Coherent Sources Multidimensional Parameters Estimation with Sparse Array of Spatially Spread Long Electric-dipoles
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摘要: 由长电偶极子或大磁环组成的大尺寸电磁矢量传感器(ElectroMagnetic Vector Sensor, EMVS)比小尺寸EMVS辐射效率更高，研究其参数估计算法有助于推动EMVS的实装化应用。该文针对分离式长电偶极子稀疏阵列相干目标参数估计问题，提出高精度无模糊的多维参数闭式解算法。首先利用空域旋转不变性和单个矢量传感器内部属性得到方向余弦的高精度周期性模糊估计值，然后借助单个矢量传感器导向矢量推导出2维波达方向粗估计值，最后通过解模糊得到高精度无模糊的多维参数估计值。该方法规避了传统极化平滑算法的极化信息损失和迭代搜索过程，且能实现参数自动配对。计算机仿真结果表明了所提算法在分离式长电偶极子线阵中解相干的有效性。Abstract: The radiation efficiency of large-sized ElectroMagnetic Vector Sensor (EMVS) composed of long electric-dipoles or large magnetic-loops is higher than that of small-sized EMVS. The study of its parameter estimation algorithm is helpful to promote the practical application of EMVS. To solve the problem of parameter estimation of coherent targets with sparse array of spatially spread long electric-dipoles, a high accuracy and unambiguous closed multi-dimensional parameter solution algorithm is proposed. First, the high accuracy and periodically ambiguous direction-cosine estimations are obtained by using the spatial rotation invariance and the internal attributes of a single vector sensor. Then, the two-dimensional direction of arrival coarse estimations are derived based on the steering vector of a single vector sensor. Finally, the high accuracy and unambiguous multi-dimensional parameter estimations are obtained with disambiguation method. This proposed algorithm avoids the loss of polarization information and iterative search process of the traditional polarization smoothing algorithm, and can realize automatic matching of parameters. The computer simulation results show the effectiveness of the proposed algorithm in decoherence of the separated long electric dipole array.
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