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

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Gregory Bower Jonathan Zook Ross Bird

Abstract

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 an underlying 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 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.

How to Cite

Bower, G. ., Zook, J. ., & Bird, R. . (2014). Health Management and Diagnostics for Synthetic Aperture Radar (SAR) Payloads. Annual Conference of the PHM Society, 6(1). https://doi.org/10.36001/phmconf.2014.v6i1.2351
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Keywords

Statistical Modeling, Synthetic Aperture Radar, Heath Management

References
Bower, G., Mayer, J., & Reichard, K. (2011). "Symbolic Dynamics and Analysis of Time Series Data for Diagnostics of a dc-dc Forward Converter," in Annual Conference of the Prognostics and Health Management Society, Montreal, 2011.

Bower, G., Mayer, J., & Reichard, K. (2008). “Symbolic Dynamics for Anomaly Detection in a dc-dc Forward Converter,” Proceedings of the 2008 International Conference on Prognostics and Health Management, Denver, October 2008.

Berger, S. D. (2001). "The spectrum of a digital radio frequency memory linear range gate stealer electronic attack signal," Proceedings of the 2001 IEEE Radar Conference, Atlanta, 2001.

Daw, C.S., C.E.A. Finney & E.R. Tracy (2003). "A review of symbolic analysis of experimental data." Review of Scientific Instruments 74.2 (2003): 915-930.

Gorham, L. & L. Moore (2010). "SAR image formation toolbox for MATLAB." Proceedings of the SPIE. 2010.

Kwak, C.M. (2009). "Application of DRFM in ECM for pulse type radar," 34th International Conference on Infrared, Millimeter, and Terahertz Waves, Busan, Korea, 2009.

Mehalic, M. & Sayson, A. M. (1992). "A dual-port DRAM component for a digital RF memory," Proceedings of the IEEE 1992 National Aerospace and Electronics Conference, Dayton, 1992.
Melvin, William L., Scheer, James, A. (2013). Principles of Modern Radar: Advanced Techniques. Edison, NJ: SciTech Publishing.

Papoulis, A., & Pillai U. S. (2002). Probability, Random Variables, and Stochastic Processes. 4th Edition, New York, NY: McGraw-Hill.

Ray, Asok (2004). "Symbolic dynamic analysis of complex systems for anomaly detection." Signal Processing (2004): 1115-1130.

Richard, Mark A., Scheer, James A., & Holm, William A. (2010). Principles of Modern Radar: Basic Principles. Raleigh, NC: SciTech Publishing, Inc.

Rutledge, D., Cheng, N., York, R. & Weikle II, R. (1999). "Failure of Power Combining Arrays." IEEE Transactions on Microwave Theory and Techniques 47.7 (1999): 1077-1082.

U.S. Air Force. Gotcha Volumetric SAR Data Set. Sensor Data Management System. August 2011. .
Section
Technical Papers