## 3 FMCW LiDAR Research Progress
### Progress in Signal Processing Research  
In order to optimize ranging performance and overcome the influence of environmental factors, and because traditional methods are often limited by hardware performance and algorithm adaptability, researchers are constantly exploring more advanced signal processing algorithms and system designs. In 2020, a team from Yokohama National University in Japan made significant progress in silicon-based modulator technology by developing an in-phase/quad-phase (I/Q) modulator [43]. This modulator integrates a compact photonic crystal phase shifter, efficiently producing carrier-suppressed single-sideband modulated signals. By finely adjusting the frequency and phase of the modulating signal through precise timing control, the researchers achieved a frequency-modulation performance characterized by a side-mode  
Figure 12: Experimental setup of the proposed FMCW LiDAR system for unambiguous measurement on distance and velocity. CC: control circuit; FC: fiber coupler; VOA: variable optic attenuator; AOM: acousto-optic modulator; PCL: fiber collimator lens; TFP: thin film polarizer; HWP: half-wave plate; QWP: quarter-wave plate; BD: balanced detector. © Elsevier Copyright 2023, Optics and Lasers in Engineering [42]. rejection ratio of 22.3 dB. This technology not only improves the modulation efficiency of the signal but also effectively addresses the performance degradation problem of traditional modulators in complex environments. In 2021, [PERSON] et al. [44] improved the empirical mode decomposition (EMD) algorithm by introducing a novel correlation coefficient criterion based on differential terms for the meticulous selection of effective intrinsic mode functions (IMFs). Additionally, they combined singular value decomposition and wavelet denoising techniques for echo signal-processing, successfully elevating the signal-to-noise ratio from 5.28 dB to 21.06 dB. This innovative methodology in signal-processing significantly reduces the impact of noise, marking a substantial advancement in the field. In 2022, researchers from Tsinghua University [45] undertook a comparative study of wavelet analysis and the short-time Fourier transformer (STFT) within the context of time-frequency analysis applications for FMCW LiDAR systems. The results indicated that although STFT boasts superior resolution and measurement accuracy, wavelet analysis excels in temporal and lateral resolutions and demonstrates a superior ability to filter noise compared to STFT. In 2019, the Hefei Institute of Physical Science of the Chinese Academy of Sciences [46] proposed a novel denoising method called the WT-VMD algorithm. This method merges the sparrow search algorithm (SSA) with variation mode decomposition (VMD), tailored specifically for LiDAR signal-processing. Testing on simulated LiDAR signals with 50, 100, and 1000 pulses showcased significant improvements in SNR, root mean square error (RMSE), and smoothness indicators. Specifically, signal-processing resulted in SNR improvements of 138.5%, 77.8%, and 42.8%, and reductions in root mean square error (RMSE) of 81.8%, 72.0%, and 68.8%, respectively. For aerosol and cloud signals, the SNR increased by 83.3%, 60.4%, and 24.0%, while the RMSE decreased by 70.8%, 66.0%, and 50.5%, respectively. The WT-VMD SSA-based joint algorithm is based on the dual needs of improving processing efficiency and accuracy. It optimizes the limitations of traditional algorithms in processing complex signals. It shows excellent performance in the denoising of actual LiDAR signals and effectively improves the accuracy of LiDAR signals. In 2023, the Tianjin University proposed a target information solving method and system for FMCW LiDAR to improve data processing efficiency and reduce hardware load [47]. In this method, the intermediate frequency (IF) signals in the up-sweep and down-sweep are first downsampled and processed by low-pass and high-pass filters, respectively. Power spectrum measurements are performed on the outputs of the low-pass and high-pass filters.