volume | PIER Journals

Abstract

The accuracy of scattering measurements in near-field millimeter-wave cylindrical scanning imaging system is often degraded by the contamination from additive noise and clutter. Thus, efficient noise removal technique is necessary to achieve accuracy improvement. This paper proposes an independent component analysis denoising algorithm, which relies on the assumption of statistical independence of the sources, where high order statistical properties are used. In the algorithm, the virtual noise components are incorporated into the independent component analysis model, which expands original one-dimensional observation to virtual multi-dimensional observations. The computationally efficient sources estimation technique is presented, based of joint diagonalization of fourth order cumulant matrix. The high speed millimeter-wave near-field cylinder scanning imaging system is set up to verify the denoising results of range profiles, three-dimensional scatter intensity and two-dimensional projection images. The results indicate both the feasibility and validity of the proposed denoising algorithm to be applied in the near-field millimeter-wave cylindrical scanning imaging system.

1. Soumekh, M., "Reconnaissance with slant plane circular SAR imaging," IEEE Trans. on Image Processing, Vol. 5, No. 8, 1252-1265, 1996.
doi:10.1109/83.506760 Google Scholar

2. LaHaie, I. J. and E. I. LeBaron, "Processing techniques for removal of target support contamination," AP-S. Digest Antennas and Propagation Society International Symposium, Vol. 1, 488-491, 1993.
doi:10.1109/APS.1993.385301 Google Scholar

3. Burns, J. W. and N. S. Subotic, "Reduction of clutter contamination in radar cross section measurements using independent components analysis," IEEE Antennas and Propagation Society International Symposium, Vol. 1, 731-734, 2004. Google Scholar

4. LaHaie, I. J., "An overview of advanced processing techniques for RCS measurements," IEEE Antennas and Propagation Society International Symposium, Vol. 4, 608-611, 2001. Google Scholar

5. Kristensson, G. and M. Gustafsson, "Measuring the extinction cross section," 3rd European Conference on Antennas and Propagation, EuCAP 2009, 3633-3636, 2009. Google Scholar

6. Hilliard, D. and L. To, "Advanced radar cross section clutter removal algorithms," 2010 Proceedings of the Fourth European Conference on Antennas and Propagation (EuCAP), 1-5, 2010. Google Scholar

7. Piere, C., "Independent component analysis, a new concept," Signal Processing — Special Issue on Higher Order Statistics, Vol. 36, No. 3, 287-314, 1994. Google Scholar

8. Miller, E. G. and W. John, "ICA using spacings estimates of Entropy," Journal of Machine Learning Research, Vol. 4, No. 7–8, 1271-1295, 2003. Google Scholar

9. Cardoso, J. F. and A. Souloumiac, "Blind beamforming for non-Gaussian signals," IEE Proceedings — Radar and Signal Processing, Vol. 140, No. 6, 362-370, 1993.
doi:10.1049/ip-f-2.1993.0054 Google Scholar

10. Murillo-Fuentes, J. J. and F. J. Gonzalez-Serrano, "Independent component analysis with sinusoidal fourth-order contrasts," Acoustics, Speech, and Signal Processing, Vol. 5, 2785-2788, 2001. Google Scholar

11. Hyvarinen, A., "Survey on independent component analysis," Neural Computing Surveys, Vol. 2, 94-128, 1999. Google Scholar

12. Hyvarinen, A. and E. Oja, "Independent component analysis: Algorithms and application," Neural Networks, Vol. 13, 411-430, 2000.
doi:10.1016/S0893-6080(00)00026-5 Google Scholar

13. Hyvarinen, A., "Gaussian moments for noisy independent component analysis," IEEE Signal Processing Letters, Vol. 6, No. 6, 145-147, 1999.
doi:10.1109/97.763148 Google Scholar

14. Giannakis, G. and S. Shamsunder, "Modelling of non-Guassian array data using cumulants: DOA estimation of more sources with less sensors," Signal Processing, Vol. 30, No. 3, 279-297, 1993.
doi:10.1016/0165-1684(93)90014-2 Google Scholar

15. Jiao, W. D. and S. X. Yang, "Study of noise removal techniques based on independent component analysis," Journal of Zhejiang University, Vol. 38, No. 7, 872-876, 2004. Google Scholar

Dr. Xin Wen

Dr. Feng Nian

Dr. Yujie Yang

Dr. Keming Fen