An auto-calibration method for spatially and temporally correlated noncircular sources in unknown noise fields

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• 作者：Jie-xin Yin ; Ying Wu ; Ding Wang
• 关键词：Auto ; calibration ; Alternating projection ; Noncircularity ; Instrumental sensor ; Unknown spatially correlated noise ; Modeling error
• 刊名：Multidimensional Systems and Signal Processing
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：27
• 期：2
• 页码：511-539
• 全文大小：1,461 KB
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• 作者单位：Jie-xin Yin (1)
  Ying Wu (1)
  Ding Wang (1)
  
  1. Department of Communication Engineering, Zhengzhou Information Science and Technology Institute, Zhengzhou, 450002, Henan, People’s Republic of China
  
• 刊物类别：Engineering
• 刊物主题：Circuits and Systems
  Electronic and Computer Engineering
  Signal,Image and Speech Processing
  Artificial Intelligence and Robotics
  
• 出版者：Springer Netherlands
• ISSN：1573-0824

文摘

Angularly dependent gain and phase uncertainties are produced by the combined effects of multiple sensor errors. This paper proposes a direction-finding method for noncircular signals in the presence of angularly dependent gain/phase errors, which utilizes instrumental sensors to achieve auto-calibration and relies on an improved alternating projection procedure. By applying the principle of the extended 2-sided instrumental variable signal subspace fitting algorithm, the proposed method is effective for separating spatially and temporally correlated noncircular sources from the unknown colored (i.e., spatially correlated) noise. Considering that modeling errors of instrumental sensors are frequently encountered in practice, this paper also presents a theoretical derivation for the closed-form expression of the mean square error of the estimation under the influence of modeling errors of instrumental sensors in the first-order analysis. Finally, the results of two series of simulations are demonstrated. The first series of simulations verifies the effectiveness of the proposed auto-calibration method, and shows that noncircularity and temporal correlation of sources are informative for enhancing the calibration performance of our method. The results also prove that the proposed method performs better than the instrumental sensor method when applied to spatially and temporally correlated noncircular sources. Moreover, this performance advantage of our method is more prominent when signal-to-noise ratio is low, or in spatially correlated noise fields. The second series of simulations validates the theoretical prediction, and thus our statistical analysis has a high predictive value for calibration performance of the proposed method under the influence of modeling errors.