Acta Optica Sinica, Volume. 31, Issue 5, 510004(2011)
Resolution of Closely Spaced Objects via Infrared Focal Plane Using Reversible Jump Markov Chain Monte-Carlo Method
[1] [1] S. Blackman, R. Popoli. Design and Analysis of Modern Tracking Systems[M]. Norwood: Artech House, 1999
[2] [2] Sabino Gadaleta, Allison Floyd, Benjamin J. Slocumb. Pixel-cluster decomposition tracking for multiple IR-sensor surveillance[C]. SPIE, 2003, 5204: 270~281
[3] [3] Daniel Macumnber, Sabino Gadaleta, Allison Floyd et al.. Hierarchical closely-spaced objects(CSO) resolution for IR sensor surveillance[C]. SPIE, 2005, 5913: 591304
[5] [5] Jonathan Korn, Howard Holtz, Morton S. Farber. Trajectory estimation of closely spaced objects (CSO) using infrared focal plane data of an STSS (space tracking and surveillance system) platform [C]. SPIE, 2004, 5428: 387~399
[6] [6] J. T. Reagn, Theagenis J. Abatzoglou. Model-based superresolution CSO processing[C]. SPIE, 1993, 1954: 204~218
[8] [8] Nielson W. Schulenburg, John A. Hackwell. Bayesian approach to image recovery of closely spaced objects[C]. SPIE, 1993, 1954: 82~93
[9] [9] Walter E. Lillo, Nielson W. Schulenburg. Bayesian closely spaced object resolution with application to real data[C]. SPIE, 2002, 4729: 152~162
[10] [10] Liu Tao, Chen Hao, Jiang Weidong et al.. A Gibbs sampling approach to closely spaced objects resolution via IR focal plane[J]. Signal Processing, 2010, 26(8): 1193~1199
[11] [11] M. Wax, T. Kailath. Detection of Signals by information theoretic criteria[J]. IEEE Transaction on Acoustic, Speech and Signal Processing, 1985, 33(2): 387~392
[12] [12] Rafael A. Irizarry. Information and posterior probability criteria for model selection in local likelihood estimation[J]. J. American Statistical Association, 2001, 96(453): 303~315
[13] [13] Peter J. Green. Reversible jump markov chain Monte Carlo computation and Bayesian model determination[J]. Biometrika, 1995, 84(2): 711~732
[14] [14] Christophe Andrieu, Arnaud Doucet. Joint Bayesian model selection and estimation of noisy sinusoids via reversible jump MCMC[J]. IEEE Transactions on Signal Processing, 1999, 47(10): 2667~2676
[15] [15] C. Andrieu, JFG de Freitas, A Goucet. Robust full Bayesian learning for neural networks[R]. Technical Report CUED/F-INFENG/TR 343, 1999, Cambridge University Engineering Department, England
[16] [16] Ana S.Lukic, Miles N. Wernick, Nikolas P. Galatsanos et al.. Reversible jump markov chain Monte Carlo signal detection in functional neuroimaging analysis [C]. IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2004, 868~871
[17] [17] N. William, J. P. Reilly. Wideband array signal processing using MCMC methods[J]. IEEE Transactions on Signal Processing, 2005, 53(2): 411~426
[18] [18] Minghui Li, Yilong Lu. Maximum likelihood DOA estimation in unknown colored noise fields[J]. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(3): 1079~1090
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Lin Liangkui, Xu Hui, Xu Dan, An Wei. Resolution of Closely Spaced Objects via Infrared Focal Plane Using Reversible Jump Markov Chain Monte-Carlo Method[J]. Acta Optica Sinica, 2011, 31(5): 510004
Category: Image Processing
Received: Dec. 2, 2010
Accepted: --
Published Online: May. 9, 2011
The Author Email: Liangkui Lin (kk2buaa@163.com)