Kalman Filtering with Intermittent Observations: Tail Distribution and Critical Value


Yilin Mo and Bruno Sinopoli

IEEE Transactions on Automatic Control, Mar 2012, Volume 57, Issue 3, Pages 677-689

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Abstract

In this paper, we analyze the performance of Kalman filtering for discrete-time linear Gaussian systems, where packets containing observations are dropped according to a Markov process modeling a Gilbert-Elliot channel. To address the challenges incurred by the loss of packets, we give a new definition of non-degeneracy, which is essentially stronger than the classical definition of observability, but much weaker than one-step observability, which is usually used in the study of Kalman filtering with intermittent observations. We show that the trace of the Kalman estimation error covariance under intermittent observations follows a power decay law. Moreover, we are able to compute the exact decay rate for non-degenerate systems. Finally, we derive the critical value for non-degenerate systems based on the decay rate, improving upon the state of the art.

Corrections

The Theorem 4 is flawed due to the fact that in (39), \(\text{det}(U)\) could be zero. However, the main results (Theorem 5-8) still hold. To fix the problem in (39), one can check the proof of Theorem 7 in the arxiv paper.