Differential Kalman filtering for tracking Rayleigh fading channels

The performance of the estimator used in the tracking of a fading channel plays an essential role in many wireless receivers. The conventional Kalman filter is an optimum estimator; however, it is computationally demanding and complex for real-time implementation. A new approach is proposed for the implementation of the Kalman filter based on differential channel states. This leads to a robust differential Kalman filtering algorithm that can be simplified further to ease the implementation without any major loss in performance. It is also shown that the simplifications made to the differential Kalman filter lead to the least mean squares (LMS) algorithm, identifying it as a special case of the Kalman filter