Reduced polynomial order linear prediction

Abstract

Abstract-Reduced rank linear predictive frequency and direction-of-arrival (DOA) estimation algorithms use the singular value decomposition (SVD) to produce a noise-cleaned linear prediction vector. These algorithms then root this vector to obtain a subset of roots, whose angles contain the desired frequency or DOA information. The roots closest to the unit circle are deemed to be the "signal roots." The rest of the roots are "extraneous." The extraneous roots are expensive to calculate. Further, a search must be done to discern the signal roots from the extraneous roots. Here, we present a reduced polynomial order linear prediction method that simplifies the rooting computation for applications where high-speed processing is critical.

An Adaptive CM Array Preconditioner for Blind Multi-User Separation

“…Ordinary least squares (OLS) is the most common and the most basic estimator that can be employed to carry out linear prediction on the CM array. Extensions of the former with or without additional modifications are discussed and evaluated in [20] and later works, such as [21], [22].…”