杨林森, 张子敬, 郭付阳. 基于Radon-Ambiguity变换的LFM信号时/频差快速联合估计[J]. 电波科学学报, 2016, 31(6): 1074-1080. doi: 10.13443/j.cjors.2016091601
      引用本文: 杨林森, 张子敬, 郭付阳. 基于Radon-Ambiguity变换的LFM信号时/频差快速联合估计[J]. 电波科学学报, 2016, 31(6): 1074-1080. doi: 10.13443/j.cjors.2016091601
      YANG Linsen, ZHANG Zijing, GUO Fuyang. Joint time-frequency offset estimation for LFM signals based on the Radon-Ambiguity transform[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2016, 31(6): 1074-1080. doi: 10.13443/j.cjors.2016091601
      Citation: YANG Linsen, ZHANG Zijing, GUO Fuyang. Joint time-frequency offset estimation for LFM signals based on the Radon-Ambiguity transform[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2016, 31(6): 1074-1080. doi: 10.13443/j.cjors.2016091601

      基于Radon-Ambiguity变换的LFM信号时/频差快速联合估计

      Joint time-frequency offset estimation for LFM signals based on the Radon-Ambiguity transform

      • 摘要: 提出了一种基于Radon-Ambiguity变换(Radon-Ambiguity Transform, RAT)的线性调频(Linear Frequency Modulated, LFM)信号时/频差快速联合估计的算法.根据LFM信号在多个不同角度上的RAT峰值位置建立一组以信号间时差和频差为未知量的方程组,求解方程组即可得到时/频差的估计值.对于存在噪声的信号,RAT误差会导致方程组不能直接求解,为了抑制噪声干扰,采用最小二乘法估计时/频差.本文算法无需计算二维平面上各点的模糊函数值,并且由于离散RAT可以通过快速傅里叶变换快速实现,具有所需运算量低的优点.仿真实验表明,相比于常见的基于模糊函数峰值搜索的时/频差估计算法,本文算法在保证时/频差估计精度的同时能够显著提高运算效率.

         

        Abstract: A fast method for joint estimation of the time-frequency offset for linear frequency modulated signals based on the Radon-Ambiguity transform (RAT) is proposed in this paper. According to peak positions of the RAT of a LFM signal on different angles, a set of equations with the time-frequency offset as the unknowns can be established and the time-frequency offset can be estimated by solving the equations. For signals disturbed by noise, errors of the RAT will cause the equations having no solutions. In order to eliminate the noise, the least square method is used to estimate the time-frequency offset. Because the proposed algorithm does not need to calculate the value of each point on the 2-D Ambiguity plane and the RAT for sampled signals can be realized rapidly by several processes of the fast Fourier transform, the proposed method has advantage of low computational cost. Simulation results show that the proposed algorithm can ensure the accuracy of the estimation of the time-frequency offset as well as is computationally efficient compared with common methods based on peak searching of the Ambiguity function.

         

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