Beiyi Liu
Akita Prefectural University
16 Papers
17 Citations
Beiyi Liu is an academic researcher from Akita Prefectural University. The author has contributed to research in topics: Compressed sensing & Sparse approximation. The author has an hindex of 4, co-authored 16 publications.
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Papers
Variable-step-size based sparse adaptive filtering algorithm for channel estimation in broadband wireless communication systems
TL;DR: A variable step-size ZA-NLMS (VSS-ZA- NLMS) algorithm to improve the ASCE and is theoretically analyzed and verified by numerical simulations in terms of mean square deviation (MSD) and bit error rate (BER) metrics.
Sparse Detection Algorithms Based on Two-Dimensional Compressive Sensing for Sub-Nyquist Pulse Doppler Radar Systems
TL;DR: Four 2-D compressive sensing algorithms which are extended from the traditional 1-D CS algorithms (the ZAP, IHT, ISTA, and FISTA algorithms) are proposed which can achieve comparable detection performance with lower memory requirement and eliminate the interference of impulsive noise in the non-Gaussian impulse noise environment.
Fast NLMF-type algorithms for adaptive sparse system identifications
Guan Gui,Beiyi Liu,Li Xu,Wentao Ma +3 more
- 01 Dec 2015
TL;DR: This paper proposes a kind of non-constraint fast sparse NLMF-type algorithms for applying in ASIDE, which provides an alternative way to get rid of the restriction of SNR-dependent initial MSE and input variance.
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Compressive Sensing Based Direction-of-Arrival Estimation in MIMO Radars in Presence of Strong Jamming via Blocking Matrix
Beiyi Liu,Guan Gui,Shin-ya Matsushita,Li Xu +3 more
- 08 Jul 2018
TL;DR: A compressive sensing (CS)-based anti-jamming DOA estimation with only a few snapshots is proposed and it is demonstrated that the proposed method outperforms the traditional methods.
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DOA Estimation With Small Snapshots Using Weighted Mixed Norm Based on Spatial Filter
TL;DR: A CS-based DOA estimation using a novel weighted weighting matrix based on a spatial filter which can roughly “clean up” or eliminate the signals coming from the directions of the true sources is proposed.
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