Direction of arrival (DOA) estimation is an important research area having exten-sive applications including radar, sonar, wireless communications, and electronic warfare systems. Development and popularization of the compressed sensing (CS) theory has led to a vast literature on the use of the CS techniques in DOA esti-mation which has been shown to be superior over the classical techniques under various scenarios. In the CS based techniques, measurement matrices determine the received information while sparsity promoting reconstruction algorithms are used to estimate the unknown DOAs. Hence, design of measurement matrices and sparse reconstruction algorithms are among the most important aspects of the CS theory. In this thesis, both aspects are investigated and novel techniques are proposed for improved performance. Following a brief explanation of the classical and the CS based DOA estimation techniques, a new optimization perspective is introduced on the Capon’s beam-former by using the minimum mean square error criterion. After that, a mea-surement matrix design methodology exploiting prior information on the source environment is introduced. Hardware and sofware implementation constraints of the introduced method are investigated and more efficient alternatives are pro-posed. Additionally, an adaptive dictionary design algorithm is introduced for more effective use of the prior information. Lastly, the Cramer-Rao Lower Bound expression for the compressed DOA signal models is derived and its implications on the measurement matrix design are investigated leading to a sector based mea-surement matrix design technique along with a novel reconstruction algorithm.