A partitioned approach to spectral estimation

In selecting a reduced parametric representation of a process, it is desirable to utilize any a priori knowledge available. The authors propose a general partitioning scheme that can utilize such knowledge in a local enhancement of the process model. By partitioning the observation space, or a transform thereof, into disjoint subspaces, alternative possibly conflicting constraints can be applied to each region. The optimal subspace partitioning is found using a dynamic programming algorithm minimizing a mean-least-square error criterion derived from a rational (ARMA) innovations model