D-Optimized Sampling Design for System Identification
For researchers in system identification, this work enables accurate identification in scenarios where periodic excitation is impractical, addressing a practical bottleneck.
The paper develops a nonparametric frequency response function estimator for continuous-time system identification under nonperiodic multisine excitation and nonuniform sampling, and designs irregular sampling schemes that reduce spectral leakage and improve statistical accuracy.
Traditional system identification with multisine inputs relies on uniform sampling and periodic excitation to preserve Fourier orthogonality and avoid spectral leakage, limiting its use in scenarios with irregular sampling or nonperiodic inputs. This work investigates continuous-time system identification under nonperiodic multisine excitation and nonuniform sampling. We develop a nonparametric frequency response function estimator suited to such conditions and design irregular sampling schemes that enhance the informativeness of measurements and reduce spectral leakage. The proposed sampling scheme improve the statistical accuracy of system identification in settings where periodic excitation is impractical.