Full-waveform pulsed LiDAR dataset

A dataset comprised of full-waveform (periodically sampled) pulses emulating LiDAR signals is provided. The waveforms were captured at a sampling rate of 20 Gsample/s and over a dynamic range of 45 dB. A simple Python notebook is also given, showing how to properly load the waveforms and capture parameters from the stored .mat file. The dataset was used to demonstrate a new time-frequency estimation method for pulsed LiDAR systems [1]. The dataset was also used to build efficient machine learning (ML) models capable of accurate and precise time-of-flight estimations [2]. For full details, please check the Experimental Setup in [1]. Please contact Daniel Bastos ([e-mail]) for any further questions. References: [1] – D. Bastos, A. Brandão, A. Lorences-Riesgo, P. P. Monteiro, A. S. R. Oliveira, D. Pereira, H. Z. Olyaei and M. V. Drummond, , "Time-Frequency Range Estimation Method for Pulsed LiDAR," in IEEE Transactions on Vehicular Technology, vol. 72, no. 2, pp. 1429-1437, Feb. 2023, doi: 10.1109/TVT.2022.3207588. [2] – Daniel Bastos, Bruno Faria, Paulo P. Monteiro, Arnaldo S. R. Oliveira, and Miguel V. Drummond, "Machine learning-aided LiDAR range estimation," Opt. Lett. 48, 1962-1965 (2023), doi: 10.1364/OL.487000.本数据集包含模拟激光雷达（LiDAR）信号的全波形（周期性采样）脉冲。该波形的采集采样率为20 G样本/秒，动态范围达45分贝。同时附带一份简易Python脚本笔记本，演示如何正确加载该波形数据，并从存储的.mat格式文件中提取相关参数。本数据集曾用于演示一种面向脉冲激光雷达系统的新型时频估计方法[1]，还被用于构建可实现高精度飞行时间估计的高效机器学习（ML）模型[2]。完整细节请参阅文献[1]中的实验设置章节。如有任何进一步疑问，请联系Daniel Bastos（邮箱：[e-mail]）。 参考文献： [1] D. Bastos、A. Brandão、A. Lorences-Riesgo、P. P. Monteiro、A. S. R. Oliveira、D. Pereira、H. Z. Olyaei及M. V. Drummond，《面向脉冲激光雷达的时频距离估计方法》，发表于《IEEE车辆技术汇刊》，第72卷第2期，第1429-1437页，2023年2月，DOI: 10.1109/TVT.2022.3207588。 [2] Daniel Bastos、Bruno Faria、Paulo P. Monteiro、Arnaldo S. R. Oliveira及Miguel V. Drummond，《机器学习辅助激光雷达距离估计》，发表于《光学快报（Opt. Lett.）》第48卷，第1962-1965页，2023年，DOI: 10.1364/OL.487000。