Passive Localization

Passive localization focuses on determining the location of an object or emitter without requiring it to actively transmit signals, relying instead on received signals from other sources. Current research emphasizes improving accuracy and efficiency through diverse approaches, including geometric methods that leverage sensor geometry, particle filtering enhanced by reinforcement learning for data fusion, and Monte Carlo techniques for efficient global localization. These advancements are crucial for applications ranging from indoor tracking and robotics navigation to large-scale multi-robot systems operating in GPS-denied environments.