针对全波形激光测距中存在的幅相误差问题, 提出一种基于神经网络的幅相误差改正方法。利用非合作目标探测信息, 通过提取回波波形的形状信息、能量信息、梯度信息、对称性信息及距离信息特征参数, 根据皮尔逊相关系数对特征参数进行分级, 建立多回波特征信息与幅相误差改正的神经网络模型以校正全波形激光测量中各通道幅相误差的影响。实验使用 5%、20%、60%、80%标准反射板及激光采集模块在室内对 7种距离进行数据分组采集和处理, 并与传统测量方法进行对比。结果表明, 该方法可以有效减小全波形激光测量中幅相误差的影响, 测量精度提高了 51.2%以上。

Aiming at the problem of amplitude and phase errors in full-waveform light detection and ranging (LiDAR), a method based on neural network and pulse feature parameters is proposed. The feature parameters of waveform, including shape information, energy information, gradient information, symmetry information, and distance information were extracted from non-cooperative target detection information and graded based on the Pearson correlation coefficient. A neural network model combining multi-echo characteristic information with amplitude-phase error correction was established to correct the amplitude and phase errors of each channel in full-waveform laser measurement. The experiment used 5%, 20%, 60% and 80% standard reflectorsand laser acquisition modules to collect pulse data at 7 different distances. The processed data was also compared with the traditional full waveform measurement method. The results show that this method could effectively reduce the influence of amplitude and phase errors in the full-waveform laser measurement, and the measurement accuracy is improved by more than 51.2%.