A suboptimum decoding-based smoothing algorithm for dynamic systems with or without interference
Keri̇m Demi̇rbaş, Cornelius T. Leondes · International Journal of Systems Science · 1986
A suboptimum smoothing algorithm is presented for dynamic systems with or without an arbitrary random interference. For the disturbance noise, observation noise and interference, only independency is assumed. The model functions are any defined functions. This algorithm estimates the states by first representing the state model by a trellis diagram and then using a suboptimum decoding algorithm. It requires a constant memory for its implementation. Numerical results have shown that for either linear or non-linear discrete models with interference, the new algorithm performs very well. Further, in the absence of interference, it performs better than the extended Kalman filter algorithm for some non-linear discrete models.