Monte Carlo sampling based in-home location tracking with minimal RF infrastructure requirements

Abstract

The paper describes research towards a system for locating users in a home environment requiring only a minimal wireless infrastructure. The only sensor reading used for the location estimation is the radiofrequency received signal strength indication (RSSI) measured by an RF interface (e.g., Wi-Fi). Location estimates are computed using Bayesian filtering on sample sets derived by Monte Carlo sampling. Wireless signal strength maps for the filter are obtained by a two-step parametric and measurement driven ray-tracing approach to account for absorption and reflection characteristics of various obstacles. Our trace driven simulations indicate that RSSI readings from a single access point in an indoor environment are sufficient to derive good location estimates of users.