Weighted Null Space Fitting (WNSF): A Link between The Prediction Error Method and Subspace Identification

TL;DR

This paper introduces Weighted Null Space Fitting (WNSF), a novel method that combines the robustness of subspace identification with the accuracy of prediction error methods for state-space model estimation.

Contribution

WNSF is a new approach that estimates the null space of the extended observability matrix, avoiding SVD and providing statistically optimal weighting, bridging SIM and PEM advantages.

Findings

WNSF avoids SVD by estimating null space.

WNSF is asymptotically efficient based on simulations.

WNSF combines robustness and accuracy in state-space identification.

Abstract

Subspace identification method (SIM) has been proven to be very useful and numerically robust for estimating state-space models. However, it is in general not believed to be as accurate as the prediction error method (PEM). Conversely, PEM, although more accurate, comes with non-convex optimization problems and requires local non-linear optimization algorithms and good initialization points. This contribution proposes a weighted null space fitting (WNSF) method to identify a state-space model, combining some advantages of the two mainstream approaches aforementioned. It starts with the estimate of a non-parametric model using least-squares, and then the reduction to a state-space model in the observer canonical form is a multi-step least-squares procedure where each step consists of the solution of a quadratic optimization problem. Unlike SIM, which focuses on the range space of the extended observability matrix, WNSF estimates its null space, avoiding the need for singular value decomposition. Moreover, the statistically optimal weighting for the null space fitting problem is derived. It is conjectured that WNSF is asymptotically efficient, which is supported by a simulation study.