Supporting EMSOs through Edge AI and Cloud Computing
18 Nov 2020
Artificial intelligence technologies such as Deep Learning networks are much more than just executable algorithms on a sensor. The usage of AI essentially requires the processing of very large amounts of data, which are recorded by the sensors and are available for training. This type of AI learning using a large amount of labeled data is known as supervised learning. These data must be stored, structured and analyzed efficiently in order to be useful for the training of the Deep Learning network. Managing Big Data in turn requires new, high-performance IT systems. In the area of the Internet of Things (IoT), very capable IT architectures have emerged, which allows military sensor data to be stored, processed and analyzed based on a cost efficient and powerful technology stack (Cloud Native Computing). Above all, AI requires an end-to-end view of the systems and operational processes. The results achieved so far using Deep Learning networks in military sensors have been very promising. This is only the beginning, since many fields of application have not yet been considered and developed at this point in time.