volume | PIER Journals

2005-11-19

Detection and Localization of RF Radar Pulses in Noise Environments Using Wavelet Packet Transform and Higher Order Statistics

Omar A. Alyt,

Dr. Omar A. Alyt

Department of Microwave and Communication Engineering

University of Magdeburg

Germany

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Abbas Omar,

Prof. Abbas Omar

Chair of Microwave and Communication Engineering

University of Magdeburg

Germany

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Atef Elsherbeni

Professor Atef Elsherbeni

Electrical Engineering Department

University of Mississippi

USA

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, Vol. 58, 301-317, 2006

doi:10.2528/PIER05070204

Abstract

Weak signal detection and localization are basic and important problems in radar systems. Radar performance can be improved by increasing the receiver output signal-to-noise ratio (SNR). Localizing the received signal is an important task in the detection of signal in noise. Distorting the localization of the received signal can leads to incorrect target range measurements. In this paper an algorithm is described for extracting and localizing an RF radar pulse from a noisy background. The algorithm combines two powerful tools: the wavelet packet analysis and higher-order-statistics (HOS). The use of the proposed technique makes detection and localization of RF radar pulses possible in very low signal-to-noise ratio conditions, which leads to a reduction of the required microwave power or alternatively extending the detection range of radar systems.

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Omar A. Alyt, Abbas Omar, and Atef Elsherbeni, "Detection and Localization of RF Radar Pulses in Noise Environments Using Wavelet Packet Transform and Higher Order Statistics," , Vol. 58, 301-317, 2006.
doi:10.2528/PIER05070204

References

1. Abbate, C. M. and P. K. Das, Wavelets and Subbands Fundamentals and Applications, Birkhäuser Bosten, 2002.

2. Nikias, L. and A. P. Petropulu, Higher-Order Spectra Analysis: A nonlinear signal processing framework, PTR Prentice Hall, 1993.

3. Sobhy, M. I., K. H. Moustafa, and M. Y. Makkey, "Real-time processing of noisy RF pulses," Proceeding of the 32nd European Microwave Conference, No. 9, 2001.

4. Donoho, D. L., "Denoising by soft-thresholding," IEEE Transactions on Information Theory, Vol. 41, No. 3, 613-627, 1995.
doi:10.1109/18.382009

5. Childers, G., Probability and Random Processes, McGraw-Hill, 1997.

6. Ravier, P. and P. O. Amblard, "Wavelet packets and denoising based on higher-order-statistics for transient detection," Signal Processing, Vol. 81, No. 9, 1909-1926, 2001.
doi:10.1016/S0165-1684(01)00088-3

7. Elsehely, E. and M. I. Sobhy, "Reduction of interference in microwave automotive radars," IEEE, No. 6, 2000.

8. Skolnik, M. I., Introduction to Radar Systems, 3rd edition, 2001.

9. Yoshida, Radar Technology, Radar Technology, I.E.C.E. Publ., 1984.

10. Huether, B. M., A. C. Gustafson, and R. P. Broussard, "Wavelet preprocessing for high range resolution radar classification," IEEE Transaction on Aerospace and Electronic Systems, Vol. 37, No. 4, 1321-1331, 2001.
doi:10.1109/7.976968

11. Ehara, N., I. Sasase, and S. Mori, "Weak radar signal detection based on wavelet transform," Electronics and Communications in Japan, Vol. 77, No. 8, 105-114, 1994.

12. Malat, S. G., "A theory for multiresolution signal decomposition," IEEE Transaction on Pattern Analysis and Machine Intel ligence, Vol. 11, No. 7, 674-693, 1989.
doi:10.1109/34.192463

13. Akansu, A. N. and R. A. Haddad, Multiresolution Signal Decomposition: Transforms, subbands, 1992.

14. Donoho, D. L. and I. Johnstone, "Ideal spatial adaptation by wavelet shrinkage," Biometika, Vol. 81, 425-455, 1994.
doi:10.2307/2337118

15. Daubechies, I., Ten Lectures on Wavelets, SIAM, 1992.