Machine Learning Aided Electronic Warfare System

International Journal for Research in Applied Science and Engineering Technology, 2018

Machine learning-based (IDS) have become a critical component of safeguarding our economic and national security because of the massive quantities of data produced each day and the growing interconnection of the world's Internet infrastructures. The existing machine Learning Model technique may have difficulty comprehending the ever-increasingly complex distribution of data invasion patterns. With a small number of data points, a single deep learning algorithm may be ineffective at capturing different patterns for intrusive attacks. We presented CNN-LSTM Novel Intrusion Detection Model for Big Data to improve the efficiency of IDS-based CNN-LSTM even further (NIDM). NIDM uses behavioural traits and content functions to understand the characteristics when compared to earlier single learning model tactics, this strategy can improve the rate of intrusive attack detection.

Zenodo (CERN European Organization for Nuclear Research), 2022

As we know that surveillance in the air has become one of the important defence precautions that we must take. So, for that reason radar plays a very important role as it captures images that can fool a human eye and then converts it into process able data. Then this data can be sorted and classified by our Artificial Intelligence Algorithms. Even here we have a variety of models for this purpose but it is up to us and our data and the output we expect, which Algorithm should be used. Here in this Survey, we have researched on different Radar Systems as well as the different signals that it generates and many AI Algorithms that are currently used in the industry.

Computers & Mathematics with Applications, 1992

A.bstract-The goal of this paper is to consider, formulate, and solve prediction problems encountered in tactical air combat. The problem involves prediction and identification of continuoustrajectory air combat maneuvers where only partial/incomplete information is given. This problem is solved using a qualitative representation of the maneuvers and their implementation as a neural network. We have broken our central dynamical problem down into several smaller subproblems ("eigencm-ves"), which describe the states of a continuous-trajectory dynamic system. We find tiust the resulting sequences of vectors uniquely express the time evolution of interacting dynamic objects. This method has been used to describe the forms of relationships between accelerations and velocities (not the values themselves.) All possible modes of a system can be identified while offering a complete parametrization of all possible tactical maneuvers. Additional inforraation can be used to establish which of the several alternative behaviors will actually take place. These sequences serve as the symbolic input to the artificial neural network we have provided. We found that due to high correlation of input data, a single hidden layer could not satisfactorily distinguish (with at least 55-85% accuracy) simple one-on-one maneuvers, such as the Turn-In, from more complex two-on-one maneuvers; for this reason, two hidden layers were incorporated. For each layer, many different architectures and learning rules were tested; the network described here gives the best results (55-95% accuracy for partial information). Thus, we found that the neural network implementation provided a high-speed, fault-tolerant, and robust computational cell for the identification of tactical maneuvers and suggestions for a best countermaneuver. We note that, for the sake of completeness, we include considerable background material about neural networks also.

Proceedings of the ... International Scientific Conference "Strategies XXI", 2021

Artificial intelligence contributes greatly to enhance situational awareness, providing early warning and contributing to the decision making process in the hybrid warfare context. Artificial intelligence brings a paradigm shift to "new" wars and threats, powered by increasing availability of military data and rapid progress of artificial intelligence techniques. The purpose of this paper is to identify researchers' interest in the use of "artificial intelligence" in the "hybrid warfare" environment and to establish the topics they approach. In this respect, the aim of this paper is to produce a literature review by accessing a scientific database in order to perform an analysis on how connected topics, such as: machine learning, data mining, deep learning and artificial neural network are integrated in the military domain.

IRJET, 2020

Data flowing from radar and electronic warfare (EW) systems to the analyst's screen will determine the course of action in any given mission. Bearing in mind that decisions need to be made, at times in seconds, it's critical for radar and EW systems to quickly sift through that data and turn it into actionable intelligence. To achieve this goal, the industry is using artificial intelligence (AI), machine learning (ML), and deep learning (DL) techniques to program these systems and make them into smarter, more autonomous tools. The main objective of this project is to overcome the certain shortcomings in the present radar system i.e. they take a lot of time to detect the signals, accuracy is compromised in most cases and signal is highly prone to external interference. This project involves a combination of various fields to build a model that can be applied effectively for different radar applications. The radar signal learning is vital for reduction of clutter and noise. The dynamic processing is very important for artificial systems. The model was trained on 2.1 million signals of twenty-four different classes with varying SNR values in the range of-18 to 30. The model shows a promising accuracy of 96% for appreciable values of SNRs.

