Optimal sensor placement for RSS-based localization using Gaussian process

This paper studies optimal sensor placement for received signal strength(RSS)-based localization. We employ a Gaussian process (GP) to estimate one target position against highly nonlinear and noisy RSS. The estimation performance is then characterized by the Cramer-Rao lower bound that is used for the sensor placement by minimizing the lower bound. We analyze the optimality of the relative sensor-target geometry in terms of distances and angles between sensors and single target. Finally, some simulation illustrate how the proposed placement improves the localization performance from an accurate and a precise estimation perspectives.