Multi Source Localization

Multi-source localization aims to pinpoint the locations of multiple sources emitting signals, a crucial task with applications ranging from environmental monitoring to robotics. Current research emphasizes robust methods that handle challenges like limited data, signal interference, and data association ambiguities, employing techniques such as Bayesian optimization, optimal transport formulations, and permutation-invariant recurrent neural networks. These advancements improve accuracy and efficiency, particularly in scenarios with noisy or incomplete measurements, leading to more reliable localization systems for diverse applications.