三维混合天线阵列故障检测问题建模:分析与评估

IF 3.5 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Somayeh Komeylian;Christopher Paolini
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引用次数: 0

摘要

故障检测领域的研究一直在稳步发展,以监测存在激励相位和振幅误差的阵列天线的性能。故障元件的存在会显著降低天线阵列的辐射特性和性能。三维 HAAwBE 元件输出端的激励相位和振幅的测量误差由几个稀疏的非零向量表征。我们设计了一个正则化 $l_{2,1}$ 准则问题来模拟故障元件和噪声的误差。在这项工作中,我们采用了联合稀疏性设置下的 ADMM 方法,以解决 HAAwBE 退化辐射模式的大量样本的正则化 $l_{2,1}$ -norm 问题,而不是计算其阵列因子,后者需要大量复杂的数学计算。在联合稀疏性设置下,所提出的 ADMM 技术可使问题的成本函数与模型参数和变量向量的关系最小化。通过采用三种不同的优化方法,我们进一步提高了 HAAwBE 在故障检测和 DoA 估算这两个问题上的准确性和稳定性:LS-SVM、NN-RBF 和 NN-MLP 三种不同的优化方法,并进行了比较。结果,在存在 50% 故障元素的情况下,通过使用 LS-SVM、NN-MLP 和 NN-RBF 优化方法,HAAwBE 分别获得了 91.83%、91.24% 和 88.33% 的高成功率,从数值上验证了 HAAwBE 的卓越性能。此外,使用 HAAwBE 估算 DoA 的结果表明,使用优化方法识别三个信号源位置的分辨率很高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling of the Fault Detection Problem for a 3-D Hybrid Antenna Array: Analysis and Evaluation
Research in the field of fault detection has steadily been developing for monitoring the performance of array antennas in the presence of errors in excitation phases and amplitudes. The presence of faulty elements degrades significantly the radiation characteristics and performance of antenna arrays. The measured errors in excitation phases and amplitudes at outputs of elements of the 3D HAAwBE are characterized by a few sparse non-zero vectors. A regularized $l_{2,1}$ -norm problem is designed to model errors of faulty elements and noise. In this work, we have implemented the ADMM method under the joint sparsity setting to solve the regularized $l_{2,1}$ -norm problem for a number of samples of the degraded radiation pattern of the HAAwBE rather than computing its array factor, which requires significant and complex mathematical computation. The proposed ADMM technique under the joint sparsity setting allows for minimizing the cost function of the problem with respect to both model parameters and variable vectors. We have further increased accuracy and stability of the performance of the HAAwBE in the two problems of fault detection and DoA estimation by deploying three different optimization methods: LS-SVM, NN-RBF, and NN-MLP, and compared to each other. Consequently, the superior performance of the HAAwBE has been numerically verified by the high success rates of 91.83%, 91.24%, and 88.33%, by performing the LS-SVM, NN-MLP, and NN-RBF optimization methods, respectively, in the presence of 50% faulty elements. Furthermore, results of DoA estimation by the HAAwBE have represented the high resolution in recognizing locations of three signal sources with performing the optimization method.
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来源期刊
CiteScore
6.50
自引率
12.50%
发文量
90
审稿时长
8 weeks
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