Joint Noise Model Learning and Source Localization With Unknown Transmit Power

Instead of assuming the accurate noise model and transmit power, we address the received signal strength (RSS) localization problem under a more practical assumption that the exact noise model and transmit power are unavailable. In this letter, we propose to use a universal mixture of Gaussian (MoG) distribution to model the unknown noise due to the fact that MoG distribution can adaptively fit any distribution by adjusting the involved parameters. We propose a novel variational inference framework to achieve joint automatical noise model learning and target localization with unknown transmit power. Numerical experiments are conducted to prove the robustness and effectiveness of the proposed algorithm.