Modeling and Simulation for CBRN Hazard Source Item Estimation through Sensor Data Fusion

The parameter inversion of harmful gas diffusion source term is the inverse problem of harmful gas diffusion hazard assessment, which is of great significance to hazard prediction assessment and emergency response. Based on Bayesian reasoning method, using sensor detection data and forward atmospheric diffusion model, the likelihood function is constructed. Markov chain Monte Carlo (MCMC) sampling is used to establish the source term parameter inversion algorithm, and the simulation platform is established by MATLAB software for simulation analysis. The simulation results show that the inversion results are in good agreement with the initial source parameters, which proves the feasibility of the method.