Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
System identification
Identification of continuous systems
Identification of continuous systems
Digital signal processing (3rd ed.): principles, algorithms, and applications
Digital signal processing (3rd ed.): principles, algorithms, and applications
Matrix computations (3rd ed.)
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Estimation of continuous-time autoregressive model from finelysampled data
IEEE Transactions on Signal Processing
A delta least squares lattice algorithm for fast sampling
IEEE Transactions on Signal Processing
Estimation of Continuous-Time Stochastic Signals From Sample Covariances
IEEE Transactions on Signal Processing
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Both direct and indirect methods exist for identifying continuous-time linear systems. A direct method estimates continuous-time input and output signals from their samples and then use them to obtain a continuous-time model, whereas an indirect method estimates a discrete-time model first. Both methods rely on fast sampling to ensure good accuracy. In this paper, we propose a more direct method where a continuous-time linear model is directly fitted to the available samples. This method produces an exact model asymptotically, modulo some possible aliasing ambiguity, even when the sampling rate is relatively slow. We also state conditions under which the aliasing ambiguity can be resolved, and we provide experiments showing that the proposed method is a valid option when a slow sampling frequency must be used but a large number of samples is available.