Geometric remarks on Kalman filtering with intermittent observations

TL;DR

This paper challenges previous interpretations of the critical observation probability in Kalman filtering with intermittent data, proposing a geometric approach to better understand the behavior of covariance estimates.

Contribution

It introduces a differential geometric framework to analyze the covariance estimation problem, revealing that the critical probability depends on the chosen metric and the type of average considered.

Findings

Different metrics lead to different critical probabilities.

The average error behavior varies with the geometric interpretation.

Some metrics show no critical probability, altering previous assumptions.

Abstract

Sinopoli et al. (TAC, 2004) considered the problem of optimal estimation for linear systems with Gaussian noise and intermittent observations, available according to a Bernoulli arrival process. They showed that there is a "critical" arrival probability of the observations, such that under that threshold the expected value of the covariance matrix (i.e., the quadratic error) of the estimate is unbounded. Sinopoli et al., and successive authors, interpreted this result implying that the behavior of the system is qualitatively different above and below the threshold. This paper shows that this is not necessarily the only interpretation. In fact, the critical probability is different if one considers the average error instead of the average quadratic error. More generally, finding a meaningful "average" covariance is not as simple as taking the algebraic expected value. A rigorous way to frame the problem is in a differential geometric framework, by recognizing that the set of covariance matrices (or better, the manifold of Gaussian distributions) is not a flat space, and then studying the intrinsic Riemannian mean. Several metrics on this manifold are considered that lead to different critical probabilities, or no critical probability at all.