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基于线性调频信号的空时自适应杂波谱估计 被引量:1

Chirp-Based Space-Time Clutter Spectrum Estimation for STAP Radar by Sparse Representation
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摘要 空时自适应信号处理(STAP)是抑制强杂波的有效工具。由于线性调频信号(chirp)已经大范围应用于雷达,提出一种适应于chirp信号的STAP方法,并将其应用于杂波空时谱的估计。由于场景的非平稳性,传统通过大量训练样本的方法很难得到准确的杂波空时谱,因此引入了稀疏方法进行空时谱估计。该方法只需要用一个训练样本即可得到杂波空时谱。仿真实验在正侧视和非正侧视模型下采用非凸局部最优化的欠定系统局灶算法(FOCUSS)和正交匹配搜索算法(OMP)验证了chirp-STAP模型的有效性。 Space-time adaptive processing(STAP)is an effective tool for suppressing strong clutter.Because the chirp signal is widely used in airborne radar,a chirp based on space-time signal model,termed chirp-STAP,is developed to estimate the space-time clutter spectrum.Because of the non-homogeneity of the environment,the sparse method is introduced to the chirp-STAP,which needs only one training sample.Numerical results demonstrate that the proposed model can obtain a high performance both in side-looking case and non-side-looking case when using the focal underdetermined system solver(FOCUSS)and the orthogonal matching pursuit(OMP).
作者 白尕太 罗昀 陈国斌 聂伟 余银 BAI Gatai;LUO Yun;CHEN Guobin;NIE Wei;YU Yin(Science and Technology on Electronic Information Control Laboratory,Chengdu 610036,China)
出处 《电子信息对抗技术》 北大核心 2021年第1期14-17,97,共5页 Electronic Information Warfare Technology
关键词 空时自适应信号处理 线性调频信号 稀疏 杂波空时谱 STAP chirp signal sparse method space-time clutter spectrum
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  • 1Donoho D L, Elad M, Temlyakov V N. Stable recovery of sparse overeomplete representations in the presence of noise [J]. IEEE Transactions on Information Theory, 2006, 52(1): 6-18.
  • 2Donoho D L, Eladar M. Optimally sparse representation in general (non-orthogonal) dictionaries via L1 minimization [J]. IEEE Transactions on Signal Processing, 2003, 12(6) : 197- 220.
  • 3Gorodnitsky I F, Rao B D. Sparse signal reconstruction from limited data using FOCUSS: A re-weighted minimum norm algorithm [J].IEEE Transactions on Signal Processing, 1997, 45(3): 600-616.
  • 4Rao B D, Engan K, Cotter S F, et al. Subset selection in noise based on diversity measure minimization [J].IEEE Transactions on Signal Processing, 2003, 51(3): 760- 770.
  • 5Malioutov D, Cetin M, Willsky A S. A sparse signal reconstruction perspective for source locaiization with sensor arrays [J]. IEEE Transactions on Signal Processing, 2005, 53(2) : 3010 - 3022.
  • 6Baraniuk R, Steeghs P. Compressive radar imaging [C]// Radar Conference. Boston, MA, USA:IEEE Press, 2006: 128 - 133.
  • 7LIU Hesheng, GAO Xiaorong, Schimpf P H, et al. A recursive algorithm for the three dimensional imaging of brain electric activity: Shrinking LORETA-FOCUSS [J]. IEEE Transactions on Biomedical Engineering, 2004, 51(10) 1794 - 1802.
  • 8SUN Ke, ZHANG Hao, LI Gang, et al. A novel STAP algorithm using sparse recovery technique [C]// International Conference on Geoscience and Remote Sensing. Captown, South Africa: IEEE Press, 2009:336- 339.
  • 9Burintramart S, Sarkar T K, ZHANG Yang, et al. Performance comparison between statistical-based and direct data domain STAPs [J]. Digital Signal Processing, 2007, 17(3) : 737 - 755.
  • 10Klemm R. The Applications of Space-Time Adaptive Processing [M]. London, UK: IEE, 2004.

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