Full-waveform LiDAR echo decomposition method

Abstract

The echoes of full-waveform light detection and ranging (LiDAR) contain information on the targets such as distances and properties. To access such information, we demonstrate an echo decomposition method based on soft-thresholding Empirical Mode Decomposition (EMD-soft) filter, Levenberg-Marquardt (LM) optimization, and a method to estimate the initial parameters consisting of a variety of waveforms. This method decomposes raw full-waveform echoes into independent Gaussian pulses and derives their function expressions. Distances and properties of the targets can be extracted from the parameters of these Gaussian pulses. First, EMD-soft is used to filter the noise of raw echoes and estimate the amount of noise. Then, initial parameters for follow-up optimization are estimated through peak and inflection points of the filtered echoes by a given process. Lastly, after these initial parameters are input, LM optimization is applied to optimize the parameters, and function expressions of Gaussian pulses can be obtained through the parameters. Finally, information on the targets can be accurately extracted. Simulations and experiments are carried out to evaluate the performance of the proposed method. Simulated waveforms are generated by adding Gaussian white noise into a waveform consisting of several independent Gaussian pulses. Decomposition experiments of these simulated waveforms indicate that the decomposition error of this method is around 0.1 ns, which means 15 mm in ranging error. In actual experiments, a laboratory-built full-waveform LiDAR experimental system is utilized to generate and obtain echoes. Decomposition experiments of these real waveforms also show that the ranging error of this method is less than 0.1 m. Compared with two other Gaussian decomposition methods in decomposing the same echoes mentioned above, this method shows enhanced decomposition success rate and accuracy. The proposed method realizes high success rate and accuracy decomposition. It can provide reliable data for the subsequent analysis and will play an important role in many fields, such as remote sensing and mapping. Compared with other decomposition methods, the EMD-soft we used does not have a threshold of filter frequency, that is, it will be effective in any frequency, unlike some other filters that will make mistakes when close to their thresholds. In addition, EMD-soft does not need a given kernel function or parameter, which means it is a more generic method to deal with various LiDAR echoes. LM optimization is one of the most popular algorithms for optimizing LiDAR echoes, but requiring high-accuracy initial parameters is a big problem for the algorithm. Sometimes if the initial parameters do not have enough accuracy, the algorithm may converge to a local minimum instead of a global minimum and lead to a wrong fitting. Thus, in this method, a special process is realized in the section on evaluating initial parameters and enhancing the optimization effect from another way. Echo decomposition is the foundation of echo analysis. We will try to extract more properties of targets from the decomposed waveforms and build the functional relationship of these properties with the parameters of waveforms in future work.