Mixed near-field and far-field source localization revised: propagation loss included

• Published in: Multidimensional systems and signal processing Vol. 31; no. 2; pp. 711 - 723
• Main Authors: Behmandpoor, Pourya, Haddadi, Farzan
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2020; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Circuits and Systems; Computer simulation; Direction of arrival; Electrical Engineering; Engineering; Localization; Parameter estimation; Sensor arrays; Signal classification; Signal,Image and Speech Processing; T shape; Array signal processing; Far-field source; Propagation loss; Source localization; Near-field source
• ISSN: 0923-6082; 1573-0824
• DOI: 10.1007/s11045-019-00683-2

This paper is concerned with source localization when path loss is taken into account. We modify multiple signal classification method to localize near-field sources whose received power is different in the sensors of the array due to path loss. Traditional methods fail to localize the sources, and they also fail to separate the bearing estimation and the range estimation of the sources, when path loss is considered. We suggest a T-shaped array avoiding multidimensional search to estimate source location parameters, separately. At the first step, the ranges of the signal sources are estimated, and then at the second step, the directions of arrival of the sources are estimated using the respective ranges determined at the first stage. The performance of the proposed method is assessed when mixed near-field and far-field sources coexist. Simulation results are presented to show the superior performance of the proposed algorithm compared to the existing techniques and the Cramer–Rao bound.