Identifiability Analysis of Noise Covariances for LTI Stochastic Systems with Unknown Inputs

TL;DR

This paper investigates the conditions under which process and measurement noise covariances in LTI stochastic systems with unknown inputs can be uniquely identified using a correlation-based approach, addressing a practical gap in filter design.

Contribution

It establishes necessary and sufficient conditions for the joint and individual identifiability of noise covariances in systems with unknown inputs, extending existing knowledge.

Findings

Necessary conditions for joint identifiability of Q and R.

Conditions for unique identification of Q when R is known.

Conditions for unique identification of R when Q is known.

Abstract

Most existing works on optimal filtering of linear time-invariant (LTI) stochastic systems with arbitrary unknown inputs assume perfect knowledge of the covariances of the noises in the filter design. This is impractical and raises the question of whether and under what conditions one can identify the process and measurement noise covariances (denoted as $Q$ and $R$, respectively) of systems with unknown inputs. This paper considers the identifiability of $Q$/$R$ using the correlation-based measurement difference approach. More specifically, we establish (i) necessary conditions under which $Q$ and $R$ can be uniquely jointly identified; (ii) necessary and sufficient conditions under which $Q$ can be uniquely identified, when $R$ is known; (iii) necessary conditions under which $R$ can be uniquely identified, when $Q$ is known. It will also be shown that for achieving the results mentioned above, the measurement difference approach requires some decoupling conditions for constructing a stationary time series, which are proved to be sufficient for the well-known strong detectability requirements established by Hautus.