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基于频域聚焦和稀疏表示的宽带信号DOA估计方法 被引量:1

Direction of Arrival Estimation of Wideband Signal Based on Frequency Domain Focusing and Sparse Representation
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摘要 针对常规宽带信号测向算法在低信噪比、低快拍数、低角度间隔条件下算法性能严重下降甚至失效的问题,本文创新性地提出一种基于频域聚焦与稀疏表示的宽带信号波达方向(Direction Of Arrival, DOA)估计算法。该算法首先对各频点数据进行聚焦处理,建立参考频率处的基矩阵;然后构造块对角矩阵,并构建基于协方差矩阵的稀疏表示模型,达到无须估计噪声功率就可估计宽带信号波达方向的效果。仿真结果表明,本文所提出的算法在低信噪比、低快拍数、低角度间隔条件下依然可实现对宽带信号DOA的准确估计。 Aiming at the problem that the performance of conventional wideband signal direction finding algorithms is severely degraded or even invalid under the conditions of low signal-to-noise ratio, low snapshots, and low angular interval, this paper innovatively proposes a wideband signal DOA estimation algorithm based on frequency domain focusing and sparse representation. The algorithm first focuses on the data of each frequency point and establishes the basis matrix at the reference frequency;then constructs the block diagonal matrix, and builds a sparse representation model based on the covariance matrix, so that the broadband signal can be estimated without estimating the noise power. The effect of direction. The simulation results show that the algorithm proposed in this paper can still achieve accurate estimation of the wideband signal DOA under the conditions of low signal-to-noise ratio, low number of snapshots, and low angular interval.
作者 吴钊君 杨磊 胡东 颜振亚 史建涛 Wu Zhaojun;Yang Lei;Hu Dong;Yan Zhenya;Shi Jiantao(Nanjing Research Institute of Electronics Technology,Nanjing 210039,China)
出处 《信息化研究》 2021年第2期13-18,共6页 INFORMATIZATION RESEARCH
基金 国家自然科学基金面上项目(No.61973288) 江苏省高层次创新创业人才引进计划资助项目。
关键词 波达方向 稀疏表示 宽带测向 direction of arrival(DOA) sparse representation wideband angle measurement
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  • 1Donoho D. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
  • 2Cands E and Tao T. Decoding by linear programming[J]. IEEE Transactions on Information Theory, 2005, 51(12): 4203-4215.
  • 3Malioutov D, Cetin M, and Willsky A. A sparse signal reconstruction perspective for source localization with sensor arrays[J]. IEEE Transactions on Signal Processing, 2005, 53(8): 3010 3022.
  • 4Cevher V Boufounos P, Baraniuk R, et al.. Near-optimal bayesian localization via incoherence and sparsity[C]. Proceedings of the International Conference on Information Processing in Sensor Networks, San Francisco, 2009: 205-216.
  • 5Duarte M. Localization and bearing estimation via structured sparsity models[C]. Proceedings of the IEEEStatistical Signal Processing Workshop, Ann Arbor, 2012: 333-336.
  • 6Kim J, Lee O, and Ye J. Compressive MUSIC: revisiting the link between compressive sensing and array signal processing [J]. 1EEE Transactions on Information Theory, 2012, 58(1): 278-3{)1.
  • 7Lee K, Bresler Y, and Junge M. Subspace methods for joint sparse recovery[J]. IEEE Transactions on Information Theory, 2012, 58(6): 3616-3641.
  • 8Donoho D, Elad M, and Temlyakov V. Stable recovery of sparse overcomplete representations in the presence of noise[J]. [EEE Transactions on Information Theory, 2006, 52(1): 6-18.
  • 9Chi Yue-jie, Scharf L, Pezeshki A, et al.. Sensitivity to basis mismatch in compressed sensing[J]. IEEE Transactions on Signal Processing, 2011, 59(5): 2182-2195.
  • 10Herman M and Strohmer T. General deviants: an analysis of perturbations in compressed sensing[J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(2): 342-349.

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