Parameter estimation of superimposed sinusoids by data matrix subfactorization: Theory and algorithm > International Conferences

Parameter estimation of superimposed sinusoids by data matrix subfactorization: Theory and algorithm
Year	2016
Month
Journal	2016 / International Conference on Actual Problems of Electron Devices Engineering (APEDE)
Author	A. S. Moutchkaev; S.-H. Kong; A. A. L'vov
Link	https://ieeexplore.ieee.org/abstract/document/7879042 126회 연결
Estimating parameters of a sum of complex exponentials in white noise is considered in this paper. A simplified maximum likelihood estimation algorithm based on subfactorization of a structured data matrix is proposed, and we show that parameterization of the data model in signal space allows to improve estimation accuracy at low signal-to noise ratio (SNR). The idea of solution of the normal equations is based on the singular value decomposition method of the data matrix, which allows one to simplify drastically the obtained equations. The geometric sence of the proposed solution is discussed.