Brings together in one book classical and modern DOA techniques, showing the connections between them Contains contributions from the leading people in the field Gives a concise and easy- to- read introduction to the classical techniques Evaluates the strengths and weaknesses of key super-resolution techniques Includes applications to sensor networks Classical and Modern Direction of Arrival Estimation contains both theory and practice of direction finding by the leading researchers in the field. This unique blend of techniques used in commercial DF systems and state-of-the art super-resolution methods is a valuable source of information for both practicing engineers and researchers. Key topics covered are: Classical methods of direction finding Practical DF methods used in commercial systems Calibration in antenna arrays Array mapping, fast algorithms and wideband processing Spatial time-frequency distributions for DOA estimation DOA estimation in threshold region Higher order statistics for DOA estimation Localization in sensor networks and direct position estimation T. Engin Tuncer is a Professor in Electrical and Electronics Engineering Department of Middle East Technical University, Turkey. His research is focused on sensor array and multichannel signal processing, statistical signal processing and communications. Ben Friedlander is an internationally known expert in the areas of statistical signal processing and its applications to communications and surveillance systems. He has extensive experience spanning over three decades in array processing and direction finding. In recent years his work focused on the use of multiple antennas for wireless communications. Currently he is a professor of electrical engineering at the University of California at Santa Cruz. Brings together in one book classical and modern DOA techniques, showing the connections between them Contains contributions from the leading people in the field Gives a concise and easy- to- read introduction to the classical techniques Evaluates the strengths and weaknesses of key super-resolution techniques Includes applications to sensor networks