Heuristic techniques for maximum likelihood localization of radioactive sources via a sensor network

Abstract Maximum likelihood estimation (MLE) is an effective method for localizing radioactive sources in a given area. However, it requires exhaustive search parameter estimation, which time-consuming. In this study, heuristic techniques were employed to radiation source parameters that provide the maximum by using network of sensors. Hence, time consumption MLE would be effectively reduced. First, was detected k -sigma method. Subsequently, applied readings and positions detectors have source. A comparative study performed accuracy evaluated traditional methods techniques. The via grid fixed multiple resolutions. Additionally, four commonly used algorithms applied: firefly algorithm (FFA), particle swarm optimization (PSO), ant colony (ACO), artificial bee (ABC). experiment conducted real data collected Low Scatter Irradiator facility at Savannah River National Laboratory as part Intelligent Radiation Sensing System program. showed 3.27 s resolution 0.59 multi-resolution MLE. heuristic-based 0.75, 0.03, 0.02, 0.059 FFA, PSO, ACO, ABC, respectively. location error approximately 0.4 m either search-based or can comparable through less time-consuming process than