FAST PHASE-DIFFERENCE-BASED DOA ESTIMATION USING RANDOM FERNS

Hui Chen, Tarig Ballal, Tareq Y. Al-Naffouri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Direction of arrival (DOA) information of a signal is important in communications, localization, object tracking and so on. Frequency-domain-based time-delay estimation is capable of achieving DOA in subsample accuracy; however, it suffers from the phase wrapping problem. In this paper, a frequency-diversity based method is proposed to overcome the phase wrapping problem. Inspired by the machine learning technique of random ferns, an algorithm is proposed to speed up the search procedure. The performance of the algorithm is evaluated based on three different signal models using both simulations and experimental tests. The results show that using random ferns can reduce search time to 1/6 of the search time of the exhaustive method while maintaining the same accuracy. The proposed search approach outperforms a benchmark frequency-diversity based algorithm by offering lower DOA estimation error.
Original languageEnglish (US)
Title of host publication2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages256-260
Number of pages5
ISBN (Print)9781728112954
DOIs
StatePublished - Mar 18 2019

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