Partial Relaxation: A Computationally Efficient Direction-of-Arrival Estimation Framework (2020)
Abstract / truncated to 115 words
Direction-of-Arrival (DOA) estimation from data collected at a sensor array in the presence of noise has been a fundamental and long-established research topic of interest in sensor array processing. The application of DOA estimation does not only restrict to radar but also spans multiple additional fields of research, including radio astronomy, biomedical imaging, seismic exploration, wireless communication, among others. Due to the wide applications of DOA estimation, various methods have been developed in the literature to increase the resolution capability, computational efficiency, and robustness of the algorithms. However, a trade-off between the estimation performance and the computational complexity is generally inevitable. This thesis addresses the challenge of developing low-complexity DOA estimators with the ability to ... toggle 6 keywordsdoa estimation – approximate maximum likelihood – partial relaxation – eigenvalue decomposition – efficient implementation – cramer rao bound
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