In this thesis work, Cyclic Root MUSIC method is integrated with spatial smoothing and interpolation techniques to estimate the direction of arrivals of coherent,cyclostationary signals received via a Uniform Circular Array (UCA). Cyclic Root MUSIC and Conventional Root MUSIC algorithms are compared for various signal scenarios by computer simulations. A cyclostationary process is a random process with probabilistic parameters, such as the autocorrelation function, that vary periodically with time. Most of the man-made communication signals exhibit cyclostationarity due to the periodicity arising from their carrier frequencies, chip rates, baud rates, etc. Cyclic Root MUSIC algorithm exploits the cyclostationarity properties of signals to achieve signal selective direction of arrival estimation. Spatial smoothing is presented to overcome the coherent signals problem in a multipath propagation environment. Forward spatial smoothing and forward backward spatial smoothing techniques are investigated. Interpolation method is presented to cope with the restrictions of spatial smoothing on array structure. Although the array structure that is considered in this thesis (Uniform Circular Array), is not suitable for applying spatial smoothing directly, using interpolation method makes it possible. Performance of Cyclic Root MUSIC and Conventional Root MUSIC algorithms are compared under variation of various factors by computer simulations. Effects of signal type on the performance of the algorithms are observed by using different signal scenarios.

Source localization with a wireless sensor network remains an important area of research as the number of applications with this problem increases. This work considers the problem of source localization by a network of passive wireless sensors. The primary means by which localization is achieved is through direction-finding at each sensor, and in some cases, range estimation as well. Both single and multiple-target scenarios are considered in this research. In single-source environments, a solution that outperforms the classic least squared error estimation technique by combining direction and range estimates to perform localization is presented. In multiple-source environments, two solutions to the complex data association problem are addressed. The first proposed technique offers a less complex solution to the data association problem than a brute-force approach at the expense of some degradation in performance. For the second technique, the process of signal classification is considered as another approach to the data association problem. Environments in which each signal possesses unique features can be exploited to separate signals at each sensor by their characteristics, which mitigates the complexity of the data association problem and in many cases improves the accuracy of the localization. Two approaches to signal-selective localization are considered in this work. The first is based on the well-known cyclic MUSIC algorithm, and the second combines beamforming and modulation classification. Finally, the implementation of a direction-finding system is discussed. This system includes a uniform circular array as a radio frequency front end and the universal software radio peripheral as a data processor. / Master of Science