The range frequency autocorrelation function (RFAF) based algorithm is proposed for radar target detection and motion parameter estimation in our previous work. In the RFAF-based method, the symmetric autocorrelation function (SAF) is constructed with respect to the range frequency, and three dimensional (slow time, range frequency, and shift frequency) energy accumulation can be completed coherently. In this article, as a further study of the RFAF-based method, the fast implementation and detailed asymptotic statistical performance analyses of RFAF are studied. First, on the basis of the frequency circular convolution theorem, the fast implementation is proposed. Then, to evaluate the anti-noise performance and estimation accuracy, the output signal to noise ratio (SNR) and asymptotic mean square errors (AMSEs) of estimated parameters are derived in closed forms. Theoretical analyses and numerical simulation results reveal that the RFAF-based method outperforms several state-of-the-art algorithms in terms of anti-noise performance and parameter estimation accuracy, and its computational efficiency is high.

中文翻译：

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雷达高速目标检测距频自相关函数的计算实现及渐近统计性能分析


在我们之前的工作中，提出了基于距离频率自相关函数（RFAF）的算法用于雷达目标检测和运动参数估计。基于RFAF的方法中，针对距离频率构造对称自相关函数(SAF)，可以连贯地完成三维(慢时、距离频率和移频)能量积累。本文作为对基于RFAF方法的进一步研究，研究了RFAF的快速实现和详细的渐近统计性能分析。首先，基于频率循环卷积定理，提出了快速实现方法。然后，为了评估抗噪声性能和估计精度，以封闭形式导出估计参数的输出信噪比（SNR）和渐近均方误差（AMSE）。理论分析和数值模拟结果表明，基于RFAF的方法在抗噪声性能和参数估计精度方面优于几种最先进的算法，并且计算效率很高。