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Utilizing higher moments to detect time-varying target in radar echo with non-stationary background

Published online by Cambridge University Press:  13 February 2015

Renzhou Gui
Renzhou Gui*
Affiliation:
School of Electronics and Information Engineering, Tongji University, Shanghai 200092, Chaina
*
Corresponding author:R. Gui Email: [email protected]
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Abstract

Detecting time-varying target in non-stationary background is difficult and attractive problem. Time-varying movement exists widely in radar and communication systems. The non-linear processing with higher moments is discussed in the situation. Firstly the signal model of time-varying target with fix acceleration is analyzed. Then the radar echoes from synthetic aperture radar (SAR) are processed with higher moments. It not only restrains Gaussian noise automatically, but also suppresses non-stationary noise. Time-varying targets are different from the non-stationary background clutters. Moreover, the influences of the shadows about time-varying targets are reduced in the algorithm of higher moments. The proposal mentioned above utilizes higher moments to avoid analyzing the complex electromagnetic wave propagation and scatter theory. The differences of detection probability are compared between the chip with time-varying target and the clutter chip. The performances among different number order moments are compared by processing lots of actual SAR data added non-stationary noise. Lastly, the suitable number order moments are suggested by comparing the results from processing the actual radar echoes.

Keywords

Time-varying movementHigher momentsDetection probabilitySynthetic aperture radar
Type
Research Paper
Information
International Journal of Microwave and Wireless Technologies , Volume 8 , Issue 2 , March 2016 , pp. 215 - 219
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2015 

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References

REFERENCES

[1]Dakovicć, M.; Thayaparan, T. and Stankovicć, L.: Time–frequency-based detection of fast manoeuvring targets. IET Signal Process., 4 (3), (2010), 287–297.Google Scholar
[2]Bidon, S.; Savy, L. and Deudon, F.: Fast coherent integration for migrating targets with velocity ambiguity, in 2011 IEEE Radar Conf. (RADAR), vol., no., 027,032, 23–27 May 2011.Google Scholar
[3]Gui, R. and Yang, Z.: Application of Hopfield neural network for extracting Doppler spectrum from ocean echo. Radio Sci., 41 (4), (2006), RS4S90-1–6.Google Scholar
[4]Gui, R.Z.: Application of adaptive beamformer based on iterative weight algorithm to suppress interferences. Syst. Eng. Electron., v(30), n(4), (2008), 597–600.Google Scholar
[5]Gui, R.Z. and Yang, Z.J.: Research on the application of adaptive techniques in high frequency ground wave radar. Syst. Eng. Electron., v(28), n(2), (2006), 181–183 + 187.Google Scholar
[6]Tigrek, R.F. and van Genderen, P.: Compensation of range migration for cyclically Repetitive Doppler-Sensitive Waveform (OFDM). IEEE Trans. Aerosp. Electron. Syst., 46 (4) (2010), 2118–2123.Google Scholar
[7]Kronauge, M.; Schroeder, C. and Rohling, H.: Radar target detection and Doppler ambiguity resolution, in 2010 11th Int. Radar Symp. (IRS), vol., no., 1, 4, 16–18 June 2010.Google Scholar
[8]Boyer, R.: Performance bounds and angular resolution limit for the moving colocated MIMO radar. IEEE Trans. Signal Process., 59 (4) (2011), 1539–1552.Google Scholar
[9]Lu, Y.-b.; Zhang, L.-q.; Zhou, Y.-q. and Gao, H.-w.: Study on distributed aperture coherence-synthetic radar technology. J. Syst. Eng. Electron., 35 (8) (2013), 1657–1662.Google Scholar
[10]Shearman, E.D.R.: Propagation and scatting in MF/HF ground wave radar. IEE Proc., 130 (7), (1983), 579–590.Google Scholar
[11]Zhang, X.D. and Bao, Z.: Analysis and Processing of Non-Stationary Signal, National Defence Industry Press, Beijing, 1998.Google Scholar
[12]Raney, R.K.: Synthetic aperture imaging radar and moving targets. IEEE Trans. Aerosp. Electron. Syst., 7 ( 1971), 499–505.Google Scholar
[13]Soumekh, M.: Moving target detection in foliage using along track monopulse synthetic aperture radar imaging. IEEE Trans. Image Process., 6 (8) (1997), 1148–1163.Google Scholar
[14]Xue, M.; Jung, Y.S. and Zhang, G.F.: State estimation of convective storms with a two-moment microphysics scheme and an ensemble Kalman filter: experiments with simulated radar data. Q. J. R. Meteorol. Soc., 136 (468) (2010), 685–700.Google Scholar
[15]Grandi, G.D.; Lee, J.-S. and Schuler, D.: Target detection and texture segmentation in polarimetric SAR images using a wavelet frame: theoretical aspects. IEEE Trans. Geosci. Remote Sens., 45 (11) (2007), 3437–3453.Google Scholar
[16]Aiazzi, B.; Alparone, L. and Baronti, S.: Information-theoretic heterogeneity measurement for SAR imagery. IEEE Trans. Geosci. Remote Sens., 43 (3) (2004), 619–624.Google Scholar
[17]Greco, M.S. and Gini, G.: Statistical analysis of high-resolution SAR ground clutter data. IEEE Trans. Geosci. Remote Sens., 45 (3) (2007), 566–575.Google Scholar
[18]Chen, Q.; Haddar, H. and Lechleiter, A.: A sampling method for inverse scattering in the time domain. Inverse Problems, 26 (8) (2010), 085001(17pp).CrossRefGoogle Scholar
[19]Gui, R.: Detecting vehicle target in SAR image with high moment, in Radar Symposium (IRS), 2011 Proceedings International, Publication 2011, 391–395, September 2011.Google Scholar