用于条纹像距离提取的迭代加权质心方法

条纹阵列探测激光雷达具有测距精度高、作用距离远、探测景深宽和数据更新率高等显著优点，已被广泛应用于地形测绘、海岸带监测、城市三维重构、森林生态研究等领域中。传统的信号鉴别方法在回波信号距离提取过程中存在一定的局限性，影响了条纹阵列探测激光雷达的距离分辨能力和目标识别能力。针对这一问题在处理条纹图像时引入了一种迭代加权质心算法，讨论了该算法在质心定位中的独特优越性，结合条纹阵列探测激光雷达的信号分布特征确定了该算法中关键参数的选取。利用该算法在1.4 km的作用距离下获得了具有清晰边界特征的目标距离像，有效抑制了距离提取过程中产生的边界模糊效应，显著提升了系统的细节分辨能力，相比于传统的质心算法，测距精度提高了17%。

Iteratively weighted center of gravity algorithm used for depth extraction of streak image

Streak array detecting lidar has advantages of high ranging accuracy, long detecting distance, wide ranging gate and high data acquiring rate, which is widely used in topographic mapping, coastal zone monitoring, urban 3D remodeling, forest ecological research etc. The traditional method on signal discrimination has some limitations in the depth extraction of echo signal, which affects the capability of depth distinguish and target recognition of streak array detecting lidar. For this problem, an iteratively weighted centroid algorithm was introduced while dealing the streak images and the unique advantages on centroid location of the algorithm were discussed. The key parameters of the algorithm were determined according to signal distribution character of streak array detecting lidar. By using this algorithm, the range image with clear boundary feature of a target was obtained at the distance of 1.4 km. The boundary blurring effect was effectively suppressed in the depth extraction process and the resolution ability of the system was improved. Compared with traditional centroid algorithm, the ranging accuracy was improved by 17%.