Data Analysis and Application Study



The active sonar detection usually includes the following: beamforming, a matched filter and normalization. They explore the geometrical information of the array, correlation between the target echo and the transmitted echo, and numerical smoothing respectively. Although these methods are versatile, researchers try to find better detection solutions under heavy reverberation, for example, to use longer and larger arrays, to cancel more reverberation, to design better matched filters and so on. Currently, reverberation models and specialized processing methods are two attractive techniques··1··. As for the first method, receivers with differing range resolutions may encounter different statistics for a given waveform. Furthermore, a given receiver may encounter different statistics at different ranges··2··. The Weibull, log-normal, Rician, multimodal Rayleigh and non-Rayleigh distributions have been used to describe sonar reverberation··3, 4··. The matched filter is the optimal detector with the background of Gaussian noise; therefore, these studies imply that it is necessary to reduce the reverberation before the signal is fed into the matched filter. Hence, the second method aims to cancel reverberation. Reverberation can be assumed to be a sum of returns issued from the transmitted signal, and a data matrix may be generated under the segmentation of the data received by active sonar··5,6··. Then the principal component inverse (PCI)··7–13·· can be applied to separate reverberation and target echo with the data matrix.


Face Recognition Independent Component Analysis Speech Signal Independent Component Analysis Blind Source Separation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [1]
    Barnard T J, Khan F (2004) Statistical normalization of spherically invariant non-Gaussian clutter. IEEE Journal of Oceanic Engineering 29(2): 303–309CrossRefGoogle Scholar
  2. [2]
    Fialkowski J M, Gauss R C, Drumheller D M (2004) Measurements and modelling of low-frequency near-surface scattering statistics. IEEE Journal of Oceanic Engineering 29(2): 197–214CrossRefGoogle Scholar
  3. [3]
    LePage K D (2004) Statistics of broad-band bottom reverberation predictions in shallow-water waveguides. IEEE Journal of Ocean Engineering 29(2): 330–346CrossRefGoogle Scholar
  4. [4]
    Preston J R, Abraham D A (2004) Non-Rayleigh reverberation characteristics near 400 Hz observed on the New Jersey shelf. IEEE Journal of Oceanic Engineering 29(2): 215–235CrossRefGoogle Scholar
  5. [5]
    Kirsteins I P, Tufts D W (1994) Adaptive detection using low rank approximation to a data matrix. IEEE Transactions on Aerospace and Electronic Systems 30(1): 55–67CrossRefGoogle Scholar
  6. [6]
    Kirsteins I P, Tufts D W (1989) Rapidly adaptive nulling of interference. In: IEEE International Conference on Systems Engineering Fairborn, 1989, pp 269–272Google Scholar
  7. [7]
    Freburger B E, Tufts D W (1998) Case study of principal component inverse and cross spectral metric for low rank interference adaptation In: Proceedings of ICASSP’ 98, 1998, 4: 1977–1980Google Scholar
  8. [8]
    Freburger B E, Tufts D W (1998) Adaptive detection performance of principal components inerse, cross spectral metric and the partially adaptive multistage Wiener filter. In: Signals, Systems & Computers, 1998. Conference Record of the Thirty-Second Asilomar Conference. Pacific Grove, 1998, 2: 1522–1526Google Scholar
  9. [9]
    Freburger B E, Tufts D W (1997) Rapidly adaptive signal detection using the principal component inverse (PCI) method. In: Conference Record of the Thirty-First Asilomar Conference on Signals, Systems & Computers, Pacific Grove, 1997, 1:765–769Google Scholar
