Abstract
The joint application of singular spectrum analysis (SSA) approach (basic variant or adaptive variant) and unitary transformation of the forward-backward data matrix obtained after SSA technique or forward-backward version of the covariance matrix is proposed for improvement of estimation performance of the signal parameters. The unitary transformation reduces the computational complexity and improves the threshold performance of spectral analysis performed by subspace-based techniques. Performance improvement can be explained by forward–backward averaging effect that has a place when performing the unitary transformation. This averaging effectively doubles the number of samples. The proposed approach can be characterized by reduced computational load such as the computations with real-valued numbers are performed after unitary transformation. Unitary Root-MUSIC is mainly used for the simulation. The unitary formulation of ESPRIT is obtained for the problem of estimation of signal component frequencies. The possible applications of considered approach in the communication systems (including channel estimation, speech processing, automatic modulation classification and so on) are considered. Simulation results are presented to demonstrate the improved performance of subspace-based techniques when using proposed approach.
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Vasylyshyn, V. (2021). Estimation of Signal Parameters Using SSA and Linear Transformation of Covariance Matrix or Data Matrix. In: Ageyev, D., Radivilova, T., Kryvinska, N. (eds) Data-Centric Business and Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-030-71892-3_15
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