Second-Order Kalman Filtering Application to Fading Channels Supported by Real Data

The lack of effective techniques for estimation of shadow power in fading mobile wireless communication channels motivated the use of Kalman Filtering as an effective alternative. In this paper, linear second-order state space Kalman Filtering is further investigated and tested for applicability. This is important to optimize estimates of received power signals to improve control of handoffs. Simulation models were used extensively in the initial stage of this research to validate the proposed theory. Recently, we managed to further confirm validation of the concept through experiments supported by data from real scenarios. Our results have shown that the linear second-order state space Kalman Filter (KF) can be more accurate in predicting local shadow power profiles than the first-order Kalman Filter, even in channels with imposed non-Gaussian measurement noise.