## 1 Introduction  
Lidar technology demonstrates significant potential in high-precision distance measurement by virtue of its high-precision ranging capability [1, 2, 3, 4]. Among various lidar systems, streak-tube imaging lidar stands out for its exceptional temporal resolution and 2D imaging high efficiency [5]. This feature makes STIL particularly effective in capturing dynamic scenes, which is crucial for applications that require precise range measurements, such as underwater target imaging [6, 7]. The ability of STIL to provide high-resolution temporal data ensures accurate target tracking and detailed analysis, making it highly advantageous in sea wave detection, where both precision and speed are critical.  
To enhance the performance of STIL systems, optimizing both system design and data processing techniques has become a significant research focus. For instance, [PERSON] et al. introduced 4D imaging of ocean waves using STIL, demonstrating its potential in dynamic environmental monitoring [8]. Similarly, [PERSON] et al. explored real-time 3D imaging with streak tube Lidar using LabVIEW, providing important insights into real-time data processing and software integration [9].  
Further developments by [PERSON] et al. and [PERSON] et al. focused on improving the physical design of streak tubes, both through numerical simulations and experimental verification, enhancing the compactness and functionality of streak tube cameras [10, 11]. [PERSON] et al. extended this by integrating kilohertz laser pulses with STIL for few-photon detection, which is crucial for low-light applications [12].  
Researchers have proposed various improvements in system architecture [13, 14, 15] and data processing methodologies [16, 17] to increase both accuracy and efficiency. The current state of the art is also characterized by innovations in signal processing and image optimization. For example, [PERSON] et al. applied Gaussian fitting for 3D reconstruction, which is a critical technique for enhancing measurement accuracy [18]. Similarly, [PERSON] et al. and [PERSON] et al. contributed to the improvement of point cloud data homogenization and the application of machine learning techniques like OSA-YOLOv5s for automatic feature extraction from lidar images [19; 20]. Previous studies have extensively explored system parameter optimization [21; 22; 23; 24] and advanced data processing techniques [25; 26] to enhance measurement accuracy. Originally developed for underwater measurements, STIL has been extended to ground target measurements, where ranging accuracy is a critical factor in determining the system's detection performance, particularly in long-range ground measurements. However, there remains a notable gap in the precise design and verification of ranging accuracy, especially in the context of long-distance ground measurements.  
In this study, we present a self-developed STIL system specifically designed for long-range target detection. To achieve enhanced ranging accuracy, we meticulously optimized both the laser emitter and receiver components. Central to the receiver design is a custom streak camera, which not only effectively addresses the challenges posed by the far-field characteristics of the detection laser but also ensures high gain and an improved signal-to-noise ratio (SNR) during streak image echo recording. Following these optimizations, we conducted a quantitative evaluation of the system's ranging model. Subsequently, we performed a long-range ground experiment to validate the system's ranging accuracy, thereby demonstrating the effectiveness of our design improvements.