Small unmanned aerial vehicles (UAVs) are becoming more and more popular and also a challenge for civilian and military security. A UAV has to be detected first, but due to environmental condition (e.g. night or fog) the detection is impeded. To assess the threat of a possible hostile UAV, identification is helpful. If the type of UAV can be determined, information about size, payload, velocity and range can be given and countermeasures can be considered. Identification of UAVs can be more accurate using multiple spectral ranges at the same time.We present a systematic approach for acquisition of multispectral signatures in the field and in the lab, structured storage in a database and composition of partially synthetic images as training data for identification in an artificial neural network. We set up a multispectral camera system comprised of three imagers, in the visible spectrum, SWIR and MWIR. The cameras are externally triggered. This allows an image acquisition in the field with a synchronized video stream. In addition to that, high resolution images are made in the lab from different angles all around the micro UAV. A specific background is chosen, so it will be masked and with a given real world background image a partially synthetic image can be generated. These can be validated with data that was gathered in the field. Both are stored in a database, along with metadata, to allow access to particular data when needed. Synthetic images and signatures from the field can be used as multispectral training data for an artificial neural network to enable identification of a UAV.
Due to the growing number of small and agile unmanned aerial vehicles (UAVs), including consumer micro-drones, appropriate countermeasures technologies are necessary to protect public and military forces, to increase critical infrastructure resilience, and to secure exchange of data. Most of the countermeasure requires reliable up-to-date position information of the approaching threats. Beside precise determination of the angle-of-arrival laser pulse time-of-flight information is one of the promising technologies to measure the distance to a target. Laser Range Finders (LRF) are typically used for long ranges to large objects or slowly moving targets. Within the scope of this paper we are going to show a method to enhance laser ranging capabilities to small and fastmoving UAV targets. Dealing with small and agile targets the primary limitation of many laser ranging systems is the reduced hit rate. The restricted torque of the pan-tilt-unit drives is not able to align the LRF in the direction of agile UAV targets in the sky. In this paper, we will present a method using an additionally piezo steering device to reduce this residual tracking error. To estimate the improvement, we are going to compare results under same conditions with and without the fast steering device. Experimental evaluations show an improvement of the LRF hit rate during high accelerations of micro UAVs. We present theoretical analysis and experimental results of UAV laser range measurements under realistic environmental conditions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.