基于稀疏迭代协方差估计的缺失数据谱分析及时域重建方法

马俊涛; 高梅国; 董健

doi:10.11999/JEIT151008

基于稀疏迭代协方差估计的缺失数据谱分析及时域重建方法

doi: 10.11999/JEIT151008

2.
(北京理工大学电子与信息学院北京 100081) ②(中国人民解放军军械工程学院电子与光学工程系石家庄 050003)

基金项目:

国家自然科学基金(61401024)

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出版历程
- 收稿日期: 2015-09-09
- 修回日期: 2016-01-29
- 刊出日期: 2016-06-19

Sparse Iterative Covariance Estimation-based Approach for Spectral Analysis and Reconstruction of Missing Data

2.
(School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China)

Funds:

The National Natural Science Foundation of China (61401024)

摘要

摘要: 应用于缺失数据恢复的迭代自适应方法(IAA)被证实可利用20%的有效数据估计信号参数，并能高精度恢复缺失数据，优于经典GAPES方法，但当缺失数据超过80%时其数据恢复性能迅速下降。该文基于稀疏迭代协方差估计提出一种新的缺失数据谱分析方法(M-SPICE)及针对该方法的缺失数据修正时域重建方法。该方法将加权缺失数据协方差拟合代价函数转换为凸优化问题，构造循环最小化器保证缺失数据参数估计的全局收敛特性，通过对缺失数据估计算子的更新实现了时域重建方法的修正，使其在有效数据功率谱欠估计的情况下获得更高的数据重建精度。仿真实验表明无论是数据块缺失还是任意缺失，该方法均能够利用更少的有效数据进行谱分析，并重建大比例缺失数据。
- 缺失数据重建 /
- 谱估计 /
- 迭代自适应 /
- 稀疏协方差估计
Abstract: Many researches confirmed the excellent performance of Iterative Adaptive Approach (IAA), when it is applied to spectrum analysis of missing data. Simulation results show that the IAA can use 20 percent of the data to recover the missing samples, which is superior to Gapped Amplitude and Phase EStimation (GAPES). But the reconstruction performance of IAA degrades rapidly when the missing data exceed 80%. This paper introduces a novel method of missing data spectrum analysis, and a relevant modified method of time-domain reconstruction is proposed, called Missing SParse Iterative Covariance-based Estimation(M-SPICE). This method converts the weighted missing data covariance fitting cost function to a convex optimization problem. The global convergence property is obtained by adopting cyclic minimizers. The time-domain reconstruction method is modified by renewing estimation operator, which increases the accuracy of the data reconstruction in the case of underestimation. The simulation indicates that the novel method can be used to estimate the missing data spectrum, and reconstruct missing data accurately, with even fewer valid samples, regardless of gapped or arbitrary missing patterns.
- Missing data reconstruction /
- Spectral analysis /
- Iterative Adaptive Approach (IAA) /
- Sparse covariance- based estimation

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参考文献(19)

STOICA P, LARSSON E G, and LI Jian. Adaptive filter-bank approach to restoration and spectral analysis of gapped data[J]. The Astronomical Journal, 2000, 120(4): 2163-2173.

SCHAFER J L and GRAHAM J W. Missing data: our view of the state of the art[J]. Psychological Methods, 2002, 7(2): 147-177.

BAI Xueru, ZHOU Feng, XING Mengdao, et al. High- resolution radar imaging of air targets from sparse azimuth data[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(2): 1643-1655.

王成, 胡卫东, 杜小勇, 等. 稀疏子带的多频段雷达信号融合超分辨距离成像[J]. 电子学报, 2006, 34(6): 985-990.

WANG Cheng, HU Weidong, and DU Xiaoyong, et al. The super-resolution range imaging based on sparse band multiple frequency bands radars signal fusion[J]. Acta Electronica Sinica, 2006, 34(6): 985-990.

刘启, 洪文, 谭维贤, 等. 宽角合成孔径雷达二维缺失数据自适应幅相估计成像方法[J]. 电子与信息学报, 2012, 34(3): 616-621. doi: 10.3724/SP.J.1146.2011.00650.

LIU Qi, HONG Wen, TAN Weixian, et al. Adaptive tuning missing-data amplitude and phase estimation method in wide angle SAR[J]. Journal of Electronics Information Technology, 2012, 34(3): 616-621. doi: 10.3724/SP.J.1146. 2011.00650.

田彪, 刘洋, 徐世友, 等. 基于几何绕射理论模型高精度参数估计的多频带合成成像[J]. 电子与信息学报, 2013, 35(7): 1532-1539. doi: 10.3724/SP.J.1146.2012.01364.

TIAN Biao, LIU Yang, XU Shiyou, et al. Multi-band fusion imaging based on high precision parameter estimation of geometrical theory of diffraction model[J]. Journal of Electronics Information Technology, 2013, 35(7): 1532-1539. doi: 10.3724/SP.J.1146.2012.01364.

YARDIBI T, LI Jian, STOICA P, et al. Source localization and sensing: a nonparametric iterative adaptive approach based on weighted least squares[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(1): 425-443.

SUN W, SO H C, CHEN Y, et al. Approximate subspace- based iterative adaptive approach for fast two-dimensional spectral estimation[J]. IEEE Transactions on Signal Processing, 2014, 62(12): 3220-3231.

ZHANG Yongchao, ZHANG Yin, LI W, et al. Divide and conquer: a fast matrix inverse method of iterative adaptive approach for real beam superresolution[C]. International Geoscience and Remote Sensing Symposium (IGARSS), Qubec City, 2014: 698-701.

GLENTIS G O, JAKOBSSON A, and ANGELOPOULOS K. Block-recursive IAA-based spectral estimates with missing samples using data interpolation[C]. International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, 2014: 350-354.

STOICA P, LI Jian, and LING J. Missing data recovery via a nonparametric iterative adaptive approach[J]. IEEE Signal Processing Letters, 2009, 16(4): 241-244.

GLENTIS G O, ZHAO K, JAKOBSSON A, et al. Non-parametric high-resolution SAR imaging[J]. IEEE Transactions on Signal Processing, 2013, 61(7): 1614-1624.

KARLSSON J, ROWE W, XU L, et al. Fast missing-data IAA with application to notched spectrum SAR[J]. IEEE Transactions on Aerospace Electronic Systems, 2014, 50(2): 959-971.

STOICA P, PRABHU Babu, and LI Jian. New method of sparse parameter estimation in separable models and its use for spectral analysis of irregularly sampled data[J]. IEEE Transactions on Signal Processing, 2011, 59(1): 35-47.

STOICA P, PRABHU Babu, and LI Jian. SPICE: a sparse covariance-based estimation method for array processing [J]. IEEE Transactions on Signal Processing, 2011, 59(2): 629-638.

PARK H R and LI Jie. Sparse covariance-based high resolution time delay estimation for spread spectrum signals [J]. Electronics Letters, 2015, 51(2): 155-157.

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