Estimation of large sets of stochastic signals: The case of sparse sampling
Torokhti, A., Howlett, P. and Laga, H. (2014) Estimation of large sets of stochastic signals: The case of sparse sampling. Sampling Theory in Signal and Image Processing, 13 (3). pp. 207-230.
In many applications, a priori information on a large set of signals of interest can only be obtained for a few signals, p, while information on other signals is missing. At the same time, it is required to estimate each reference signal. The signal is a stochastic vector and the observations are noisy. The conceptual foundation of the proposed filter is an optimal least squares linear estimate of the incremental change to the p signal pairs, extended by a natural interpolation to an estimated value of each reference signal. The new filter is expressed in terms of the Moore-Penrose pseudo-inverse matrices and therefore is always well-defined.
|Publication Type:||Journal Article|
|Copyright:||2014 Sampling Publishing|
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