电子学报 ›› 2013, Vol. 41 ›› Issue (9): 1694-1702.DOI: 10.3969/j.issn.0372-2112.2013.09.005

• 学术论文 • 上一篇    下一篇

抑制校正源方位估计偏差的阵元位置误差鲁棒估计算法

王鼎, 姚晖, 吴瑛   

  1. 解放军信息工程大学信息系统工程学院, 河南郑州 450002
  • 收稿日期:2011-05-26 修回日期:2013-03-01 出版日期:2013-09-25
    • 作者简介:
    • 王 鼎 男,1982年生于安徽芜湖,博士,信息工程大学讲师,感兴趣的研究方向:现代信号处理. E-mail:wang_ding814@aliyun.com;姚 晖 男,1985年生于江西上饶,现为信息工程大学博士研究生,感兴趣的研究方向:阵列信号处理. E-mail:yaohui56@sina.com;吴 瑛 女,1960年生于河南郑州,信息工程大学教授,博士生导师,感兴趣的研究方向:现代信号处理. E-mail:hnwuying22@163.com
    • 基金资助:
    • 国家自然科学基金资助项目 (No.61201381); 信息工程学院未来发展基金资助项目 (No.YP12JJ202057)

Robust Algorithm for Sensor Position Estimation Against the Direction Deviations of the Calibration Sources

WANG Ding, YAO Hui, WU Ying   

  1. Institute of Information System Engineering, PLA Information Engineering University, Zhengzhou, Henan 450002, China
  • Received:2011-05-26 Revised:2013-03-01 Online:2013-09-25 Published:2013-09-25

摘要: 针对校正源方位估计偏差会影响阵元位置误差校正精度这一问题,该文在假设校正源方位估计偏差的概率分布先验已知的条件下,根据信号子空间拟合理论和贝叶斯估计理论框架,提出一种能够抑制校正源方位估计偏差的阵元位置误差鲁棒估计算法.该算法以Newton迭代的形式给出,并不需要对校正源方位进行数值优化.理论分析和仿真实验均表明:在一定条件下,文中的新算法对校正源方位估计偏差具有较好的鲁棒性,并且其渐近性能可逼近相应的克拉美罗界.

关键词: 有源校正, 阵元位置误差, 信号子空间拟合, 贝叶斯估计, Newton迭代, 克拉美罗界

Abstract: The direction deviations of the calibration sources would seriously degrade the calibration precision of the sensor positions.Aiming to this problem,the robust calibration algorithm for estimating the sensor shape against the location deviations is presented under the condition that the prior probability distribution of the direction deviations is available.The robust algorithm is derived based on the signal subspace fitting (SSF) technique and the Bayesian estimation theory frame,and it is numerically implemented via the Newton iteration without optimizing the locations of the calibration sources.Both the theory analysis and simulation experiments validate that the novel algorithm has preferable robustness against the location deviations of the calibration sources and its asymptotic performance can reach the Cramé-Rao bound (CRB) under some moderate conditions.

Key words: active calibration, sensor position error, signal subspace fitting, Bayesian estimation, Newton iteration, Cramé-Rao bound

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