  10. [10]
    Palka T A, Tufts D W (1998) Reverberation characterization and suppression by means of principal components. In: OCEANS’ 98 Conference Proceedings, Nice, 1998, 3: 1501–1506Google Scholar
  11. [11]
    Ginolhac G, Jourdain G (2002) Principal component inverse algorithm for detection in the presence of reverberation. IEEE Journal of Oceanic Engineering 27(2): 310–321CrossRefGoogle Scholar
  12. [12]
    Ginolhac G, Jourdain G (2000) Detection in presence of reverberation. In: OCEANS 2000 MTS/IEEE Conference and Exhibition, Providence, 2000, 2:1043–1046Google Scholar
  13. [13]
    Carmillet V, Amblard P O, Jourdain G (1999) Detection of phase-or frequencymodulated signals in reverberation noise. JASA 105(6):357–389CrossRefGoogle Scholar
  14. [14]
    Bell A J, Sejnowski T J (1995) An information-maximization approach to blind separation and blind deconvolution. Neural Computation 7(6):1129–1159CrossRefGoogle Scholar
  15. [15]
    Cong F Y (2005) Blind signal separation and reverberation cancelling with active sonar data. In: Proceedings of ISSPA 2005, Sydney, 2005, pp 523–526Google Scholar
  16. [16]
    Edelson G S, Kirsteins I P (1994) Modeling and suppression of reverberation components. In: IEEE Seventh SP Workshop on Statistical Signal and Array Processing, Quebec City, 1994, pp 437–440Google Scholar
  17. [17]
    Hyvärinen A (1999) Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions Neural Networks 10(3): 626–634CrossRefGoogle Scholar
  18. [18]
    Abraham D A, Lyons A P (2004) Simulation of non-Rayleigh reverberation and clutter. IEEE Journal of Oceanic Engineering 29(2): 347–362CrossRefGoogle Scholar
  19. [19]
    Cong F Y, Chen C H, Ji S (2004) Approach based on colored character to blind deconvolution for speech signals. In: Proceedings of ICIMA 2004, Chengdu, 2004, pp 396–399Google Scholar
  20. [20]
    Sawada H, Mukai R, Araki S et al (2004) A robust and precise method for solving the permutation problem of frequency-domain blind source separation. IEEE Transactions on Speech and Audio Processing 12(5): 530–538CrossRefGoogle Scholar
  21. [21]
    Ikram M Z, Morgan D R (2005) Permutation inconsistency in blind speech separation: Investigation and solutions. IEEE Transactions on Speech and Audio Processing 13(1): 1–13CrossRefGoogle Scholar
  22. [22]
    Lu W, Rajapakse J C (2003) Eliminating indeterminacy in ICA. Neurocomputing 50(1): 271–290zbMATHCrossRefGoogle Scholar
  23. [23]
    Ikram M Z (2001) Multichannel blind separation of speech signals in a reverberant environment. Dissertation, Georgia Institute of TechnologyGoogle Scholar
  24. [24]
    Vigario R N (2002) Extraction of ocular artefacts from EEG using independent component analysis. Electroencephal. Clin. Neurophysiol 103: 395–404CrossRefGoogle Scholar
  25. [25]
    Tong S B, Bezerianos A, Paul J (2001) Removal of ECG intererence from the EEG recordings in small animals using independent component analysis. Journal of Neuroscience Methods 108: 11–17CrossRefGoogle Scholar
  26. [26]
    Tong S B (2002) Nonextensive entropy analysis of electroencephalogram and its application study in monitoring brain injury from cardiac arrest. Dissertation, Shanghai Jiao Tong University (Chinese)Google Scholar
  27. [27]
    Zhang Y Y (2006) Algorithm research on blind signal processing over underdetermined model. Dissertation, Shanghai Jiao Tong University (Chinese)Google Scholar
  28. [28]
    Hu Y (2003) Research on blind source separation and its applications in image processing. Dissertation, Shanghai Jiao Tong University (Chinese)Google Scholar
