线性调频雷达啁啾率估计的机器学习方法

A. Young, David Luong, B. Balaji, S. Rajan
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引用次数: 2

摘要

低信噪比雷达信号的检测和参数估计,特别是线性调频(LFM)雷达信号,是一个相当有趣的问题。在之前的工作中,这个问题已经使用各种信号处理技术进行了研究,例如最大似然估计,分数傅里叶变换和基于wigner - ville的方法,以分析复杂线性调频信号的信号参数。其他工作集中在应用深度学习来自动识别各种雷达波形类型及其特征,如线性调频(LFM)、巴克码和矩形波形。在本文中,我们从机器学习的角度研究了给定先验信息的多个LFM雷达信号。我们探索使用朴素贝叶斯、支持向量机和神经网络分类器从一组已知的啁啾率中识别LFM啁啾率,这些啁啾率来自不同信噪比条件下的特定雷达发射器。仿真结果证明了该技术在低至-20 dB的低信噪比条件下识别雷达LFM模式的可行性,其中使用现有方法(例如Wigner-Ville)失败。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Approach to Chirp Rate Estimation of Linear Frequency Modulated Radars
The detection and parametric estimation of low-SNR radar signals, particularly linear frequency modulated (LFM) radar signals, is a problem of considerable interest. In prior work, this problem has been investigated using various signal processing techniques, such as maximum likelihood estimation, fractional Fourier transform and Wigner-Ville-based methods, to analyze the signal parameters of a complex linear frequency modulated signal. Other work has focused on applying deep learning to automatically recognize various radar waveform types and their features, such as linear frequency modulation (LFM), Barker code and rectangular waveforms. In this paper, we investigate this problem from a machine learning perspective for multiple LFM radar signals given a priori information. We explore the use of naive Bayes, support vector machine and neural network classifiers to identify the LFM chirp rate, out of a set of known chirp rates, from a specific radar emitter under varying SNR conditions. Simulation results demonstrate the viability of this technique to identify the radar LFM mode in very low signal-to-noise ratio conditions down to -20 dB where using existing approaches (e.g., Wigner-Ville) fail.
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