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EMD and VMD-GWO parallel optimization algorithm to overcome Lidar ranging limitations.

Pulsed Lidar can obtain rich target information in one pulse, but the echo pulse signal is extremely susceptible to low laser transmitting power and complex target environments, resulting in an amplitude that is too low, which affects detection efficiency and ranging accuracy. In this paper, a variational modal decomposition based on gray wolf optimizer (VMD-GWO) and an empirical mode decomposition (EMD) parallel for denoising and signal enhancement in pulse Lidar is proposed and demonstrated completely. First, the adaptive strategy EMD is used for denoising the signal to obtain effective information. The combination of optimal VMD parameters of quadratic penalty αv and decomposition mode k was obtained by using the GWO to select the modal component with the smallest center frequency as effective information. Second, EMD and VMD-GWO parallel optimization algorithms are used to reconstruct the signal to obtain denoising and enhanced signals. Finally, a real experiment was carried out with the pulse Lidar ranging equipment. Our method compared with EMD-soft, EMD-VMD,WL-db4//EMD-DT and WL-db4//VMD has achieved greater improvement. When the target distance and the reflectivity of the reflectivity plate are 30 m and 10%, respectively, the peak signal-to-noise ratio (PSNR) of the weak echo signal calculated by our method can reach 11.5284 dB. And when in the dead zone of the system ranging, it is effectively denoising and enhancing the signal.