  29. [29]
    Samal A, Iyengar P (1992) Automatic recognition and analysis of human faces and facial expression:A survey. Pattern Recognition 25(1): 65–77CrossRefGoogle Scholar
  30. [30]
    Cottrell G, Metcalfe J (1991) Face, gender and emotion recognition using holons. In: Advances in Neural Information Processing Systems. D. Touretzky. Morgan Kaufmann, San Mateo, CA, 1991, 3: 564–571Google Scholar
  31. [31]
    Turk M, Pentland A (1991) Eigenfaces for recognition. Journal of Cognitive Neuroscience 13(1): 71–86CrossRefGoogle Scholar
  32. [32]
    Penev P S, Atick J J (1996) Local feature analysis: A general statistical theory for object Representation. Network: Comput. Neural Syst. 7(3): 477–500zbMATHCrossRefGoogle Scholar
  33. [33]
    Bartlett M S, Sejnowski T J (1997) Viewpoint invariant face recognition using independent component analysis and attractor networks. In: M. Mozer, M. Jordan and T. Petsche (eds) Advances in Neural Information Processing Systems. The MIT Press, Cambridge, MA, 1997, 9: 817–823Google Scholar
  34. [34]
    Liu C, Wechsler H (1999) Comparative assessment of independent component analysis (ICA) for face recognition. In: International Conference on Audio and Video Based Biometric Person Authentication, Washington, 1999Google Scholar
  35. [35]
    Yuen P C, Lai J H (2000) Independent component analysis of face images. In: IEEE Workshop on Biologically Motivated Computer Vision, Seoul, 2000Google Scholar
  36. [36]
    Bartlett M S, Movellan J R, Sejnowski T J (2002) Face recognition by independent component analysis. IEEE Transactions on Neural Networks 13 (6): 1450–1464Google Scholar
  37. [37]
    Draper B A, Baek K, Bartlett M S (2003) Recognizing faces with PCA and ICA. Computer Vision and Image Understanding 91(1–2): 115–137CrossRefGoogle Scholar
  38. [38]
    Ferreira A, Figueiredo M (2003) Class-adapted image compression using independent component analysis. In: IEEE International Conference on Image Processing-ICIP’2003, Barcelona, 2003Google Scholar
  39. [39]
    Ferreira A, Figueiredo M (2003) Image compression using orthogonalised independent components bases. In: Proceedings of the IEEE Neural Networks for Signal Processing Workshop, Toulouse, 2003Google Scholar
  40. [40]
    Puga A T, Alves A P (1998) An experiment on comparing PCA and ICA in classical transform image coding. In: Proceedings of Independent Component Analysis (ICA’98), Aussois, 1998, pp 105–108Google Scholar
  41. [41]
    Wang Z (2003) Data compression in medical virtual reality. Dissertation, Shanghai Jiao Tong University (Chinese)Google Scholar
  42. [42]
    Wong T T, Fu C W, Heng P A (2001) Interactive relighting of panoramas. IEEE Computer Graphics and Applications 21(1): 32–41CrossRefGoogle Scholar
  43. [43]
    Wong T T, Fu C W, Heng P A et al (2002) The plenoptic illumination function. IEEE Transactions. on Multimedia 4(3): 361–371CrossRefGoogle Scholar
  44. [44]
    Barlow H B (1961) Possible principles underlying the transformation of sensory messages. In: W.A. Rosenbluth (ed) Sensory Communication. The MIT Press, Cambridge, pp 217–234Google Scholar
  45. [45]
    Attneave F (1954) Some informational aspects of visual perception. Psychol. Rev. 61:183–193CrossRefGoogle Scholar
  46. [46]
    van Hateren J H (1992) A theory of maximizing sensory information. Biol. Cybern. 68(1): 23–29zbMATHCrossRefGoogle Scholar
  47. [47]
    Field D J (1994) What is the goal of sensory coding? Neural Computation 6: 559–601CrossRefGoogle Scholar
  48. [48]
