Abstract:

In frequency-modulated continuous-wave (FMCW) lidar, the distance to an illuminated target is proportional to the beat frequency of the interference signal. Laser phase noise often limits the range accuracy of FMCW lidar, and existing frequency estimation methods make overly simplistic assumptions about the noise model. In this work, we propose an algorithm that performs frequency estimation via phase unwrapping by explicitly accounting for correlations in the phase noise. Given a candidate frequency, we approximately recover the maximum likelihood unwrapping sequence using the Viterbi algorithm and the phase noise statistics. The algorithm then alternates between unwrapping and frequency estimate refinement until convergence. Compared to state-of-the-art alternatives, our algorithm consistently achieves superior performance at long range or with large-linewidth lasers when the signal-to-noise ratio is sufficiently high.