Optimum decoding-based smoothing algorithm for dynamic systems
Keri̇m Demi̇rbaş, Cornelius T. Leondes · International Journal of Systems Science · 1985
A new smoothing algorithm for discrete models is presented. For the disturbance noise and the observation noise, only independency is assumed. Moreover the models’ functions are not limited to continuous functions, i.e. they can be non-continuous. This algorithm estimates the states by first quantizing them and then using the Viterbi decoding algorithm. Simulation results have shown that for some non-linear models the new algorithm performs better than the extended Kalman filter algorithm, while it performs almost as well as the Kalman filter algorithm for linear models with gaussian noise.