Modified Levenberg–Marquardt-Based Optimization Method for LiDAR Waveform Decomposition

Abstract

A modified Levenberg-Marquardt (LM) method is proposed to improve the waveform-decomposition efficiency of light detection and ranging (LiDAR). The conventional constant-model-based LM fitting algorithm is subsequently modified using two proposed models: the linear model and exponential model. By revising the update coefficient of the damping term to make it consistent with the variation of residual error, the magnitude of oscillation is effectively reduced to provide better convergence. The models were experimentally verified using observed data acquired by our experimental large-footprint LiDAR system. The results indicate that the two modified LM-based algorithms provide better performance in terms of convergence speed and iteration efficiency for waveform decomposition in comparison with the traditional algorithm. Most prominently, the exponential LM algorithm provides 69% maximum improvement in convergence speed and 103% in acceptable iteration efficiency in comparison with the traditional algorithm.