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.
|Original language||English (US)|
|Title of host publication||2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||5|
|State||Published - Mar 18 2019|