Many Path Planning (PP) techniques have been proposed, but a majority of the existing techniques equally evaluate many spaces of the maps, including unlikely ones, thereby creating time performance issues. However, computing time can be significantly reduced by searching for paths solely in the spaces that have the highest priority of providing an optimal/sub-optimal path. A major portion of the computing time is usually wasted on modeling and exploration of spaces that have a very low possibility of providing optimal/sub-optimal paths.
#Space warfare radar display full#
Overall, we demonstrated through simulations that our proposed model achieves simultaneous detection, classification, localization, and tracking of a target.įinding an optimal/quasi-optimal path for Unmanned Aerial Vehicles (UAVs) utilizing full map information yields time performance degradation in large and complex three-dimensional (3D) urban environments populated by various obstacles. The database is used to develop several machine learning (ML) models using different algorithms to classify a target. A database of different types of aerial vehicles is created artificially based on Gaussian distributions.
As an additional feature, steering of the beams can be used for increased coverage and improved localization and classification performance. Any aerial vehicle disrupting the path of the laser beams are detected and subsequently localized and tracked. The laser mesh acts as a virtual net in the sky. The proposed approach is based on creating a mesh structure of laser beams initiated from aerial platforms towards the ground. In this paper, a novel technique as an alternative to radar technology is proposed. The radar systems at their maximum operating ranges, however, are not always able to detect stealth and small UAVs mainly due to their small radar cross-sections and/or low altitudes. The stealth technology and unmanned aerial vehicles (UAVs) are expected to dominate current and future aerial warfare. Overall, we demonstrated through simulations that our proposed model achieves simultaneous detection, classification, localization, and tracking of a target. The database is used to develop several Machine Learning (ML) models using different algorithms to classify a target.
The radar systems at their maximum operating ranges, however, are not always able to detect stealth and small UAVs mainly due to their small radar cross sections and/or low altitudes. Stealth technology and Unmanned Aerial Vehicles (UAVs) are expected to dominate current and future aerial warfare.
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