A Data-Driven McMillan Degree Lower Bound

TL;DR

This paper introduces a probabilistic method to estimate a lower bound on the McMillan degree of a linear system from noisy measurements, using a new bound on the singular values of random Hankel matrices.

Contribution

It provides a novel probabilistic upper bound on Hankel matrix singular value perturbations, guiding threshold setting for McMillan degree estimation under noise.

Findings

New probabilistic bound on 2-norm of random Hankel matrices

Guidance for threshold setting in noisy McMillan degree estimation

Comparable accuracy to empirical bounds without unknown constants

Abstract

Given measurements of a linear time-invariant system, the McMillan degree is the dimension of the smallest such system that reproduces these observed dynamics. Using impulse response measurements where the system has been started in some (unknown) state and then allowed to evolve freely, a classical result by Ho and Kalman reveals the McMillan degree as the rank of a Hankel matrix built from these measurements. However, if measurements are contaminated by noise, this Hankel matrix will almost surely be full rank. Hence practitioners often estimate the rank of this matrix---and thus the McMillan degree---by manually setting a threshold between the large singular values that correspond to the non-zero singular values of the noise-free Hankel matrix and the small singular values that are pertubations of the zero singular values. Here we introduce a probabilistic upper bound on the perturbation of the singular values of this Hankel matrix when measurements are corrupted by additive Gaussian noise, and hence provide guidance on setting the threshold to obtain a lower bound on the McMillan degree. This result is powered by a new, probabilistic bound on the 2-norm of a random Hankel matrix with normally distributed entries. Unlike existing results for random Hankel matrices, this bound features no unknown constants and, moreover, is within a small factor of the empirically observed bound when entries are independent and identically distributed. This bound on the McMillan degree provides an inexpensive alternative to more general model order selection techniques such as the Akaike Information Criteria (AIC).