KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Online Publication Date2019-03-18
Print Publication Date2018-11
Permanent link to this recordhttp://hdl.handle.net/10754/631802
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AbstractDirection 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.
CitationChen H, Ballal T, Al-Naffouri TY (2018) FAST PHASE-DIFFERENCE-BASED DOA ESTIMATION USING RANDOM FERNS. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). Available: http://dx.doi.org/10.1109/GlobalSIP.2018.8646676.
Conference/Event name2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018