Most existing shooting training and analysis systems are based on photoelectric detection methods, such as position-sensitive detector (PSD), or image recognition techniques. These methods are mainly designed for short- and medium-range applications and exhibit limitations in working distance and adaptability to complex environments, making them difficult to apply to long-range sniper training scenarios. Therefore, this study aimed to develop a measurement system for sniper aiming point trajectory that can achieve long-range and high-precision measurement under outdoor conditions, thereby providing a reliable tool for quantitative evaluation and technical analysis of long-range shooting training.

To meet the practical requirements of sniper training, a vision-based sniper aiming point trajectory measurement system was designed and implemented. Infrared light sources were deployed on the target as aiming point markers to enhance detectability under long-range conditions. At the gun side, a beam splitter was introduced to allow the image sensor to share the optical path with the sniper scope, ensuring consistency between the captured field of view and the shooter’s aiming view. By exploiting the high magnification and narrow field-of-view characteristics of the sniper scope, the imaging quality of aiming points at long distances was improved, while the requirements for image sensor resolution and infrared source power were reduced. To suppress background light interference in complex outdoor environments, an infrared narrowband filtering technique was incorporated into the imaging optical path to enhance the contrast between the marker signal and the background. For real-time processing of high-resolution and high-frame-rate image data, a hardware processing architecture centered on a field-programmable gate array (FPGA) was developed, and a two-stage image processing strategy based on regions of interest (ROI) was adopted to achieve high-precision and stable extraction of aiming point positions. In addition, an inertial measurement unit (IMU) was integrated into the system to reliably determine the firing moment through analysis of acceleration signal features. The related measurement data were transmitted wirelessly to a tablet terminal for real-time recording and comprehensive analysis.

Experimental results indicated that the system achieved a trajectory update rate of 100 Hz, enabling effective capture of subtle fluctuations during the sniper aiming process. The IMU-based firing moment detection method realized temporal alignment between the aiming trajectory and the firing event, with a detection accuracy of 97.7% (Table 1), providing a reliable temporal reference for quantitative analysis of aiming trajectory before and after firing. Displacement platform experiments were conducted to verify the measurement consistency and stability of the system at different distances. The trajectory measurement accuracies were 3.30 mm and 3.88 mm at distances of 100 m and 500 m, respectively (Fig.13). Long-range field tests and precision analysis further demonstrated that the system was capable of stably acquiring complete aiming point trajectories at kilometer-level working distances, and exhibited the capability to achieve a target-plane displacement resolution better than 5 mm. Comparative analysis with SCATT and FN Expert systems showed that the proposed system demonstrated distinctive advantages in terms of practical working distance and environmental adaptability (Table 3).

This study proposes and implements a vision-based sniper aiming point trajectory measurement system, which solves the problems of short working distance and poor environmental adaptability of existing systems. Through the coordinated design of infrared markers, an optical multiplexing scheme, and FPGA-based real-time processing architecture, high-precision measurement of aiming trajectories at kilometer-level distances is achieved under outdoor conditions. Experimental results verify the comprehensive performance of the system in terms of real-time capability, measurement accuracy, and stability, providing reliable support for quantitative evaluation and technical analysis in sniper training.