Signal parameter estimation methods: The non-eigenvector based approach

Abstract

An idea behind this dissertation is the estimation of signal parameters of a radio channel snapshot. The focal point here to utilize the high resolution estimation capabilities of subspace based methods in association with non-eigenvector based parameter estimation methods to reduce the complexity in wireless system parameter estimation process. The first part of the work is to scrutinize various subspace based parametric estimation methods in well explored array signal processing based wireless communication system problem. These high resolution spectral parameter estimation methods broadly classified in terms of eigenvector based and non-eigenvector based estimation methods. Although providing high resolution and well-known in literature, the eigenvector based spectral parameter estimation methods do not comply requirements of real time signal processing of today's highly complex radio receivers. Therefore, this dissertation is focusing on computationally efficient noneigenvector methods for signal spectral parameter estimation such as Rank Revealing QR factorization, Propagator Method, Accelerated MUSIC, and other triangular factorization methods. The second part of this dissertation concentrate on performance evaluation of these noneigenvector based methods in real world communication system problems. The performance of these methods is demonstrated under three different parameter estimation problems such as multipath time delay estimation in FH-CDMA system, channel estimation problem in MUMIMO system, and joint parameter estimation problem in array signal processing. The role of eigenvector based methods in the spectral parameter estimation is efficiently transformed into non-eigenvector based parameter estimation procedure. This transformation leads to development of computationally efficient algorithms with an enhanced estimation capability.