Subspace approach for two-dimensional parameter estimation of multiple damped sinusoids

In this paper, we tackle the two-dimensional (2-D) parameter estimation problem for a sum of K ≥ 2 real/complex damped sinusoids in additive white Gaussian noise. According to the rank-K property of the 2-D noise-free data matrix, the damping factor and frequency information is contained in the K dominant left and right singular vectors. Using the sinusoidal linear prediction property of these vectors, the frequencies and damping factors of the first dimension are first estimated. For each frequency of the first dimension, the corresponding parameter in the second dimension is then obtained to achieve autopairing. Computer simulations are included to compare the proposed approach with several conventional 2-D estimators in terms of mean square error performance and computational complexity. Index Terms dominant singular vector, linear prediction, weighted least squares, two-dimensional parameter estimation, spectral analysis