A Statistical Approach to Subspace Based Estimation with Applications in Telecommunications
Björn Ottersten, D. Asztély, Martin Kristensson, Stefan Parkvall · 1996
Subspace based estimation using decomposition techniques such as the SVD is a powerful tool in many signal processing applications where low rank signals in noise are observed. Examples of which include, sensor array signal processing, harmonic analysis, factor analysis, system identification, and blind channel equalization. By appropriately making use of a low rank signal model, subspace based, computationally efficient estimation techniques may be formulated. Also, the performance of subspace based methods is in many cases optimal or near optimal. This paper presents a systematic approach for formulating subspace based estimation techniques based on statistical considerations of the data. This approach may be applied to a wide range of problems where low rank signals are observed in noise. Some special cases are shown to result in well known estimators and examples of subspace based techniques in array signal processing, timing estimation, and channel estimation are given. 1 Introduc...