复杂噪声环境下基于LVD的LFM信号参数估计

Parameter Estimation of LFM Signals Based on LVD in Complicated Noise Environments

针对传统的线性调频(LFM)信号参数估计方法平滑交叉项时,会出现参数估计精度降低和计算复杂度增加等问题,该文引入LVD(LV’s Distribution)方法,该方法可以在参数空间直接显示中心频率和调频斜率(CFCR)。LVD首先对对称参数瞬时自相关函数(PSIAF)进行尺度变换,消除信号在时间轴上的线性频率偏移,然后对尺度变换后的时间变量作2维傅里叶变换,将1维LFM信号转化为2维单频信号。信号各分量在LVD平面表现为多个独立尖峰,使交叉项的能量聚集影响可忽略不计,且信号各峰值所在位置对应于各信号分量的中心频率和调频斜率。LVD可有效抑制高斯噪声,但在脉冲性较强的a稳定分布噪声中,该方法在CFCR域的性能退化甚至失效。对此,该文结合分数低阶统计量理论,提出一种a稳定分布噪声环境下的分数低阶LVD新方法。仿真实验表明该方法在高斯噪声和脉冲噪声环境下均可稳定工作,具有较好的鲁棒性。

In view of reducing the effects of cross terms, conventional methods of parameter estimation for Linear Frequency Modulation （LFM） signals suffer from low accuracy and huge computational complexity. To solve these problems, LV’s Distribution （LVD） based method is introdused in this paper. It provides directly accurate Centroid Frequency-Chirp Rate （CFCR） representation of a LFM signal. The rescaling operator is used for the Parametric Symmetric Instantaneous Autocorrelation Function （PSIAF） to eliminate the effects of linear frequency migration on the time axis, then a two-dimensional （2-D） Fourier transform is taken over the new scaled time variables to convert a 1-D LFM signal into a 2-D single-frequency signal. The resulting signal can be represented with distinct peaks on the CFCR plane, whereas the energy of the cross terms can be ignored compared with the peaks of auto terms. The coordinate values of LFM components directly correspond to their centroid frequency and chirp rate. LVD can suppress effectively the Gaussian noise, however, the performance of the CFCR domain analysis for signals in heavy-tailed impulsive noise environment is in severe degradation. Considering this issue, an improved Fractional Lower Order LVD （FLOLVD） for the a stable distribution noise is proposed. Computer simulation results show that the proposed approach obtains high-accuracy phase estimation, and it is robust to the impulse noise as well as the Gaussian noise.