Probabilistic Indoor Tracking of Mobile Wireless Nodes Relative to Landmarks

Abstract

The profile-based approach is known to be advantageous when it comes to inferring positions of mobile wireless devices in complex indoor environments. The past decade has seen a significant body of work that explores different implementations of this approach, with varying degrees of success. Here, we cast the profile-based approach in a probabilistic framework. Launching from the theoretical basis that this framework provides, we provide a suite of carefully designed methods that make use of sophisticated computations in pursuit of high localization accuracy with low hardware investment and moderate set-up cost. More specifically, we use full distributional information on signal measurements at a set of discrete locations, termed landmarks. Positioning of a mobile node is done relative to the resulting landmark graph and the node can be found near a landmark or in the area between two landmarks. Key elements of our approach include profiling the signal measurement distributions over the coverage area using a special interpolation technique; a two-tier statistical positioning scheme that improves efficiency by adding movement detection; and joint clusterhead placement optimization for both localization and movement detection. The proposed system is practical and has been implemented using standard wireless sensor network hardware. Experimentally, our system achieved an accuracy equivalent to less than $5$ m with a $95\,\%$ success probability and less than $3$ m with an $87\,\%$ success probability. This performance is superior to well-known contemporary systems that use similar low-cost hardware.

Research partially supported by the NSF under grants EFRI-0735974, CNS-1239021, and IIS-1237022, by the ARO under grants W911NF-11-1-0227 and W911NF-12-1-0390, and by the ONR under grant N00014-10-1-0952.