GPR Data Processing Using the Component-Separation Methods PCA and ICA

This paper illustrates clutter reduction in stepped- frequency ground penetrating radar (SFGPR) data for anti- personal landmines detection. For the purpose of clutter reduc- tion, two subspace projection techniques have been studied and applied to experimental data, namely the principle component analysis (PCA) and the independent component analysis (ICA). Their output SNR have also been compared. These two algorithms have been applied and compared for experimental data set with non-metallic AP landmines. The experimental data were collected by using an SFGPR operating on the frequency range from 1 GHz to 20 GHz.

[1]  Jon Shlens,et al.  A TUTORIAL ON PRINCIPAL COMPONENT ANALYSIS Derivation , Discussion and Singular Value Decomposition , 2003 .

[2]  Håkan Brunzell,et al.  Detection of shallowly buried objects using impulse radar , 1999, IEEE Trans. Geosci. Remote. Sens..

[3]  V. Koshelev,et al.  Ultrawideband radiators of high-power pulses , 2001, PPPS-2001 Pulsed Power Plasma Science 2001. 28th IEEE International Conference on Plasma Science and 13th IEEE International Pulsed Power Conference. Digest of Papers (Cat. No.01CH37251).

[4]  J. Cardoso,et al.  Blind beamforming for non-gaussian signals , 1993 .

[5]  Terrence J. Sejnowski,et al.  Blind separation and blind deconvolution: an information-theoretic approach , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

[6]  Jean-François Cardoso,et al.  Equivariant adaptive source separation , 1996, IEEE Trans. Signal Process..

[7]  Abbas Omar,et al.  Combining wavelet packets with higher-order-statistic for GPR detection of non-metallic anti-personnel land mines , 2005, SPIE Remote Sensing.

[8]  Andrzej Cichocki,et al.  Adaptive Blind Signal and Image Processing - Learning Algorithms and Applications , 2002 .

[9]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[10]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[11]  G. Nadim,et al.  Wavelet packets for GPR detection of non-metallic anti-personnel land mines based on higher-order-statistic , 2005, Proceedings of the 3rd International Workshop on Advanced Ground Penetrating Radar, 2005. IWAGPR 2005..

[12]  Jean-Franois Cardoso.  High-Order Contrasts for Independent Component Analysis , 1999, Neural Computation.

[13]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[14]  G. Nadim,et al.  Clutter reduction and detection of landmine objects in ground penetrating radar data using singular value decomposition (SVD) , 2005, Proceedings of the 3rd International Workshop on Advanced Ground Penetrating Radar, 2005. IWAGPR 2005..

[15]  Elmar Wolfgang Lang,et al.  A neural implementation of the JADE algorithm (nJADE) using higher-order neurons , 2004, Neurocomputing.

[16]  Eric L. Miller,et al.  Statistical method to detect subsurface objects using array ground-penetrating radar data , 2002, IEEE Trans. Geosci. Remote. Sens..