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Fast phase-difference-based DoA estimation using random ferns

Citation Author(s):
Hui Chen, Tarig Ballal, Tareq Y. Al-Naffouri
Submitted by:
Hui Chen
Last updated:
27 November 2018 - 3:31am
Document Type:
Poster
Document Year:
2018
Event:
Presenters Name:
Hui Chen
Paper Code:
1313

Abstract 

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.

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