Evolutions of mathematical statistics and corrections to CFAR and STAP theories

Author

Shirman, Y.D. ; Orlenko, V.M.

Author_Institution

Kharkov Univ. of Air Forces, Kharkov

fYear

2008

fDate

21-23 May 2008

Firstpage

1

Lastpage

6

Abstract

Since Fisherpsilas maximum likelihood (ML) method is correct only for asymptotically great number of samples, the exile of Bayessian approach from mathematical statistics (MS) hampered development of CFAR and STAP theories. Many heuristic corrections to these theories appeared therefore. But, it seems better to begin creating the generalized Bayessian theory, providing the processing algorithms for fast varying conditions. The new Pareto-Gaussian a priory model of total interference (TI) intensity is therefore reasoned. Investigation of its use in CFAR and STAP theories is begun. The contours are outlined of future combination of such theories with fast progress in ldquoknowledge aided signal processingrdquo.

Keywords

Bayes methods; space-time adaptive processing; Bayessian theory; CFAR theory; Pareto-Gaussian a priory model; STAP theory; heuristic corrections; total interference intensity; Adaptive signal processing; Covariance matrix; Information theory; Interference; Pareto analysis; Probability; Radar; Random processes; Signal processing algorithms; Statistics; constant falls alarm rate (CFAR); limited falls alarm rate (LFAR); space time adaptive processing (STAP);

fLanguage

English

Publisher

ieee

Conference_Titel

Radar Symposium, 2008 International

Conference_Location

Wroclaw

Print_ISBN

978-83-7207-757-8

Type

conf

DOI

10.1109/IRS.2008.4585697

Filename

4585697