Uncooperative Emitter Localization Using Signal Strength in Uncalibrated Mobile Networks

This paper explores the problem of localizing an emitter of radio frequency energy using a network of mobile receiver nodes, each taking measurements of the emitter's received signal strength (RSS). In this paper, we drop the assumption of receiver calibration, leading to biased measurements for each node. We model these bias effects as additive random variables for each receiver in the log-distance path loss model. We propose two novel estimators to handle these effects as modifications of the nonlinear least squares and the Gaussian particle filter algorithms. The estimators are augmented using the principle of variance least squares, in which the biases' effect on the data covariance is estimated online. These estimates inform subsequent iterations of the nonlinear algorithms. The path loss exponent and emitter power offset are likewise treated as unknowns. Our simulations show the performance improvement evident in various scenarios over the naive approaches, other contemporary algorithms, and with respect to the Cramer-Rao lower bound. We further show the efficacy of our approach through several experiments using real RSS data collected from a mobile network.