Two-Stage Frequency Estimation based on 1-bit Weighted Fusion and SQ-Rife

In the field of modern electronic warfare, high-speed and high-precision frequency estimation is the core technology for achieving effective electronic reconnaissance in complex electromagnetic environments. Aiming at the nonlinear distortion problem faced by traditional 1-bit quantization technology in the frequency measurement process and the high complexity of largepoint Fourier transform hardware implementation, this paper proposes a two-stage frequency estimation algorithm based on 1-bit weighted fusion and SQ-Rife. The algorithm proposes a 1-bit weighted fusion DFT calculation structure, which reduces the nonlinear distortion caused by 1-bit quantization while retaining the low computational complexity of 1-bit receiver technology. Then, through a twostage frequency estimation algorithm, after the low-point FFT algorithm calculation is completed, the local spectrum interval is refined, breaking through the limitation of traditional FFT relying on the number of points to improve resolution. Finally, in view of the FFT fence effect, a segmented quantization Rife (SQ-Rife) algorithm more suitable for FPGA platform is proposed. Simulation results show that under the condition of sampling frequency of 2 G and signal-to-noise ratio of 20 dB, the maximum frequency measurement error of our method is less than 0.30 MHz, and the RMSE has excellent performance of 0.14 MHz under high signal-to-noise ratio. The algorithm is deployed on the FPGA platform, and the single-cycle frequency measurement takes 352 ns at a 250 MHz clock cycle, and the LUT consumption is only about $18 \%$. The proposed algorithm effectively balances frequency estimation accuracy, computational complexity, and hardware resource consumption, offering an innovative solution for the engineering implementation of high-speed electronic reconnaissance systems.