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Published in:
2013 | OriginalPaper | Chapter
12. Study on Gaussian Priory Model for Space Time Adaptive Processing
Author : Jiamou Wang
Published in: Proceedings of the 2nd International Conference on Green Communications and Networks 2012 (GCN 2012): Volume 3
Publisher: Springer Berlin Heidelberg
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Abstract
Exile of Bayesians approach from mathematical statistics hampered development of stable theory of CFAR and STAP. Many heuristic corrections appeared therefore to various aspects of these theories. But, it seems better to begin creating the generalized Bayesians theory, following which one can obtain the processing algorithms in fast varying conditions without corrections. A priory statistical model of TI was reasoned with this purpose as the Pareto-Gaussian (PG) process belonging to SIRP ones. Preference of PG process consists of its simplicity and compatibility with CFAR -STAP problems. The PG model allows accounting for: (1) (Quasi Gaussian TI of highest entropy \( (\eta = 0) \); (2) essentially non Gaussian TI \( (\eta < - 1/ 2 ) \).