Reconfigurable architectures for task based and numerically intensive satellite signal processing

Conventional non-adaptive filters used in satellite communication for signal processing performs well as long as the spatial density of the impulse noise is less (typically less than 0.2). An adaptive filter can handle impulse noise even with higher probabilities and also can preserve sharpness and detail while smoothing nonimpulse noise. Traditional adaptive filters works in a rectangular window whose size varies during filter operation depending on certain conditions. The optimal size of this window is crucial and influences the computation and memory requirements. However, this depends on the application and a starting value needs to be estimated by experimenting with various sizes of the standard filter first. For many signal processing systems, it is possible to exploit variations in signals and to keep the window size fixed but still perform effective adaptive filtering. This will minimize computation and memory requirements. In this context, reconfigurable computing is proposed in this work, for signal processing applications with various objectives, including high performance, flexibility, specialization and adaptability.