A circular convolution based on compressed sensing imaging algorithm for FMCW CSAR
Pages 187 - 196
Abstract
The frequency modulated continuous wave circular synthetic aperture radar (FMCW CSAR) is a high resolution imaging radar, which plays an important role in target recognition. However, the traditional wave number domain imaging algorithm has a low resolution for the target far from the scene center, which limits its wide applications in CSAR imaging. In this paper, when the targets are sparse or compressible, a circular convolution algorithm based on compressed sensing is proposed to improve the resolution. In the algorithm, the circular convolution and the Fourier transform is used to reduce computation cost. What’s more, the compressed sensing is applied to improve the imaging resolution for the target far from the scene center, which can effectively avoid the complex calculation of the system kernel function in the traditional wave number domain algorithm. Some simulation results illustrate the effectiveness of the proposed method.
References
[1]
G. Jia, W. Chang, Q. Zhang and X. Luan, The analysis and realization of motion compensation for circular synthetic aperture radar data[J], IEEE Journal Of Selected Topics In Applied Earth Observations And Remote Sensing 9(7) (2016), 3060–3071.
[2]
M. Soumekh, Reconnaissance with slant plane circular SAR imaging[J], IEEE Transactions on Image Processing 5(8) (1996), 1252–1265.
[3]
K. Leilei, W. Xiaoqing and C. Jisong, An Improved Wavenumber Domain Algorithm for Circular SAR and Cylindrical Reconstruction Areas[C], Synthetic Aperture Radar, 2009 APSAR 2009 2nd Asian-Pacific Conference, 2009, pp. 67–70.
[4]
Y.H. Luo, H.J. Song and R. Wang, An accurate and efficient extended scene simulator for FMCW SAR with static and moving targets[J], IEEE Geoscience and Remote Sensing 11(10) (2014), 1159–1164.
[5]
M. Soumekh, Synthetic Aperture Radar Signal Processing with MATLAB Algorithms, Wiley, New York, 1999.
[6]
L. Yun, T. Wei-Xian, H. Wen, W. Yan-Ping and W. Yi-Rong, Polar format algorithm for circular synthetic aperture radar[J], Journal of Electronics & Information Technology 32(12) (2010), 2802–2807.
[7]
Z. Li, J. Wang, J. Wu, et al., A fast radial scanned near-field 3-D SAR imaging system and the reconstruction method[J], Geoscience and Remote Sensing, IEEE Transactions on 53(3) (2015), 1355–1363.
[8]
H. Xie, S. Shi, D. An, etc., Fast factorized backprojection algorithm for one-stationary bistatic spotlight circular SAR image formation[J], IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(4) (2017), 1494–1510.
[9]
L. Yun, H. Wen and T. Weixian, Compressed sensing technique for circular SAR Imaging[C], 2009 IET International Radar Conference, 2009, pp. 1–4.
[10]
G.T. Nash, U.I. Cheema, R. Ansari, et al., Power-efficient RMA SAR imaging using pipelined Non-uniform Fast Fourier Transform[C], Radar Conference (RadarCon), 2015 IEEE IEEE, 2015, pp. 1600–1604.
[11]
P. Patil, Survey on enhanced anomaly based intrusion detection system using limited labeled data, International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE) 5(11) (2017).
[12]
B. Subiza, E. Gimeno-Nieves, J.M. Lopez-Sanchez, et al., An approach to SAR imaging by means of non-uniform FFTs[C], Geoscience and Remote Sensing Symposium, 2003 IGARSS’03 Proceedings 2003 IEEE International, IEEE 6 (2003), 4089–4091.
[13]
K.K. Sharma, Approximate signal reconstruction using nonuniform samples in fractional Fourier and linear canonical transform domains[J], Signal Processing, IEEE Transactions on 57(11) (2009), 4573–4578.
[14]
A. Dutt and V. Rokhlin, Fast Fourier transforms for nonequispaceddata[J], SIAM Journal on Scientific Computing 14(6) (1993), 1368–1393.
[15]
P. Babu and P. Stoica, Spectral analysis of nonuniformly sampled data–a review[J], Digital Signal Processing 20(2) (2010), 359–378.
[16]
J. Fessler and B.P. Sutton, Nonuniform fast Fourier transforms using min-max interpolation[J], Signal Processing, IEEE Transactions on 51(2) (2003), 560–574.
[17]
W. Alawad, M. Zohdy and D. Debnath, A study of DDoS attacks detection using supervised machine learning and a comparative cross-validation, International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE) 5(12) (2017).
[18]
S. Senay, L.F. Chaparro and L. Durak, Reconstruction of nonuniformly sampled time-limited signals using prolate spheroidal wave functions[J], Signal Processing 89(12) (2009), 2585–2595.
[19]
J. Oh, S. Senay and L.F. Chaparro, Signal reconstruction from nonuniformly spaced samples using evolutionary Slepian transform-based POCS[J], EURASIP Journal on Advances in Signal Processing 2010 (2010), 9.
[20]
M. Mobli and J.C. Hoch, Maximum entropy spectral reconstruction of non-uniformly sampled data[J], Concepts in magnetic resonance, Part A, Bridging Education and Research 32(6) (2008), 436.
[21]
G. Jia, C. Gu, W. Chang and L. Xiangyang, An improvement method for circular synthetic aperture radar imaging[J], International Journal of Remote Sensing 38(23) (2017), 6492–6504.
[22]
A.B. Altamimi, Context aware in intermittently connected network routing, International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE) 6(1) (2018).
[23]
D. Ao, R. Wang, C. Hu and Y. Li, A sparse SAR imaging method based on multiple measurement vectors model[J], Remote Sensing 297(9) (2017), 1–22.
[24]
H. Oriot and H. Cantalloube, Circular SAR imagery for urban remote sensing, In Proceedings of the 7th European Conference On Synthetic Aperture, Friedrichshafen, Geemany, 2008, pp. 1–4.
[25]
Micro-motion feature extraction of space target based on track-before-detect [J], Journal of Sensors 2017 (2017), Article ID 8723042:1–14.
[26]
J. Shi, J. Wu, A. Paul, L. Jiao and M. Gong, Change detection in synthetic aperture radar images based on fuzzy active contour models and genetic algorithms, Mathematical Problems in Engineering 2014 (2014).
[27]
J. Su, L.-B. Xie, Y. Yang, Y. Han and G.-J. Wen, A collision arbitration protocol based on specific selection function, Chinese Journal of Electronics 26(4) (2017), 864–870.
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Published: 01 January 2020
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