    Field D J (1995) Visual coding, redundancy, and feature detection. In: Arbibm (ed) The Handbook of Brain Theory and Neural Networks. The MIT Press, Cambridge, MAGoogle Scholar
  49. [49]
    Olshausen B A, Field D J (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381: 607–609CrossRefGoogle Scholar
  50. [50]
    Wang Z, Leung C S, Zhu Y S (2004) Eigen-image based compression for the image-based relighting with cascade recursive least squared networks. Pattern Recognition 37(6): 1219–1231zbMATHCrossRefGoogle Scholar
  51. [51]
    Nimeroff J S, Simoncelli E, Doresy J (1994) Efficient re-rendering of naturally illuminated environments. In: Fifth Eurographics Workshop on Rendering, Darmstadt, 1994, pp 359–373Google Scholar
  52. [52]
    Wang Z, Lee Y, Zhu Y S (2003) An improved optimal bit allocation method for sub-band coding. Pattern Recognition Letters 24(16): 3007–3013CrossRefGoogle Scholar
  53. [53]
    Frackowiak R S J, Friston K J, Frith C D et al (1997) Human brain function. Academic Press, San DiegoGoogle Scholar
  54. [54]
    Roy C S, Sherrington C S (1980) On the regulation of the blood-supply of the brain. The Journal of Physiology 11: 85–108Google Scholar
  55. [55]
    Plum F, Posner J, Troy B (1968) Cerebral metabolic and circulatory responses to induced convulsions in animals. Arch Neurol 18(1): 1–13CrossRefGoogle Scholar
  56. [56]
    Posner J, Plum F, Troy B (1969) Cerebral metabolism during electrically induced seizures in man. Arch Neurol 20: 388–395CrossRefGoogle Scholar
  57. [57]
    Fox P T, Raichle M E (1985) Stimulus rate determines regional brain blood flow in striate cortex. Annals of Neurology 17(3): 303–305CrossRefGoogle Scholar
  58. [58]
    Belliveau J, Rosen B, Kantor H (1990) Functional cerebral imaging by susceptibility-contrast NMR. Magnetic Resonance in Medicine 14: 538–546CrossRefGoogle Scholar
  59. [59]
    Ogawa S, Lee T M, Kay A (1990) Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci 87: 9868–9872CrossRefGoogle Scholar
  60. [60]
    Belliveau J, Kennedy D, Mckinstry R (1991) Functional mapping of the human visual cortex by magnetic resonance imaging. Science 254: 716–719CrossRefGoogle Scholar
  61. [61]
    Ogawa S, Tank D, Menon R (1992) Intrinsic signal changes accompanying sensory stimulation: Functional brain mapping with magnetic resonance imaging. Natl. Acad. Sci. Proc. 89: 5951–5955CrossRefGoogle Scholar
  62. [62]
    Ogawa S, Menon R S, Tank D (1993) Functional brain mapping by blood oxygenation level-dependent contrast magnetic resonance imaging: A comparison of signal characteristics with a biophysical model. Biophys. J. 64: 803–812CrossRefGoogle Scholar
  63. [63]
    Frackowiak R S J, Friston K J, Frith C D et al (1997) Human Brain Function. Academic Press, San DiegoGoogle Scholar
  64. [64]
    McKeown M J, Sejnowski T J (1998) Independent component analysis of fMRI data: Examining the assumptions. Human Brain Mapping 6(5–6): 368–372CrossRefGoogle Scholar
  65. [65]
    Biswal B B, Ulmer J L (1999) Blind source separation of multiple signal sources of fMRI data sets using independent component analysis. Journal of Computer Assisted Tomography 23(2): 265–271CrossRefGoogle Scholar
  66. [66]
    Calhoun V D, Adali T, Pearlson G et al (2001) Spatial and temporal independent component analysis of functional mri data containing a pair of task-related waveforms. Hum Brain Map 13: 43–53CrossRefGoogle Scholar
  67. [67]
    Cichocki A, Kasprzak W, Skarbek W (1996) Adaptive learning algorithm for principal component analysis with partial data. Proc Cybernetics Syst 2:1014–1019Google Scholar
  68. [68]
