Deep Learning Algorithms produce better-than-human results in image recognition, generating a close to zero fault rate [1]. This article shows how this works in radar technology and explains, how Artificial Intelligence can be taught in University Education and NextGen ATC qualification.
Deep learning is a machine learning method based on artificial neural networks. It uses multiple layers to progressively extract higher level features from the raw input.
For example, in radar data processing, lower layers may identify reflecting points, while higher layers may derive aircraft types based on cross sections.
Or even a malicious intent, based on the pattern of group behavior or planes.
An alarm situation could be derived from navigational patterns of an aircraft (rapid sinking, curvy trajectory, unexplained deviation from the prescribed trajectory etc.), indicating a technical or human-caused emergency.
Branka Jokanovic and her team made an experiment using radar to detect the falling of elderly people [2]. The "trained" radar was able to differentiate between four human motions (walking, falling, bending/straightening, sitting). The results of her experiments demonstrated the superiority of the deep learning approach over any conventional
method for in discriminating between the different considered human motions [2].
Similar to cognitive radio networking and communication, AI can play the role of cognitive decision maker, for example in cognitive radar antenna selection:
Another example is the segmentation of radar point clouds [4] through deep learning algorithms.
Most inspiring is the work by Daniel Brodeski and his colleagues [5]. The team uses IQ data for detection and localization of objects in the 4D space (range, Doppler, azimuth, elevation). Already today, the approach outperforms traditional radars. Such a deep-learning based process may lead to nothing less than the replacement of the classical radar signal processing chain. Apart from the initial system training process, it turns many of the cost drivers and time burners obsolete such as the radar calibration process.
The quality of the artificially intelligent system relies on the quality of the available labelled dataset. Labeled data is a group of samples that have been tagged with one or more labels. In the radar case it could be either synthetically generated data (relying on the quality of the sensor model), or radar calibration data, generated in an anechoic chamber on known targets with a set of known sensors.
The creation of the machine learning model can be segmented into three main phases:
Brodeski and his team stage the object detection process into 4 steps:
Many people are afraid of AI, or consider it a threat. We see it as a huge opportunity. Our objective is to enable our users to use AI as a tool to generate better, faster, safer and more economical results.
SkyRadar offers to use our systems to learn
Understanding AI means understanding the whole processes. This is why our approach is to make students work through the process from A to Z.
SkyRadar's systems make it easy to organically grow into the new technology.
As a university or aviation academy, you will get all you need to set up your learning environment including teach-the-teacher support. The systems are designed in such a way, that universities and research bodies can use the environment to develop further solutions and to exchange and discuss them with our ecosystem of users and experts.