Localization of a radioactive source with the predictive range-whale optimization algorithm method
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Heuristic algorithms can effectively improve the accuracy and efficiency of estimating the status of radioactive sources based on the maximum likelihood estimation method. However, heuristic algorithms often converge prematurely at local optima, compromising stability and making them unsuitable for prolonged monitoring tasks. To address this problem, the Predictive Range-Whale Optimization Algorithm (PR-WOA) method was proposed in this paper. Initially, the predictive location and intensity ranges of the radioactive source were determined by integrating historical prediction results. Subsequently, the initial population was more concentrated within the predictive range to improve the algorithm’s capability for local optimization. Finally, an inertia weight was introduced to adaptively adjust the search step, consequently improving its global searching capability and efficiency. The performance of the PR-WOA method was evaluated with the simulation and experimental data. Comparative studies demonstrated that the proposed method significantly improves accuracy and stability in predicting the status of the radioactive source. In the 2022 radioactive source localization experiment in Hangzhou, PR-WOA method achieved an average localization accuracy of 1.30m during the trajectory tracking of a moving radioactive source mounted on the drone. Compared to traditional heuristic algorithms, this method improved localization accuracy by 17.9 % and enhanced localization stability during long-term monitoring by 19.0 %.
Abstract
Heuristic algorithms can effectively improve the accuracy and efficiency of estimating the status of radioactive sources based on the maximum likelihood estimation method. However, heuristic algorithms often converge prematurely at local optima, compromising stability and making them unsuitable for prolonged monitoring tasks. To address this problem, the Predictive Range-Whale Optimization Algorithm (PR-WOA) method was proposed in this paper. Initially, the predictive location and intensity ranges of the radioactive source were determined by integrating historical prediction results. Subsequently, the initial population was more concentrated within the predictive range to improve the algorithm’s capability for local optimization. Finally, an inertia weight was introduced to adaptively adjust the search step, consequently improving its global searching capability and efficiency. The performance of the PR-WOA method was evaluated with the simulation and experimental data. Comparative studies demonstrated that the proposed method significantly improves accuracy and stability in predicting the status of the radioactive source. In the 2022 radioactive source localization experiment in Hangzhou, PR-WOA method achieved an average localization accuracy of 1.30m during the trajectory tracking of a moving radioactive source mounted on the drone. Compared to traditional heuristic algorithms, this method improved localization accuracy by 17.9 % and enhanced localization stability during long-term monitoring by 19.0 %.