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机构地区:[1]解放军信息工程大学信息系统工程学院,河南郑州450002
出 处:《通信学报》2014年第2期153-165,共13页Journal on Communications
基 金:国家自然科学基金资助项目(61201381);信息工程学院未来发展基金资助项目(YP12JJ202057)~~
摘 要:针对非圆信号空间谱测向中方位依赖幅相误差的校正问题,基于辅助阵元自校正算法(ISM,instrumental sensor method)基本原理,提出一种改进的ISM算法:NC-ISM算法。该算法通过利用最大非圆率信号的扩展数据模型,提高了信号利用率,使其估计精度较一般的ISM算法有明显提升,最大可分辨信源数也增加一倍。对该算法的理论性能进行研究,证明了其参数估计的统计一致性,并采用一阶误差分析方法推导了辅助阵元模型误差影响下参数估计的均方误差表达式,从而为工程应用提供理论支撑。仿真结果验证了理论推导的正确性,同时表明,该算法较ISM算法在辅助阵元模型误差与低信噪比下都有更强的顽健性。An improved direction-finding algorithm for noncircular sources was proposed in the presence of angularly dependent gain and phase errors, which is called NC-ISM based on the fundamental principle of ISM (instrumental sen- sor method). Through the application of the extended data model of non-circular signals with maximum rate, the pro- posed algorithm enhances the information utilization leading to advancement of estimation accuracy and twice the num- ber of sources that can be distinguished. The performance study on NC-ISM proved the statistical consistency of the pa- rameter estimation, and a theoretical derivation for the closed-form expression of the mean square error (MSE) of NC-ISM estimation was presented under the influence of modeling errors of instrumental sensors by the first-order analysis. Therefore, the analysis can provide theoretical support for practical applications. The simulation results not only verify the effectiveness of theoretical derivation, but also illustrate that NC-ISM is more robust than ISM with respect to signal-to-noise ratio as well as modeling errors.
关 键 词:方位依赖幅相误差 辅助阵元自校正 非圆信号 统计一致性 辅助阵元模型误差
分 类 号:TN911.7[电子电信—通信与信息系统]
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