郭付阳, 张子敬, 杨林森. 线性调频信号时/频差估计算法[J]. 电波科学学报, 2016, 31(1): 166-172. doi: 10.13443/j.cjors.2015031501
      引用本文: 郭付阳, 张子敬, 杨林森. 线性调频信号时/频差估计算法[J]. 电波科学学报, 2016, 31(1): 166-172. doi: 10.13443/j.cjors.2015031501
      GUO Fuyang, ZHANG Zijing, YANG Linsen. TDOA/FDOA estimation algorithm for linear frequency modulated signals[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2016, 31(1): 166-172. doi: 10.13443/j.cjors.2015031501
      Citation: GUO Fuyang, ZHANG Zijing, YANG Linsen. TDOA/FDOA estimation algorithm for linear frequency modulated signals[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2016, 31(1): 166-172. doi: 10.13443/j.cjors.2015031501

      线性调频信号时/频差估计算法

      TDOA/FDOA estimation algorithm for linear frequency modulated signals

      • 摘要: 提出了一种新的快速估计线性调频信号时/频差的算法.该算法将抽取的自模糊函数与Radon变换结合估计线性调频信号的调频率, 通过分数阶傅里叶变换估计出模糊函数脊线与频率轴交点位置, 应用解调频沿脊线搜索模糊函数峰值.对于接收信号中存在多分量的情况, 根据其模糊函数脊线位置的不同, 该算法能够分辨各分量信号, 并分别精确估计出各分量的时/频差.由于只需一维搜索模糊函数峰值, 并可用快速傅里叶变换实现, 该算法大大减少了运算量.仿真实验表明, 随着信噪比的提高, 该算法估计的时/频差均方误差逐渐逼近克拉美-罗下界.

         

        Abstract: A novel efficient algorithm for time difference of arrival (TDOA) and frequency difference of arrival (FDOA) estimation between two linear frequency modulated (LFM) signals is proposed in this paper. The proposed approach combines decimated ambiguity function and Radon transform to estimate the chirp rate of the LFM signal; then fractional Fourier transform (FrFT) is used to estimate the point of intersection between the ridge and Doppler axis; finally the peak of ambiguity function is searched efficiently along the ridge by using of dechirping. For the multi-component LFM signal, since different LFM signals have different ridges, the proposed approach can successfully distinguish each LFM signal from the received signal, and both the TDOA and FDOA of each LFM signal can be estimated precisely. Due to fast Fourier transform-based processing and the use of only one-dimensional searches, the proposed approach is computationally efficient. Simulation results show that with the increase of signal noise ratio (SNR), the variances of the estimates are gradually close to the Cramer-Rao lower bound.

         

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