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|Title:||Neural networks for direction of arrival estimation in the presence of array sensor failures||Authors:||Vigneshwaran Subbaraju||Keywords:||DRNTU::Engineering::Electrical and electronic engineering||Issue Date:||2007||Source:||Vigneshwaran, S. (2007). Neural networks for direction of arrival estimation in the presence of array sensor failures. Master’s thesis, Nanyang Technological University, Singapore.||Abstract:||Direction of Arrival (DoA) estimation is an important step in the implementation of adaptive/smart antennas which are an integral part of mobile communication systems. Conventional array signal processing algorithms have been widely used for DoA estimation. Among the conventional methods eigen-decomposition based methods such as MUSIC, ESPRIT etc have been found to be accurate, and provide higher resolution when compared to other methods. But these methods have been found to be computationally complex and are hence unsuitable for real-time operation. They are also sensitive to sensor failures, noise and other antenna effects like mutual coupling, non-uniformities in the positions of the sensors etc and require calibrated antennas with uniform features. A sensor is said to have failed when it can no longer receive the signals from the sources and its output consists only of the noise produced by the other circuitry involved. Mutual coupling is a phenomenon where the signal at a particular sensor induces a signal at others sensors in its close proximity. In the existing methods, sensor failure is tackled by introducing some corrections to the wrong data acquired after sensor failure and mutual coupling effects are usually overcome by calibrating the array before making the measurements.||Description:||123 p.||URI:||http://hdl.handle.net/10356/39065||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Theses|
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