In order to solve the problem of detecting, tracking and estimating the size of “low, slow and small” targets (such as UAVs) in the air, this paper designs a single-photon LiDAR imaging system based on Geiger-mode Avalanche Photodiode (Gm-APD). It improves the Mean-Shift algorithm and proposes an automatic tracking method that combines the weighted centroid method to realize target extraction, and the principal component analysis (PCA) method of the adaptive rotating rectangle is realized to fit the flight attitude of the target. This method uses the target intensity and distance information provided by Gm-APD LiDAR. It addresses the problem of automatic calibration and size estimation under multiple flight attitudes. The experimental results show that the improved algorithm can automatically track the targets in different flight attitudes in real time and accurately calculate their sizes. The improved algorithm is stable in the 1250-frame tracking experiment of DJI Elf 4 UAV with a flying speed of 5 m/s and a flying distance of 100 m. Among them, the fitting error of the target is always less than 2 pixels, while the size calculation error of the target is less than 2.5 cm. This shows the remarkable advantages of Gm-APD LiDAR in detecting “low, slow and small” targets. It is of practical significance to comprehensively improve the ability of UAV detection and C-UAS systems. However, the application of this technology in complex backgrounds, especially in occlusion or multi-target tracking, still faces certain challenges. In order to realize long-distance detection, further optimizing the field of view of the Gm-APD single-photon LiDAR is still a future research direction.

With the wide application of UAVs in modern intelligent warfare as well as in civil fields, the demand for C-UAS technology is increasingly urgent. Traditional detection methods have many limitations in dealing with “low, slow, and small” targets. This paper presents a pure laser automatic tracking system based on Geiger-mode avalanche photodiode (Gm-APD). Combining the target motion state prediction of the Kalman filter and the adaptive target tracking of Camshift, a Cam–Kalm algorithm is proposed to achieve high-precision and stable tracking of moving targets. The proposed system also introduces two-dimensional Gaussian fitting and edge detection algorithms to automatically determine the target’s center position and the tracking rectangular box, thereby improving the automation of target tracking. Experimental results show that the system designed in this paper can effectively track UAVs in a 70 m laboratory environment and a 3.07 km to 3.32 km long-distance scene while achieving low center positioning error and MSE. This technology provides a new solution for real-time tracking and ranging of long-distance UAVs, shows the potential of pure laser approaches in long-distancelow, slow, and small target tracking, and provides essential technical support for C-UAS technology.

Geiger-mode avalanche photodiode (GM-APD) is a single-photon-detection device characterized by high sensitivity and fast response, which enables it to detect echo signals of distant targets effectively. Given that weak and small targets possess relatively small volumes and occupy only a small number of pixels, relying solely on neighborhood information for target reconstruction proves to be difficult. Furthermore, during long-distance detection, the optical reflection cross-section is small, making signal photons highly susceptible to being submerged by noise. In this paper, a noise fitting and removal algorithm (NFRA) is proposed. This algorithm can detect the position of the echo signal from the photon statistical histogram submerged by noise and facilitate the reconstruction of weak and small targets. To evaluate the NFRA method, this paper establishes an optical detection system for remotely detecting active single-photon weak and small targets based on GM-APD. Taking unmanned aerial vehicles (UAVs) as weak and small targets for detection, this paper compares the target reconstruction effects of the peak-value method and the neighborhood method. It is thereby verified that under the conditions of a 7 km distance and a signal-to-background ratio (SBR) of 0.0044, the NFRA method can effectively detect the weak echo signal of the UAV.

Long-range detection and localization of UAVs remain extremely challenging, as motion-induced spatiotemporal distortion and weak echo returns severely degrade imaging quality. Although single-photon LiDAR offers photon-level sensitivity and long-range capability, its performance in dynamic UAV detection under low SNR remains constrained by motion blur, sparse photon measurements, and detector resolution, leading to unreliable target detection and localization. We present a five-dimensional motion-aware reconstruction (5D-MR) framework that jointly models the temporal (time-of-flight), spatial (row and column position), photon-count, and frame-sequence features of triggered photon events to perform signal preprocessing and dynamic target detection. Signal photons are robustly extracted from an overwhelming background via a two-stage RANSAC temporal fitting followed by DBSCAN clustering in the frame domain. Trajectory prediction combined with subpixel interpolation yields motion compensation and super-resolved localization beyond the sensor’s native resolution. Simulation results demonstrate that, even at an SNR of −9.03dB with 0.5343 photons per pixel, dynamic targets are localized with angular errors below 0.1 pixel and range errors within 0.5 m. Field experiments further validate the method for a UAV target at 1.8 km, achieving motion-blur suppression and super-resolution location reconstruction at an SNR of −8.97dB with only 0.3550 photons per pixel, resulting in angular precision better than 0.01 ^∘ and size estimation errors below 0.02 m, representing nearly two orders of magnitude improvement in resolvable localization accuracy. The 5D-MR framework enables accurate 3D localization of dynamic UAVs under extreme photon-starved conditions, surpassing detector-array resolution limits and extending single-photon LiDAR to remote airspace monitoring and ultra-low-SNR dynamic imaging.

With the wide application of UAVs in modern intelligent warfare and civil fields, the demand for C-UAS technology is increasingly urgent. Traditional detection methods have many limitations in dealing with "low, slow, and small" targets. This paper presents a pure laser automatic tracking system based on Geiger mode avalanche photodiode (Gm-APD). Combining the target motion state prediction of the Kalman filter and the adaptive target tracking of Camshift, a Cam-Kalm algorithm is proposed to achieve high-precision and stable tracking of moving targets. The system also introduces two-dimensional Gaussian fitting and edge detection algorithms to automatically determine the target's center position and the tracking rectangular box, improving the automation of target tracking. The experimental results show that the system designed in this paper can effectively track UAVs in a 70 m laboratory environment and 3.07 km to 3.32 km long-distance scene and has low center positioning error and MSE. This technology provides a new solution for real-time tracking and ranging of long-distance UAVs, shows the potential of pure laser in long-distance "low-slow-small" target tracking, and provides essential technical support for C-UAS technology.