LED-Lidar Echo Denoising Based on Adaptive PSO-VMD

Abstract

LED (light-emitting diode)-lidar (light detection and ranging) has gradually been focused on by researchers because of its characteristics of low power, high stability, and safety to human eyes. However, LED-lidar systems are easily disturbed by background light noise. Echo signal denoising is an essential work that directly affects the measurement accuracy of the LED-lidar system. The traditional variational modal decomposition (VMD) method in lidar signal denoising relies on practical experience to optimize the critical parameters of quadratic penalty factor α and the number of intrinsic mode function (IMF) components K globally, which is hard to denoise effectively. For this problem, a denoising method based on VMD with the adaptive weighted particle swarm optimization (PSO) is proposed in this work. The PSO-VMD method adaptively adjusts the weight value ω for different lidar echo signals and optimizes of the parameters α and K globally. The LED-lidar echo signals are denoised by moving average, VMD, and PSO-VMD. Using the denoised echo signals, the range compensation waveforms and the extinction coefficients are derived. The results show that the PSO-VMD denoised echo signal has the highest R-square value of 0.9972 and the minimum standard deviation value of 5.7369, while the values of r-square and standard deviation of the echo signal denoised by moving average and VMD method are 0.9902, 9.7450, 0.9945, and 7.3588, respectively. The derived distance compensation waveforms and extinction coefficients based on the PSO-VMD denoising have better stability than those based on the moving average and VMD denoising.

Keywords

• LED-lidar
• denoising
• variational modal decomposition
• particle swarm optimization