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作 者:苏宇 蒋德富[1] SU Yu;JIANG De-fu(School of Computer and Information,Hohai University,Nanjing,Jiangsu 213022,China)
机构地区:[1]河海大学计算机与信息学院,江苏南京213022
出 处:《北京理工大学学报》2020年第3期339-346,共8页Transactions of Beijing Institute of Technology
基 金:国家自然科学基金资助项目(61971179)。
摘 要:为了提高电子侦查系统中对非平稳噪声环境下线性调频连续波信号的实时检测能力,提出了一种对信号进行分段检测的低复杂度算法.根据假定最大调频斜率设定窗函数宽度,将截获信号均匀分割为多个序列,在每个时间序列内建立短时谐波模型,并采用多个正交窗函数对信号进行加权离散傅里叶变换.在此基础上,推导出符合F分布的检测模型.该模型与噪声功率无关,因此检测前无需统计噪声功率,可对非平稳噪声环境下的信号进行恒虚警率检测.仿真分析了影响该算法性能的参数,通过与单窗口检测算法对比,验证了该算法在非平稳噪声环境下优良的检测性能.In order to improve the real-time detection capability of an electronic reconnaissance system for linear frequency modulation continuous wave(LFMCW)signals in non-stationary noise environment,a low complexity algorithm for segmental detection of signals was proposed.Setting the width of the window function based on the maximum chirp rate assumption,and dividing the intercepted signal evenly into multiple segments,a short-time harmonic model was established in each time period,and the signal was weighted for discrete Fourier transform with multiple orthogonal window functions.On this basis,a detection model was derived according with the F distribution.The model was designed to be independent of noise power,so the constant false alarm rate(CFAR)detection of signals in non-stationary noise environment could be carried out without statistical noise power before detection.The parameters that affect the performance of the algorithm were simulated and analyzed,and compared with the single window detection algorithm to verify the excellent detection performance of the algorithm in non-stationary noise environment.
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