A.bstract-The goal of this paper is to consider, formulate, and solve prediction problems encountered in tactical air combat. The problem involves prediction and identification of continuous-trajectory air combat maneuvers where only partial/incomplete information is given. This problem is solved using a qualitative representation of the maneuvers and their implementation as a neural network. We have broken our central dynamical problem down into several smaller subproblems ("eigencm-ves"), which describe the states of a continuous-trajectory dynamic system. We find tiust the resulting sequences of vectors uniquely express the time evolution of interacting dynamic objects. This method has been used to describe the forms of relationships between accelerations and velocities (not the values themselves.) All possible modes of a system can be identified while offering a complete parametrization of all possible tactical maneuvers. Additional inforraation can be used to establish which of the several alternative behaviors will actually take place. These sequences serve as the symbolic input to the artificial neural network we have provided. We found that due to high correlation of input data, a single hidden layer could not satisfactorily distinguish (with at least 55-85% accuracy) simple one-on-one maneuvers, such as the Turn-In, from more complex two-on-one maneuvers; for this reason, two hidden layers were incorporated. For each layer, many different architectures and learning rules were tested; the network described here gives the best results (55-95% accuracy for partial information). Thus, we found that the neural network implementation provided a high-speed, fault-tolerant, and robust computational cell for the identification of tactical maneuvers and suggestions for a best countermaneuver. We note that, for the sake of completeness, we include considerable background material about neural networks also. 1. INTRODUCTION Picture an autonomous missile with built-in logic to identify and predict the motion of a designated airborne target. Or, alternatively, imagine a combat pilot engaged in tactical maneuvers against one or two enemy aircraft, who has a decision-aiding display which offers advantageous offensive and/or defensive countermaneuvers. We have implemented a multi-layer artificial neu-ral network (ANN), designed to identify countermaneuvers for several well-known and relevant tactical air combat maneuvers (TACM). These include the Turning-In, the Lead Turn, and the Flat/Rolling Scissors maneuvers, all one-on-one; two-on-one maneuvers include the Bracket and the Hook-and-Drag. The graphic depiction of all these maneuvers is displayed in Figure 1. A graphic representation of the Cunningham-Toon dogfight (10 May 1972) is also reprinted in Figure 2. Our approach provides an innovative methodology for TACM identification/prediction as well as the suggestion of countermaneuvers through in-loop utilization of ANNs, when faced with partial or incomplete information. The TACM problem had previously been tackled via veloc-ity/trajectory estimation, Kalman filtering, and various composite measures for threat/lethality. This module has the ability to 1) help an autonomous missile home in on a designated target aircraft engaged in tactical maneuvers, as well as 2) serve as an on-board decision-aiding device for the pilot, increasing his "situational awareness."

International Journal of Distributed Sensor Networks, 2013

Air surveillance radar tracking systems present a variety of known problems related to uncertainty and lack of accurately in radar measurements used as source in these systems. In this work, we feature the theoretical aspects of a tracking algorithm based on neural network paradigm where, from discrete measurements provided by surveillance radar, the objective will be to estimate the target state for tracking purposes as accuracy as possible. The absence of an optimal statistical solution makes the featured neural network attractive despite the availability of complex and well-known filtering algorithms. Neural networks exhibit universal mapping capabilities that allow them to be used as a control tool for capturing hidden information about models learned from a dataset. We use these capabilities to let the network learn, not only from the received radar measurement information, but also from the aircraft maneuvering context, contextual information, where tracking application is wor...

Land Forces Academy Review

Artificial Intelligence (AI) is playing an increasing role in planning and supporting military operations and becoming a key tool in intelligence and analysis of the enemy’s intelligence. Another field of application of AI is the field of application of autonomous weapon systems and vehicles. The use of AI is expected to have a greater impact on the military functions of human-machine interfaces (machine-learning, man-machine teaming). AI promises to get over the “3V challenge” (volume, variety and velocity) of Big Data, and is also expected to reduce the risks concerning the other “2V” (veracity, value), and to render data processing on a controlled level of decision based on AI’s knowledge. The aim of the article is to provide an overview on the potentials of application of AI in the military and to highlight the need to identify and define measurable indicators to evaluate benefits of state-of-the-art technologies and solutions which are expected to improve quality and performanc...

Artificial Intelligence Research, 2014

Missile guidance systems using the Proportional Navigation (PN) guidance law is limited in performance in supporting wide class of engagement scenarios with varying mission and target parameters. For surpassing this limitation, an Artificial Neural Network (ANN) to substitute the PN guidance is proposed by the authors. The ANN based system enables learning, adaptation, and faster throughput and thus equips the guidance system with capability akin to intelligent biological organisms. This improvement could remove the barrier of limitations with allowable mission scope. In this paper, a Multi-Layer Perceptron (MLP) has been selected to implement the ANN based approach for replacing PN guidance. Attempts to replace PN guidance using MLP are limited in the literature and warrant greater attention due to significant theoretical development with the MLP field in recent times. It is shown in this paper, that the MLP based guidance law can effectively substitute PN for a wide range of engagement scenarios with variations in initial conditions. A foundational argument to justify using an MLP for substituting PN is provided. Besides this, the design, training and simulation based testing approach for an MLP to replace PN has been devised and described. The potential for faster throughput is possible as the MLP nodes process information in parallel when generating PN like guidance commands. The results clearly demonstrate the potential of MLP in future applications to effectively replace and thus upgrade a wide spectrum of modern missile guidance laws.

Electronics, 2021

The paper presents an overview of current and expected prospects for the development of artificial intelligence algorithms, especially in military applications, and conducted research regarding applications in the area of civilian life. Attention was paid mainly to the use of AI algorithms in cybersecurity, object detection, military logistics and robotics. It discusses the problems connected with the present solutions and how artificial intelligence can help solve them. It briefly presents also mathematical structures and descriptions for ART, CNN and SVM networks as well as Expectation–Maximization and Gaussian Mixture Model algorithms that are used in solving of discussed problems. The third chapter discusses the attitude of society towards the use of neural network algorithms in military applications. The basic problems related to ethics in the application of artificial intelligence and issues of responsibility for errors made by autonomous systems are discussed.