Adaptive Anti-Jam Reduced-Rank Space-Time Preprocessor Algorithms for GPS

Wilbur L. Myrick, Michael D. Zoltowski and J. Scott Goldstein

Abstract:	Space-time preprocessors have a large dimensionality relative to spatial and temporal preprocessors. This translates into increased computational complexity and slower convergence. However, depending on the frequency and spatial distribution of the interferers, it may be possible to reduce the dimensionality of the preprocessor. Therefore, we present an innovative reducedrank space-time adaptive preprocessing algorithm based on the multistage nested Wiener filter (MSNWF). It is demonstrated that the MSNWF outperforms other reduced-ranking adaptive algorithms while maintaining low sample support and suppressing both wideband and narrowband jammers. Compared to other reduced-ranking methods such as Principal Components and Cross-Spectral, the MSNWF does not require matrix inversion or eigen-decomposition. Simulations are presented that illustrate the effectiveness of the reduced-rank algorithms at nulling both wideband and narrowband jammers.
Published in:	Proceedings of the 13th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 2000) September 19 - 22, 2000 Salt Palace Convention Center Salt Lake City, UT
Pages:	967 - 972
Cite this article:	Myrick, Wilbur L., Zoltowski, Michael D., Goldstein, J. Scott, "Adaptive Anti-Jam Reduced-Rank Space-Time Preprocessor Algorithms for GPS," Proceedings of the 13th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 2000), Salt Lake City, UT, September 2000, pp. 967-972.
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