真实和复杂LFMCW系统中超分辨率估计算法的性能比较

Li Yang, Liang Liwan, Pan Weifeng, Chen Yanqin, Feng Zhenghe
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引用次数: 2

摘要

在真实或复杂线性FMCW (LFMCW)雷达系统中,采用具有超分辨能力和低信噪比阈值的算法可以减轻系统对功率和带宽的需求,降低系统成本。本文对MUSIC、AR和神经网络三种典型算法在统计信噪比和分辨率性能方面进行了比较研究。仿真结果表明,神经网络算法在两种系统中都具有较高的分辨率和较低的信噪比阈值,适合于LFMCW应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance comparison of super-resolution estimation algorithms used in real or complex LFMCW systems
In real or complex linear FMCW (LFMCW) radar systems, employing algorithms with super-resolution capability and low SNR threshold can mitigate the system demands for power and bandwidth, and reduce system cost. In this paper, a comparative study among three typical algorithms is carried out on statistical SNR and resolution performance: MUSIC, AR, and neural network. Simulation results show that the neural network algorithm, having higher resolution and lower SNR threshold in both systems, is suitable for LFMCW applications.
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