Multi-source term estimation based on parallel particle filtering and dynamic state space in unknown radiation environments

• Published in: Building and environment Vol. 236; p. 110281
• Main Authors: Bai, Hua, Du, Zhijiang, Zhu, Hongbiao, Ding, Pengchao, Wang, Gongcheng, Wang, Han, Xu, Wenda, Wang, Weidong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.05.2023
• Subjects: Bayesian inference; Indoor radiation environment; Multi-source term estimation; Poisson kriging interpolation; State space construction; Indoor radiation environment; Multi-source term estimation; Bayesian inference; State space construction; Poisson kriging interpolation
• ISSN: 0360-1323; 1873-684X
• DOI: 10.1016/j.buildenv.2023.110281

When hazardous sources threaten the environment, source term estimation is a crucial concern. Robotics provides a secure solution to the issue but still encounters challenges in uncertain environments. Rapidly inferring source parameters is a prerequisite for unmanned source search. However, when the number of sources exceeds one, the sensor can only measure the intensity of the coupled field. We propose a novel online multi-radiation source term estimation framework for autonomous source-searching tasks. The method incrementally constructs the state space of Bayesian estimation to adapt to diverse scenarios. Inspired by multi-sensor fusion and particle filtering, this framework employs multi-layer particles and updates them simultaneously to enhance the efficiency of fitting observations. Subsequently, the approach corrects (eliminate and randomly generate) the particles based on Poisson kriging interpolation to prevent particles in redundant layers from interfering with estimation. Furthermore, we compare the performances of our proposed algorithm with those in simulated and real experiments. Various metrics demonstrate that the suggested framework is accurate and robust. •A framework is proposed for multi-source term estimation in unknown environments.•A dynamic state space is constructed to refine source term estimation.•Parallel particle filtering is proposed to update multi-layer particle weights.•The Poisson kriging interpolation is leveraged to verify the pseudo sources.•The effectiveness of this framework is validated by simulations and real experiments.