О пеленгации источников излучений - page 24

13. S t o i c a P. a n d N e h o r a i A. MUSIC, maximum likelihood, and Cramer-Rao
bound // IEEE Trans. ASSP. May, 1989. – Vol. 37. – №. 5. – P. 720–741.
14. Г р е ш и л о в А. А. Математические методы принятия решений: Учеб. посо-
бие для вузов. – М.: Изд-во МГТУ им. Н.Э. Баумана, 2006.
15. M a l l a t S. A wavelet tour of signal processing. – Academic Press, 1998.
16. D o n o h o D. L., J o h n s t o n e I. M., K o c h J. C., S t e r n A. S. Maximum
entropy and the nearly black object // J. R. Statist. Soc. B. – 1992. – Vol. 54, №. 1. –
P. 41–81.
17. G o r o d n i t s k y I. F. a n d R a o B. D. Sparse signal reconstruction from
limited data using focus: a re-weighted minimum norm algorithm // IEEE Trans.
Signal Processing. Mar. 1997. – Vol. 45, №. 3. – P. 600–616.
18. C h e n S. S.,
D o n o h o D. L. a n d S a u n d e r s M. A. Atomic
decomposition by basis pursuit // SIAM J. Scientific Computing. – 1998. – Vol. 20,
№. 1. – P. 33–61.
19. F u W. J. Penalized regressions: the bridge versus the LASSO // Journal of
Computational and Graphical Statistics. – Sept. 1998. – Vol. 7, №. 3.
20. G e m a n D. a n d Y a n g C. Nonlinear image recovery with half-quadratic
regularization // IEEE Trans. Image Processing. – July, 1995. – Vol. 4, №. 7. P. 932–
946.
21. M i l l e r A. J. Subset Selection in Regression. – Chapman and Hall, 2002.
22. T i b s h i r a n i R. Regression shrinkage and selection via the LASSO // Journal
of Royal Statistical Society, Series B. – Nov., 1996. – Vol. 58. – P. 267–288.
23. C e t i n M. a n d K a r l W. C. Feature-enhanced synthetic aperture radar image
formation based on nonquadratic regularization // IEEE Trans. Image Processing. –
Apr. 2001. – Vol. 10. – №. 4. – P. 623–631.
24. M a n g a s a r i a n O. L. a n d M u s i c a n t D. R. Robust linear and support
vector regression // IEEE Trans. Pattern Anal. Machine Intell. – 2000. – Vol. 22, №. 9.
– P. 950–955.
25. V o g e l C. R. a n d O m a n M. E. Fast, robust total variation-based
reconstruction of noisy, blurred images // IEEE Trans. Image Processing. Jun, 1998.
– Vol. 7, №. 6. – P. 813–824.
26. G e m a n D. a n d R e y n o l d s G. Constrained restoration and the recovery
of discontinuities // IEEE Trans. Pattern Anal. Machine Intell. Mar., 1992. – Vol. 14,
№. 3. P. 367–383.
27. H o e r l A. E. a n d K e n n a r d R. W. Ridge regression: Biased estimation
for nonorthogonal problems // Technometrics. – 1970. – Vol. 12, №. 3. – P. 55–67.
28. C h a r b o n n i e r P.,
B l a n c - F e r a u d L.,
A u b e r t
G.
a n d B a r l a u d M. Deterministic edge-preserving regularization in computed
imaging // IEEE Trans. Image Processing. – Feb. 1997. – Vol. 6, №. 2. – P. 298–310.
29. D e l a n e y A. H. a n d B r e s l e r Y. Globally convergent edge-preserving
regularized reconstruction: an application to limited-angle tomography // IEEE Trans.
Image Processing. – Feb., 1998. – Vol. 7, №.2. – P. 204–221.
30. M a l i o u t o v D. M. A sparse signal reconstruction perspective for source
localization with sensor arrays // Master of Science thesis, Massachusetts Institute of
Technology, 2003.
Статья поступила в редакцию 19.01.2007
26
ISSN 1812-3368. Вестник МГТУ им. Н.Э. Баумана. Сер. “Естественные науки”. 2007. № 3
1...,14,15,16,17,18,19,20,21,22,23 25
Powered by FlippingBook