Robust Received Signal Strength Indicator (RSSI)-Based Multitarget Localization via Gaussian Process Regression

Abstract

We consider the robust localization, via Gaussian process regression (GPR), of multiple transmitters/targets based on received signal strength indicator (RSSI) data collected by fixed sensors distributed in the environment. For such a scenario and approach, we contribute both with a novel noise robust procedure to train the parameters of the GPR model, which is achieved via a mini-batch stochastic gradient descent (SGD) scheme with gradients given in closed form, and with a pair of corresponding robust marginalization procedures for the estimation of target locations. Simulation results validate the contributions by showing that the proposed methods significantly outperform the best related state-of-the-art (SotA) alternative and approach the performance of a genie-aided (GA) scheme.