Noisy data and impulse response estimation

Abstract--This paper investigates the impulse response estimation of linear time-invariant (LTI) systems when only noisy finitelength input-output data of the system is available. The competing parametric candidates are the least square impulse response estimates of possibly different lengths. It is known that the presence of noise prohibits using model sets with large number of parameters as the resulting parameter estimation error can be quite large. Model selection methods acknowledge this problem, hence, they provide metrics to compare estimates in different model classes. Such metrics typically involve a combination of the available leastsquare output error, which decreases as the number of parameters increases, and a function that penalizes the size of the model. In this paper, we approach the model class selection problem from a different perspective that is closely related to the involved denoising problem. The method primarily focuses on estimating the parameter error in a giv...

• Type: Journal
• Year: 2010
• Where: TSP
• Authors: Soosan Beheshti, Munther A. Dahleh
• Keywords: Artificial Intelligence, Estimation Error, Model, Output Error, TSP 2010