In recent years,hybrid solid-state LiDAR has gained widespread application across aerospace,autonomous driving,and UAV remote sensing due to its reasonable cost and advanced manufacturing technology. Despite its advantages in portability and long-range detection capabilities,many researchers overlook the inherent challenges such as systematic errors,random interference,and instability issues,particularly the “edge tailing” effect within the near-field range of tens of meters. This phenomenon significantly impairs the reliability of close-range detection and testing tasks. This article begins by outlining the fundamental 3D imaging principles of solid-state LiDAR and discusses the unpredictability of near-field detection errors. It introduces a method for analyzing a measured target's 3D point cloud data using the Oriented Bounding Box (OBB) algorithm,establishing a framework for subsequent data acquisition and analysis. Experimental statistical methods were then employed to quantitatively analyze the measurement results,elucidating the influence of systematic “edge tailing” on the direct fitting results of spheres. This study also identifies a variance in echo intensity between the tailing and central points. Leveraging the existing discovery,an automatic denoising method was devised to eliminate noise from the tailing point clouds,thereby reducing systematic errors. Moreover,the analysis reveals that measurement distance,target surface colour,and motion speed significantly contribute to random errors. Recommendations are made for optimizing working distance,target colour,and flight speed in near-field detection to minimize these errors and enhance measurement stability. A series of experiments were conducted to verify the effectiveness of these methods,measuring the attitude of a large angular velocity rotating target at a 30-meter range. Identification of “expansive” trailing points at the target edges,is enabling the establishment of a precise cutoff threshold for their filtering,meaning that optimal working distances enhance the accuracy of tracking and measuring cooperative targets,with white being the preferred target colour for both day and night conditions. The necessity of defining the dynamic speed limit of the target to select a LiDAR with an appropriate frame rate,minimizes significant accuracy losses. For laser LiDAR systems with a nominal accuracy of ±2 cm,the comprehensive error reduction methods proposed can maintain size measurements of rotating targets within ±3 cm at a 30 m near-field range. The conclusions of this study offer valuable guidelines for the application of hybrid solid-state LiDAR in the tracking and rendezvous of far-field and large targets.