The ground based electro-optical tracking system (EOTS) and electro-optics and infrared (EO/IR) are the most popular small UAV (C-sUAV) detection systems. The EO/IR systems are able to detect sUAVs at a long distance about several kilometers under clean environment. However, its performance is degraded in various noises like fixed patterns, dead/bad pixels and complex background conditions such as saturated images or foggy environments. In this study, we propose an efficient methodology using high power laser radar for real time CsUAV systems. The goal of our system is to find a 0.5 meter sUAV at 2 kilo meter distance in real time. For that challenging goal, we use a laser radar with dual pan-tilt scanning systems and also apply the variable-radially bounded nearest neighbor (V-RBNN) methodology as a fast clustering method. The experimental results show that the proposed method is able to detect 0.5 meter sUAV and its calculation time is under 20 millisecond per frames in complex background and long range conditions.
For detection of a small target using electro-optical systems, multi-band 2D image sensors are used such as visible, NIR, MWIR, and LWIR. However, 2D imaging systems are not capable to detect a very small target and they are also not capable of calculating target 3D position coordinates to develop the strategic counter method. 3D sensors (e.g. Lidar, RGBD and stereo camera) are utilized to control unmanned vehicles for detecting threats and response for specific situations. Conventional Lidar systems are unable to detect small drone threat at distances higher than their maximum detecting range of 100 ∼ 120 meters. To overcome this limitation, laser radar (LADAR) systems are being developed, which allow the detection at distances up to 2 kilometers. In the development of LADAR, it is difficult to acquire datasets that contain cases of long distant targets. In this study, a fusion data generation with virtual targets technique based on minimum real LADAR initial map dataset is proposed, and precise small target detection method using voxel-based clustering and classification are studied. We present the process of data fusion generation and the experimental results for a small target detection. The presented approach also includes effective visualization of high-resolution 3D data and the results of small target detection in real time. This study is expected to contribute to the optimization of a drone threat detection system for various environments and characteristics.