The LADAR system is a device that generates a depth map using reflected laser range information after irradiating a laser pulse onto a terrain or target. In recent years, it is important to acquire accurate 3D coordinates of target objects with target identification from 3D raw data.1–3 The existing LADAR system does not have the function to calculate the target coordinates, but recently, its coordinate system LADAR is actively researched to find the target coordinates. In order to accurately calculate the target coordinates, accurate position information (GPS) of the LADAR system and distance to the target and angle of the laser are required. Generally, digital magnetic compass (DMC) should be used to obtain accurate angles. However, in a region in strong magnetic fields, DMC cannot guarantee its accuracy and its usage is limited. In this paper, we propose a coordinate calibration system to replace DMC and a method to extract accurate angle information using GPS and encoder for robust target coordinate extraction to the magnetic field variation. As experimental results of the proposed system, it is confirmed that it is a robust system in the magnetic field environment compared with the coordinate system using the existing DMC. This is an improved technique for obtaining accurate target coordinates in various environments. Using the proposed LADAR system, it is possible to construct a smart defense system to extract the precise target latitude and longitude coordinate system and to transmit the information to the associated Missile base and command center.
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.
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