Dr. Steven Langel, Senior Technical Staff, The MITRE Corporation

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The prevalence of sequential estimation in navigation has increased over the past several years. This tutorial introduces the audience to the Kalman filter (KF), the most common sequential estimator in use today, with emphasis on timing applications. The first part of the tutorial gives a general treatment of where the KF comes from and how to use it for time estimation. We will discuss measurement and state dynamic models, how to develop stochastic error models, and the use of covariance analysis for performance prediction and filter design. The second part of the tutorial covers intermediate topics like filter initialization and observability, focusing on their implications for estimating time from an ensemble of clocks. The last section highlights current research related to Kalman filters, including clock drift monitoring for fault detection and new approaches to correlated noise modeling. Numerous examples will be used throughout the tutorial to solidify concepts. Dr. Steven Langel is a senior technical staff member at The MITRE Corporation. He leads efforts designing multisensor navigation systems and is developing new techniques to improve Kalman filter robustness.