一种改进多分量LFM信号时频分布估计方法
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摘要
传统时频分布方法在估计多分量线性调频信号时,交叉项影响导致时频估计精度低。因此,提出建立信号调频斜率因子库,对时频分布核函数进行改进,使核函数能有效抑制由多分量信号引起的交叉项干扰。通过分析核函数与信号模糊函数交叉项及自身项的关系,将时频估计问题转化为求解最优调频斜率因子库来对核函数进行改进,使信号的广义模糊函数能量最大。以多分量线性调频信号为例进行仿真,结果证明与传统时频分布方法相比,改进的时频估计方法可以在有效抑制交叉项干扰的同时保留大部分自身项,提高了多分量信号时频估计的精度和抗噪声能力。
There is a notable drawback for traditional time-frequency distribution method that it fails to suppress cross-terms caused by multicomponent linear frequency modulation( LFM) signals. To effectively suppress the cross-terms, an improved method based on frequency rate database is proposed. By analyzing the relationship between kernel function and signal fuzzy functions cross and self terms, the time-frequency question is transferred to establish a frequency rate database which can adjust the kernel function optimally and get the maximum fuzzy function energy. By applying multicomponent LFM signal as an example, the simulation results show that compared with the traditional time-frequency distribution method, the proposed method can effectively suppress the cross-terms and retain most of the self-terms, which improves the accuracy and anti-noise capacity of time-frequency distribution estimation.
There is a notable drawback for traditional time-frequency distribution method that it fails to suppress cross-terms caused by multicomponent linear frequency modulation( LFM) signals. To effectively suppress the cross-terms, an improved method based on frequency rate database is proposed. By analyzing the relationship between kernel function and signal fuzzy functions cross and self terms, the time-frequency question is transferred to establish a frequency rate database which can adjust the kernel function optimally and get the maximum fuzzy function energy. By applying multicomponent LFM signal as an example, the simulation results show that compared with the traditional time-frequency distribution method, the proposed method can effectively suppress the cross-terms and retain most of the self-terms, which improves the accuracy and anti-noise capacity of time-frequency distribution estimation.
引文
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[2]刘勇,张国毅,于岩.一种非线性调频信号瞬时频率的估计算法[J].科学技术与工程,2013,13(31):9390-9395.
[3]郑生华,靳学明,徐大专.基于Wigner-Hough变换的P3/P4多相码雷达信号检测研究[J].合肥工业大学学报(自然科学版),2008,31(12):2049-2052.
[4] Qi F G,Li Z,Liang F L,et al. A novel time-frequency analysis method based on HHT for finer-grained human activity using SFCW radar[C]//2016 Progress in Electromagnetic Research Symposium. 2016:2536-2539.
[5]火元莲,袁培影,张广庶,等.闪电快电场信号时频特性的小波分析[J].测控技术,2016,35(7):25-30.
[6]闫哲,孙婉君,王奇伟.基于分数阶自相关的线性调频信号的参数提取[J].测控技术,2016,35(2):32-35.
[7]赵培洪,平殿发,邓兵,等.魏格纳-维尔分布交叉项抑制方法综述[J].探测与控制学报,2010,32(1):23-29.
[8]全盛荣,张天骐,王俊霞,等.基于稀疏分解的SFM信号的时频分析方法[J].电子技术应用,2016,42(6).
[9]宁静,诸昌钤,朱肇昆.一种改进的指数核时频表示[J].计算机应用研究,2010,27(6):2078-2079.
[10]白航,赵拥军,胡德秀,等.基于改进时频分析方法的雷达信号瞬时频率估计[J].信号处理,2012,28(2).
[11] Jokanovic B, Amin M G, Zhang Y D, et al. Time-frequency kernel design for sparse joint-variable signal representations[C]//2014 22nd European Signal Processing Conference.2015:2100-2104.
[12] Wang J Z,Su S Y,Chen Z P. Parameter estimation of chirp signal under low SNR[J]. Science China Information Science,2015,58(2):20307-20307.
[13]王晓凯,高静怀,何洋洋.基于时频自适应最优核的时频分析方法[J].系统工程与电子技术,2010,32(1):22-26.