Using multi-radar signal fusion to fuse multi-angle and multi-band signals can improve the range and cross-range resolution of images. To overcome the limitation of multi-radar signal fusion based on spectral estimation, whose solidity depends heavily on the estimated accuracy of the number of scattering centers and the matching accuracy of two-dimensional poles, a linear representation model of multi-radar signal fusion is constructed. Then the sparsity of inverse synthetic aperture radar (ISAR) signal in the Fourier domain is excavated, and a new multi-radar signal fusion method based on signal sparse representation is proposed in this paper. From the analysis and simulation, it can be seen that the accuracy of parameters estimation based on signal sparse representation is better than that from the spectral estimation, while the operation efficiency of the new method is a little lower. But the performance of parameters estimation of the new method is affected by signal sparisty.
YE Fan, HE Feng, ZHU Jubo, ZHANG Yongsheng
. Multi-radar Signal Two-dimensional Fusion Processing Based on Sparse Representation[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2011
, 32(3)
: 515
-521
.
DOI: CNKI:11-1929/V.20101213.1757.007
[1] Cuomo K M, Piu J E, Mayhan J T. Ultrawide-band coherent processing[J]. IEEE Transactions on Antennas and Propagation, 1999, 47(6): 1094-1107.
[2] Vann L D, Cuomo K M. Multisensor fusion processing for enhanced radar imaging. ADA376545, 2000.
[3] Morre T G, Zuerndorfer B W. Enhanced imagery using spectral-estimation-based techniques[J]. Lincoln Laboratory Journal, 1997, 10(2): 171-186.
[4] Piou J E, Cuomo K M, Mayhan J T. A state-space technique for ultrawide-bandwidth coherent processing. ADA336105, 1999.
[5] Naishadham K, Piou J E. State-space spectral estimation of characteristic electromagnetic responses in wideband data[J]. IEEE Antennas and Wireless Propagation Letters, 2005(4): 406-409.
[6] 王成, 胡卫东, 郁文贤. 基于非平稳时间序列处理的雷达信号融合[J]. 信号处理, 2005, 21(4): 338-343. Wang Cheng, Hu Weidong, Yu Wenxian. Radar signal fusion based on non-stationary time series processes[J]. Signal Processing, 2005, 21(4): 338-343. (in Chinese)
[7] 王成. 雷达信号层融合成像技术研究. 长沙: 国防科学技术大学电子科学与工程学院, 2006. Wang Cheng. Research on radar signal level fusion imaging techniques. Changsha: College of Electronic Science and Technology, National University of Defense Technology, 2006. (in Chinese)
[8] Mallat S G, Zhang Z F. Matching pursuits with time-frequency dictionaries[J]. IEEE Transactions on Signal Processing, 1993, 41(12): 3397-3415.
[9] Chen S S. Basis pursuit. Palo Alto: Department of Statistics, Stanford University, 1995.
[10] Chen S S, Donoho D L. Application of bases pursuit in spectrum estimation//IEEE International Conference on Acoustics, Speech and Siagnal Processing. 1998, 3: 1865-1868.
[11] Donoho D L, Elad M. Maximal sparsity representation via l1 minimization. http://www-stat.stanford.edu/~donoho/reports/, 2002.
[12] Gorodnitsky I F, Rao B D. Sparse signal reconstructions from limited data using FOCUSS: a re-weighted minimum norm algorithm[J]. IEEE Transactions on Signal Processing, 1997, 45(3): 600-616.
[13] Donoho D L. Sparse components analysis and optimal atomic decompositions. http://www-stat.stanford.edu/~donoho/reports/, 2002.
[14] 杜小勇. 稀疏成分分析及在雷达成像处理中的应用. 长沙: 国防科学技术大学电子科学与工程学院, 2005. Du Xiaoyong. Sparse component analysis and its application in radar imaging processing. Changsha: College of Electronic Science and Technology, National University of Defense Technology, 2005.(in Chinese)
[15] 汪雄良. 基追踪方法及其在图像处理中的应用的研究. 长沙: 国防科学技术大学理学院,2006. Wang Xiongliang. Basis pursuit method and its application in image processing. Changsha: College of Science, National University of Defense Technology, 2006. (in Chinese)
CJA