Particle swarm optimization-based variational mode decomposition method for enhanced wind detection of coherent Doppler wind lidar

The detection capability of pulsed Coherent Doppler Wind Lidars (CDWLs) is limited by low Signal-to-Noise Ratios (SNRs). Therefore, it is critical to develop suitable signal processing algorithms. This paper proposes an enhanced method that integrates Variational Mode Decomposition with Particle Swarm Optimization (VMD-PSO) for wind detection, using the effective detection range as the fitness function. By incorporating wind velocity continuity across range gates, the fitness function enhances the robustness and accuracy of the optimization process, particularly under low-SNR conditions. Simulation results demonstrate that the proposed fitness function improves the ability of the optimized VMD-PSO algorithm to process noisy signals, enhances the stability of extracting effective signals, and ensures wind velocity accuracy. Furthermore, using a developed CDWL system, the algorithm increased the detection range by up to 120.5% under the same pulse accumulation conditions. At the same detection capability, it reduced the required number of pulse accumulations, enhancing the overall performance of the CDWL system.