Health Management and Diagnostics for Synthetic Aperture Radar (SAR) Payloads

Gregory Bower, Jon Zook, and Ross Bird
Submission Type: 
Full Paper
Supporting Agencies (optional): 
United States Air Force Research Laboratory
phmc_14_005.pdf922.68 KBSeptember 11, 2014 - 7:23am

A statistical method based on symbolic analysis is presented for health management of Synthetic Aperture Radar systems. The approach, based on symbolic theory, develops statistical models of the underlying system dynamics using a Markov assumption and tracks the change in model over time to determine system health. The methodology was designed for minimal impact to legacy systems and required minimal computational effort in order to operate at radar data rates. The approach was then applied to radar phase history data corrupted with simulated degradation. Two degradation mechanisms were studied: interference and array degradation. In addition, the results of combined degradation were also studied in this work.

Publication Year: 
Publication Volume: 
Publication Control Number: 
Page Count: 
Submission Keywords: 
Statistical Modeling
Synthetic Aperture Radar
Heath Management
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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