Adaptive prediction error filters

Rustam Akhmadiev


Abstract:

The theory and the applications of prediction error filters are well known. Most of the time, however, they are built to be stationary (not varying in space and time) and therefore, they carry some global statistical information of the signals. Hence, these filters cannot be expected to be optimal in a nonstationary environment.

Here I address the problem of designing a nonstationary prediction error filter (PEF) using a gradient adaptive lattice and recursive least-squares filters. Their one- and two-dimensional applications (deconvolution) to nonstationary signals show better whitening properties compared to conventional stationary PEFs.






2018-06-10