Autonomous Search for an RF Emitter in Unknown Indoor Environments: An Algorithm With Experimental Validation

The autonomous search for an unknown radio-frequency (RF) emitter in an unmapped indoor environment is a fundamental capability of intelligent robotic systems, enabling applications such as search and rescue, surveillance, environmental monitoring, biological studies, and industrial automation. To address this challenge, this paper proposes an theoretical framework for autonomous RF emitter localization and search in structured yet previously unmapped indoor environments. The searching agent fuses the non-directional received signal strength (RSS) measurements to estimate the RF emitter position and signal strength, while concurrently mapping the environment and localizing itself using LiDAR measurements. The task of simultaneous emitter localization, mapping and agent self-localization are formulated within the sequential Bayesian framework. The main technical challenge lies in the unknown propagation path losses affecting the RSS measurements, which is addressed using an imprecise likelihood model based on the interval valued path loss exponent (PLE). The sequential Bayesian estimation is implemented using a Rao-Blackwellized Particle Filter (RBPF) for simultaneous localization and mapping (SLAM) and, conditional on its result, a separate Monte Carlo estimator of the RF emitter parameter vector. Path planning for search motion control is based on an entropy-reduction reward in the RF emitter parameter space, its performance is assessed in simulation against two heuristic search strategies. A physical robotic platform was developed to experimentally validate the theoretical framework. Extensive simulations and real-world experiments demonstrate the effectiveness of the proposed search algorithm.