SkyRadar Blog

Martin Rupp

Martin Rupp is a cryptographer, mathematician and cyber-scientist. He has been developing and implementing cybersecurity solutions for banks and security relevant organizations for 20 years. Currently he is researching attack scenarios and the role of AI in ATC cyber-security.

Artificial Intelligence: Hidden Markov Model Classifiers and RADAR Objects Classification by Machine Learning 1/2

In this article we will explore a very special class of classifiers, the Hidden Markov Model Classifiers (HMMs). They are mainly a statistical and probabilistic model but they have found their entry in the world of Machine Learning since they can be trained and classify data.

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Flight 752: Why ADS-B and A.I. Should Be Applied Together

The recent crash of Ukrainian Airlines Flight 752, downed by a surface-to-air (SAM) Iranian missile after a tragic human error from a military RADAR operator underlines dramatically the need for ADS-B, combined with automatic computer-based recognition of RADAR targets.

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Artificial Intelligence - Anatomy of the LeNet-1 Convolutional Network and How It Can Be Used in Order to Classify RADAR Data

In this article we shall perform the anatomy of a simple but efficient convolutional network (CNN), the LeNet-1 neural network.

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Artificial Intelligence - Understanding the Main Operations in Convolution Neural Networks for RADAR Data Classification

RADAR data classification mainly relies on convolutional neural networks. In this article, we shall detail and explain the main operations performed by Convolution networks in order to classify RADAR data.

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Artificial Intelligence - A Short Overview of Classification Techniques for RADAR Targets through Neural Networks 2/2

A relevant technique to classify RADAR object is to use a colored 2D map representing range, speed or frequency against time and color the map with power intensity. Here we represent examples of such typical RADAR data.

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Artificial Intelligence: A Short Overview of Classification Techniques for RADAR Targets through Neural Networks (1/2)

Fast reaction and decision-making of the RADAR operator is a key factor in ATC. There is a need to develop techniques for the automatic extraction and fine-granulated classification of RADAR objects, allowing for faster and better decision making and more effective processes.

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Artificial Intelligence: Important Aspects of Neural Networks Applied to Radar Systems

RADAR is short for RAdio Detection And Ranging. The principle of RADAR systems is not very difficult to understand. It is simply because most solid objects reflect radio waves, e.g., electromagnetic radiation with wavelengths superior to infrared. A RADAR therefore sends radio waves through an emitter and captures the “response”, e.g the reflecting signal through a receiver.

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