D-Optimized Sampling Design for System Identification

TL;DR

This paper introduces a novel sampling design and estimator for continuous-time system identification using nonperiodic multisine inputs and irregular sampling, addressing limitations of traditional methods.

Contribution

It develops a nonparametric frequency response estimator and designs irregular sampling schemes to improve accuracy under nonperiodic excitation and nonuniform sampling.

Findings

Enhanced statistical accuracy of system identification with irregular sampling.

Effective reduction of spectral leakage in nonperiodic excitation scenarios.

Improved informativeness of measurements through optimized sampling schemes.

Abstract

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.