Localizing unknown radiation sources by unscented particle filtering based on divide-and-conquer sampling
Yizhou Liu, Yike Xuan, Zhang De, Shuliang Zou
Abstract
Predictive estimation of radioactive sources using Bayesian theory is of great importance in nuclear emergency response. To this aim, we put forward an unscented particle filtering algorithm based on divide-and-conquer sampling. Our method exploits the information acquired by mobile detection robots to search for radioactive sources at unknown locations. Firstly, we assume a circular and extremely thin NaI (TI) scintillator detector, then analyze the geometric relationship between the detector cross-sectional area and the square of the radiation source distance, and finally establish a radiation source search model in accordance with Bayesian method. We also introduce a divide-and-conquer strategy to solve the problems of particle dilution and particle degradation, so as to achieve higher search accuracy and speed. Through simulation experiments, compared with standard particle filter and unscented particle filter, the algorithm can effectively improve particle diversity and successfully search radioactive sources in a shorter time.