Laser frequency sweep linearization by iterative learning pre-distortion for FMCW LiDAR.

Area of Science:

• Optics and Photonics
• Laser Technology
• Sensing and Measurement

Background:

• Frequency-modulated continuous-wave (FMCW) light detection and ranging (LiDAR) systems rely on precise laser frequency sweeps.
• Nonlinearity in laser frequency sweeps degrades the range resolution and accuracy of FMCW LiDAR systems.
• Existing methods for linearizing laser frequency sweeps can be complex and expensive.

Purpose of the Study:

• To develop a novel method for linearizing laser frequency sweeps in FMCW LiDAR systems using iterative learning pre-distortion.
• To derive a figure of merit for residual nonlinearity to achieve transform-limited range resolution.
• To demonstrate the effectiveness of the proposed method using commercial lasers.

Main Methods:

• An iterative learning controller is employed to generate a pre-distorted laser drive voltage waveform for linear frequency sweeps.
• The pre-distorted waveform is applied to the FMCW LiDAR system.
• Experimental validation is performed using a vertical cavity surface-emitting laser (VCSEL) and a distributed feedback (DFB) laser.

Main Results:

• The proposed iterative learning pre-distortion method successfully linearizes the laser frequency sweep.
• A relative residual nonlinearity of less than 0.005% was achieved in experimental tests.
• The method enables high-performance FMCW LiDAR systems.

Conclusions:

• The iterative learning pre-distortion method provides an effective and cost-efficient solution for linearizing laser frequency sweeps in FMCW LiDAR.
• This approach eliminates the need for expensive linear lasers, complex linearization setups, or heavy post-processing.
• The developed technique facilitates the realization of high-performance FMCW LiDAR systems.