Title

DOA estimation exploiting cyclostationarity based on direct data domain approach in a real environment

Date of Award

2003

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Electrical Engineering and Computer Science

Advisor(s)

Tapan K. Sarkar

Keywords

Cyclostationarity, Direct data domain, Direction of arrival, Signal of interest

Subject Categories

Electrical and Computer Engineering | Engineering

Abstract

In an adaptive process, the goal is to extract the signal of interest (SOI) embedded in other interfering signals and noise. Generally, this adaptive process is carried out by using the available information on the direction of arrival (DOA) of the SOI. This is true for RADAR applications where that information is available, as we know a priori to which direction a beam was transmitted. However, in a mobile communication when the information about the DOA of the SOI is not available, the problem is then how to implement an adaptive process. Hence, to implement an adaptive process we first need to estimate the DOA of SOI. In this dissertation, we propose to use the concept of cyclostationarity to achieve that goal. The term cyclostationarity implies that the signal displays characteristic spectral properties and this property is shared by almost all man made communication signals. The novelty of this dissertation is that we implement a direct data domain approach to carry out the DOA estimation exploiting cyclostationarity. Thus we avoid the formation of a covariance matrix, which is always problematical when we have short data lengths or the environment is quite dynamic. In the proposed algorithm, while the estimation of the cyclic covariance matrix is avoided, we develop a new matrix form using extremely short data samples. As a result, the computational load in the proposed approach is relatively reduced and the robustness of the estimation of SOI is significantly improved when the number of available snapshots is extremely limited. Next, we consider the problem of elimination of multipath using the principle of cyclostationarity. Hence, we develop a post-processing technique for identification of multipath or signals having the same cycle frequency of interest. Finally, we incorporate the concept of electromagnetic analysis to eliminate the problem of mutual coupling between the elements and take into account the effect of near-field scatterers. Numerical results are presented in each section to illustrate the various concepts and how to implement these principles in a simulated system.

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