LAUSR

The photon-counting technique is finding increasing applications in laser ranging systems owing to its high detection sensitivity and time resolution; however, it is still challenging to extract moving tracks accurately from an intensive noise background for non-cooperative targets using the photon-counting technique. To resolve this issue, a ranging method based on the Hough transform is proposed according to the distribution characteristics of a moving target in a point cloud figure. The proposed method divides the ranging process into the initial stage and the tracking stage. A large amount of point cloud data is used to acquire an accurate measurement of the initial distance and velocity of the target in the initial stage, whereas a smaller amount of point cloud data is used to rapidly update the distance and velocity for real-time processing in the tracking stage. The experimental results demonstrated that compared with the local distance statistics method and the density-based filtering method, the proposed method could extract target tracks effectively with decreased time consumption and lower ranging errors in different detection conditions.