Subspace based estimation of multiple real-valued sine wave frequencies is considered in this paper. A novel data covariance model is proposed. In the proposed model the dimension of the signal subspace equals the number of frequencies present in the data, which is half of the signal subspace dimension for the conventional model. Consequently, an ESPRIT like algorithm using the proposed data model is presented. The proposed algorithm is then extended for the case of complex-valued sine waves. Performance analysis of the proposed algorithms are also carried out. The algorithms are tested in numerical simulations. The newly proposed algorithm outperforms ESPRIT by a significant margin and is computationally efficient as well.
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