基于互质极化敏感阵列的参数降维估计算法
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1.四川大学电气工程学院 成都 610065; 2.北京遥感设备研究所 北京 100005; 3.西南技术物理研究所 成都 610045

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TN911.7

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Reduced-dimensional parameter estimation algorithm based on coprime polarization sensitive array
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1.College of Electrical Engineering, Sichuan University,Chengdu 610065, China; 2.Beijing Institute of Remote Sensing Equipment,Beijing 100005, China; 3.Southwest Institute of Technical Physics,Chengdu 610045, China

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    摘要:

    针对互质极化敏感阵列波达方向角(DOA)和极化参数估计中存在的计算复杂度高以及多信源情况下DOA解模糊配对错误问题,本文提出了一种基于模值约束降维求根多重信号分类(MUSIC)的DOA和极化参数联合估计算法。首先通过重构三维谱函数,对DOA和极化参数进行解耦,实现三维 MUSIC 方法的降维,然后利用多项式求根求解出DOA,并利用波束形成方法解决了互质阵列中存在的解模糊角度错配问题,最后利用极化矢量的模值有界性构造代价函数,推导出极化参数的闭式解。数值仿真结果验证了所提算法的有效性,结果表明,所提算法参数估计精度高于旋转不变技术(ESPRIT),与一维全局谱峰搜索MUSIC(1D-TSS-MUSIC)算法基本相当,但本文算法显著降低了计算复杂度,且在多信源情况下依然可以获得可靠的参数估计。

    Abstract:

    A joint DOA and polarization parameters estimation algorithm based on modulus-constrained reduced-dimensional and root multiple signal classification (MUSIC) is proposed for coprime polarization sensitive arrays to address the high computational complexity and ambiguity pairing errors. Firstly, by reconstructing the three-dimensional spectral function, the DOA and polarization parameters are decoupled to achieve dimensionality reduction in the three-dimensional MUSIC method. Then, the DOA is solved using polynomial roots, and the beamforming method is used to solve the problem of ambiguity angle mismatch in the coprime array. Finally, the cost function is constructed using the modulus boundedness of the polarization vector to derive the closed form solution of the polarization parameters. The numerical simulation results have verified the effectiveness of the algorithm. The simulation results show that the parameter estimation performance of the proposed algorithm is better than that of the estimating signal parameter via rotational invariance techniques (ESPRIT), and is basically equivalent to the one-dimensional total spectral peak search MUSIC (1D-TSS-MUSIC) algorithm. However, our algorithm significantly reduces computational complexity and can still obtain reliable parameter estimation in multi source scenarios.

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逯岩斌,陈文东,杨赟秀,舒勤.基于互质极化敏感阵列的参数降维估计算法[J].电子测量技术,2024,47(5):173-180

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  • 在线发布日期: 2024-06-05
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