State space least mean fourth algorithm

Adaptive filters generally employed for estimation purposes require high computational power when it comes to real time estimation. Therefore, in this paper we propose a computationally light yet effective estimation algorithm based on state space model. Our algorithm has been employed successfully in linear and non linear state space model based estimation problems.We investigate few examples to demonstrate the novelty of our algorithm by comparison with few existing algorithms in presence of non Gaussian noise namely uniform noise. More specifically, the state space normalized least mean squares and the Kalman filter has been compared with our algorithm.