor localization depend on surveys conducted by experts or skilled technicians. Some recent systems have incorporated surveying by users. Structuring localization systems "organically," however, introduces its own set of challenges: conveying uncertainty, determining when user input is actually required, and discounting erroneous and stale data. Through deployment of an organic location system in our nine-story building, which contains nearly 1,400 distinct spaces, we evaluate new algorithms for addressing these challenges. We describe the use of Voronoi regions for conveying uncertainty and reasoning about gaps in coverage, and a clustering method for identifying potentially erroneous user data. Our algorithms facilitate rapid coverage while maintaining positioning accuracy comparable to that achievable with survey-driven indoor deployments.

The moving-baseline localization (MBL) problem arises when a group of nodes moves through an environment in which no external coordinate reference is available. When group members cannot see or hear one another directly, each node must employ local sensing and inter-device communication to infer the spatial relationship and motion of all other nodes with respect to itself. We consider a setting in which nodes move with piecewise-linear velocities in the plane, and any node can exchange noisy range estimates with certain sufficiently nearby nodes. We develop a distributed solution to the MBL problem in the plane, in which each node performs robust hyperbola fitting, trilateration with velocity constraints, and subgraph alignment to arrive at a globally consistent view of the network expressed in its own "rest frame." Changes in any node's motion cause deviations between observed and predicted ranges at nearby nodes, triggering revision of the trajectory estimates computed by all nodes. We implement and analyze our algorithm in a simulation informed by the characteristics of a commercially available ultra-wideband (UWB) radio, and show that recovering node trajectories, rather than just locations, requires substantially less computation at each node. Finally, we quantify the minimum ranging rate and local network density required for the method's successful operation.