Unscented Kalman Filter/Smoother for a CBRN puff-based dispersion model

Fixed interval smoothing for systems with nonlinear process and measurement models is studied and applied to the assimilation of sensor data in a Chemical, Biological, Radiological or Nuclear (CBRN) incident scenario. A two-filter smoother that uses a Backward Sigma-Point Information Filter, and also a forward-backward Rauch-Tung-Striebel (RTS) smoothing form are re-derived using the weighted statistical linearization concept. Both methods are derived in the context of the Unscented Kalman Filter. The square root version of the resulting RTS Unscented Kalman Filter / Smoother is applied to a CBRN dispersion puff-based model with variable state dimension, and the data assimilation performance of the method is compared with a Particle Filter implementation.