    Wang Z, Lee Y, Zhu Y S et al (2003) An improved sequential method for principal component analysis. Pattern Recognition Letters 24(9–10): 1409–1415zbMATHCrossRefGoogle Scholar
  69. [69]
    Wang Z, Wang J J, Childress A (2005) CRLS-PCA based independent component analysis for fMRI study. In: 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Shanghai, 2005Google Scholar
  70. [70]
    Wang Z, Lee T, Fiori S (2003) An improved sequential method for principal component analysis. Pattern Recognition Letters 24(9–10): 1409–1415zbMATHCrossRefGoogle Scholar
  71. [71]
    Junqua J C, Haton J P (1995) Robustness in automatic speech recognition: Fundamentals and applications. Kluwer Academic, BostonGoogle Scholar
  72. [72]
    Choi S, Hong H (2002) Multichannel signal separation for cocktail party speech recognition: A dynamic recurrent Network. Neurocomput 49(4): 299–314zbMATHCrossRefGoogle Scholar
  73. [73]
    Parra, Spence C (2000) Convolutive blind separation of nonstationary sources. IEEE Transactions Speech Audio Process 8(3): 320–327CrossRefGoogle Scholar
  74. [74]
    Kawamoto M, Matsuoka K, Ohnishi N (1998) A method of blind separation for convolved non-stationary signals. Neurocomputing 22(1–3): 157–171zbMATHCrossRefGoogle Scholar
  75. [75]
    Buchner H, Aichner R, Kellermann W (2005) A generalization of blind source separation algorithms for convolutive mixtures based on secondorder statistics. IEEE Transactions on Speech and Audio Processing 13(1): 120–134CrossRefGoogle Scholar
  76. [76]
    Saruwatari H, Kawamura T, Nishikawa T et al (2006) Blind source separation based on algorithm fast-convergence algorithm combining ICA and beamforming. IEEE Transactions on Speech and Audio Processing 14(2): 666–678CrossRefGoogle Scholar
  77. [77]
    Matsuoka K, Nakashima S (2001) Minimal distortion principle for blind source separation. In: Proc. ICA 2001, San Diego, 2001, pp 722–727Google Scholar
  78. [78]
    Kolossa D, Orglmeister R (2004) Nonlinear postprocessing for blind speech separation. In: ICA 2004, Berlin, 2004, pp 832–839Google Scholar
  79. [79]
    Kokkinakis K, Nandi A K (2006) Multichannel blind deconvolution for source separation in convolutive mixtures of speech. IEEE Transactions on Speech and Audio Processing 14(1):200–212CrossRefGoogle Scholar
  80. [80]
    James C J (2005) Independent component analysis for biomedical signals. Physiol. Me. 26: R15–39CrossRefGoogle Scholar
  81. [81]
    Kong W, Vanderburg C R, Gunshin H et al (2008) A review of independent component analysis application to microarray gene expression data. Biotechniques 45(5):501–520CrossRefGoogle Scholar
  82. [82]
    Lee S I, Batzoglou S (2003) Application of independent component analysis to microarrays. Genome Biology 4(11):R76.1–R76.21CrossRefGoogle Scholar
  83. [83]
    Schachtner R, Lutter D, Theis F J et al (2007) How to extract marker genes from microarray data sets. In: Proceedings of the 29th Annual, Lyon, 2007, pp 4215–4218Google Scholar
  84. [84]
    Chinappetta P, Roubaud M C, Torrésani B (2004) Blind source separation and the analysis of microarray data. Journal of Computational Biology 11(6): 1090–1109CrossRefGoogle Scholar
  85. [85]
    Himberg J, Hyvärinen A, Esposito F (2004) Validating the independent components of neuroimaging time-series via clustering and visualization. NeuroImage 22: 1214–1222CrossRefGoogle Scholar

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© Shanghai Jiao Tong University Press, Shanghai and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Institute of Vibration Shock & NoiseShanghai Jiao Tong UniversityShanghaiChina

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