Fast Fourier transform (FFT) is a widely used method for frequency estimation in electronic support systems. However, when the intermediate frequency (IF) of the radar signal is not an exact multiple of the FFT resolution, the correct frequency value cannot be obtained in the FFT computation. Therefore, interpolation methods are used to improve the frequency obtained from the FFT result. In this study, 12 different interpolation techniques (Jain, corrected Jain, Quinn, improved Quinn, Jacobsen, Macleod, Ding, Voglewede, mobile industrial (MI), Candan, rectangular-window-based interpolation, and Hanning window based interpolation) used in the literature have been extensively analyzed on radar signals contaminated with Laplace and Gaussian noise at different SNR values. In addition, in order to observe the performance of the techniques in different frequency bands, the bandwidth was changed to between 100 and 1000 MHz, and 100 Monte Carlo simulations were applied for each frequency. From the experimental analysis results, the improved Quinn technique showed the best performance for both noises. In addition to accuracy evaluations, the computational complexity of each interpolation technique was analyzed in terms of floating-point operations (FLOPs). The FLOPs cost of the FFT was uniformly included in all methods to ensure fair comparison. Results showed that while all techniques operate within a similar computational range, methods like Jain and Candan exhibit lower FLOPs costs, whereas the improved Quinn method, despite its higher complexity, achieves the best estimation accuracy.

Fast Fourier Transform, Frequency Estimation, Gauss Noise, Interpolation, Laplace Noise