Impact of the Dictionary Mismatch for CS-based DOA Estimation
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The problem of direction finding has primal importance in modern life. Today, smart direction finding systems are used in various technical fields such as RADAR, SONAR, interference reduction, multi-user or multi-channel data transmission, environmental sounding, medical purposes and in many other areas of human life. Recent achievements in the field of signal processing provide a new paradigm known as Compressed Sensing (CS), where the advantages of sparse signal representation are utilized. The DOA model can be also formulated as sparse, i.e. with reduced measurement effort of the antenna. T...
The problem of direction finding has primal importance in modern life. Today, smart direction finding systems are used in various technical fields such as RADAR, SONAR, interference reduction, multi-user or multi-channel data transmission, environmental sounding, medical purposes and in many other areas of human life. Recent achievements in the field of signal processing provide a new paradigm known as Compressed Sensing (CS), where the advantages of sparse signal representation are utilized. The DOA model can be also formulated as sparse, i.e. with reduced measurement effort of the antenna. This all brings a high potential for CS to be applied to a DOA estimation. The focus of this thesis is an in-depth analysis of the impact of the off-the-grid CS-based DOA model mismatch on the recovery process and the development of an efficient CS-based DOA estimator able to provide a stable performance even for a realistic scenario with true signal locations. Brief details related to this work are provided below. Course of Studies: Communications and Signal Processing; Department: Digital Broadcasting Research Laboratory; Responsible Professor: Univ.-Prof. Dr.-Ing. Giovanni Del Galdo; Supervisor: Dr.-Ing. Florian Roemer
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