Localization in sensor networks based on log range observations

This contribution presents a unified framework for localization and tracking in sensor networks based on fusing a variety of signal energy measurements as provided by for instance acoustic, seismic, magnetic, radio, microwave and infrared sensors. The received energy from such sensors generally decays exponentially, and a log range model is introduced for the sensor observations in logarithmic scale, which is linear in transmitted power and the path loss exponent. Field trial sensor data confirms the validity of the log range model. The novelty in this contribution lies in a systematic least squares approach to eliminate these nuisance parameters and also the sensor noise variances. Details on how to solve the resulting low-dimensional non-linear least squares criterion are given, and how to extend the algorithms to target tracking. Explicit formulas for the Cramer-Rao lower bound are given for both localization and tracking.