Computational Implementation and Asymptotic Statistical Performance Analysis of Range Frequency Autocorrelation Function for Radar High-Speed Target Detection

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.