TY - GEN
T1 - OPTIMAL EIGENVALUE DECOMPOSITION BASED FREQUENCY ESTIMATION ALGORITHM FOR COMPLEX SINUSOIDAL SIGNALS
AU - Zubair, Muhammad
AU - Ahmed, Sajid
AU - Jardak, Seifallah
AU - Alouini, Mohamed-Slim
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2019/3/18
Y1 - 2019/3/18
N2 - The estimation performance of subspace based algorithms depends on the selection of dominant eigenvalues, which is challenging. In this paper, by exploiting the circular transformation, an optimal dominant eigenvalue selection subspace based frequency estimation algorithm is proposed. The proposed algorithm restricts the contribution of signal into fixed number of dominant eigenvalues. The performance of the proposed algorithm is compared with Multiple Signal Classification (MUSIC) and Karhunen-Loeve-transform (KLT) algorithms. The analytical and simulation results show that proposed algorithm outperforms the MUSIC and KLT algorithms.
AB - The estimation performance of subspace based algorithms depends on the selection of dominant eigenvalues, which is challenging. In this paper, by exploiting the circular transformation, an optimal dominant eigenvalue selection subspace based frequency estimation algorithm is proposed. The proposed algorithm restricts the contribution of signal into fixed number of dominant eigenvalues. The performance of the proposed algorithm is compared with Multiple Signal Classification (MUSIC) and Karhunen-Loeve-transform (KLT) algorithms. The analytical and simulation results show that proposed algorithm outperforms the MUSIC and KLT algorithms.
UR - http://hdl.handle.net/10754/652992
UR - https://ieeexplore.ieee.org/document/8646586
UR - http://www.scopus.com/inward/record.url?scp=85063095504&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP.2018.8646586
DO - 10.1109/GlobalSIP.2018.8646586
M3 - Conference contribution
SN - 9781728112954
SP - 1119
EP - 1123
BT - 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -