Parameter estimation for Relational Kalman Filtering

Jaesik Choi, Eyal Amir, Tianfang Xu, Albert J. Valocchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The Kalman Filter (KF) is pervasively used to control a vast array of consumer, health and defense products. By grouping sets of symmetric state variables, the Relational Kalman Filter (RKF) enables to scale the exact KF for large-scale dynamic systems. In this paper, we provide a parameter learning algorithm for RKF, and a regrouping algorithm that prevents the degeneration of the relational structure for efficient filtering. The proposed algorithms significantly expand the applicability of the RKFs by solving the following questions: (1) how to learn parameters for RKF in partial observations; and (2) how to regroup the degenerated state variables by noisy real-world observations. We show that our new algorithms improve the efficiency of filtering the large-scale dynamic system.

Original languageEnglish (US)
Title of host publicationStatistical Relational AI - Papers Presented at the 28th AAAI Conference on Artificial Intelligence, Technical Report
PublisherAI Access Foundation
Pages22-28
Number of pages7
ISBN (Electronic)9781577356745
StatePublished - 2014
Externally publishedYes
Event28th AAAI Conference on Artificial Intelligence, AAAI 2014 - Quebec City, Canada
Duration: Jul 27 2014 → …

Publication series

NameAAAI Workshop - Technical Report
VolumeWS-14-13

Conference

Conference28th AAAI Conference on Artificial Intelligence, AAAI 2014
Country/TerritoryCanada
CityQuebec City
Period7/27/14 → …

ASJC Scopus subject areas

  • Engineering(all)

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