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

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.