A Hybrid Frequency Estimation Framework for Long-Range FMCW LiDAR Under Low-SNR Conditions

Frequency-modulated continuous-wave (FMCW) light detection and ranging (LiDAR) has become increasingly significant for long-distance ranging fields. However, the beat signal is vulnerable to noise interference during detection, resulting in a low signal-to-noise ratio (SNR) and inaccurate ranging results. Although traditional methods have improved accuracy through various denoising and spectral analysis algorithms, they often struggle to maintain robust performance in long-range scenarios under low-SNR conditions. To address this challenge, this paper proposes the HOIGL, a hybrid frequency estimation algorithm which integrates Orthogonal Matching Pursuit (OMP) for initial value guidance and Gray Wolf Optimization (GWO) for local search. Specifically, by leveraging the frequency-domain sparsity of the signal, OMP performs piecewise sparse representation to yield a coarse initial frequency, within which GWO conducts a refined continuous local search to overcome dictionary discretization bias based on a custom fitness function. Monte Carlo simulations in MATLAB demonstrate that the proposed algorithm achieves an RMSE of 2.63 m compared to over 84.34 m for other methods at -20 dB SNR, while maintaining a low and stable processing time of around 0.498 s. Finally, fiber-optic-link experimental results verify the performance of HOIGL for hundred-meter-scale range detection.