Recent Developments in Statistical Signal Processing

Abstract

Publisher Summary Determining optimal algorithms and implementing the optimal algorithms are the two major aspects of statistical signal processing. There is considerable scope for application of ideas from linear system theory. One way to restrict complexity is to make further assumptions on the problem. A very common one in the last two decades has been to assume that there is an underlying finite-dimensional state-space model for the problem, in which case the memory can be fixed at the size of the state vector; however, the resulting filters will still be time variant. In any case, the important point is that these new results make for a much more direct link between signal processing theory and digital circuit implementations. This offers some promise in significantly reducing the software bottlenecks encountered in fitting all algorithms into simple general purpose architecture.