Cognitive Electronic Warfare.

Format:

Book

Author/Creator:

Haigh, Karen Zita.

Language:

English

Physical Description:

1 online resource (432 pages)

Edition:

2nd ed.

Place of Publication:

Norwood : Artech House, 2025.

Summary:

This book explores the application of artificial intelligence (AI) in electronic warfare (EW), providing a comprehensive introduction to cognitive EW systems. The second edition includes significant updates on AI and machine learning-driven technologies, adaptive systems, and practical examples, with insights drawn from recent conflicts such as the Ukraine War. It addresses challenges in congested and contested electromagnetic spectrum environments and emphasizes the importance of real-time situation awareness and decision-making in modern warfare. The book serves as a valuable resource for professionals in military, academia, and industry, offering foundational knowledge and practical tools for implementing AI in EW. Generated by AI.

Contents:

Cognitive Electronic Warfare: An Artifical Intelligence Approach, Second Edition

Foreword

Preface

Foreword to the 1st Edition

Preface to the 1st Edition

Introduction to Cognitive EW

The Vision of Cognitive EW

What Is a Cognitive System?

A Brief Introduction to AI

A Brief Introduction to EW

Cognition in EW

Civilian EW

Cognitive Radio, Cognitive Radar, and Cognitive EW

EW System Design Questions

EW Domain Challenges Viewed from an AI perspective

Situation Assessment for Electronic Support and EWBattle Damage Assessment

Decision Making for Electronic Attack, ElectronicProtect, and Electronic Battle Management

User Requirements

Reader's Guide

Conclusion

References

Objective Function

Observables that Describe the Environment

Clustering Environments

Control Parameters to Change Behavior

Metrics to Evaluate Performance

Creating a Utility Function

Utility Function Design Considerations

Example Observables, Controllables, and Metrics

Machine Learning Primer

Common ML Algorithms

Support Vector Machines (SVMs)

Artificial Neural Networks

Ensemble Methods

Other ML Algorithms

Generalization to Surprise

Hybrid ML

Open-Set Classification

Metalearning

Generative AI

Embedded ML

Training an ML Model

Algorithmic Trade-Offs

The Speed of AI

Electronic Support

Detection, Localization, and Signal Separation

Emitter Classification and Characterization

Feature Engineering and Behavior Characterization

Waveform Classification

Specific Emitter Identification

Performance Estimation

Multisensor Data Fusion

Data Fusion Approaches

Example: 5G Data Fusion for Localization

Distributed Data Fusion

Anomaly Detection

Causal Relationships

Intent Recognition.

Conclusion

Electronic Protect and Electronic Attack

Optimization

Multiobjective Optimization

Searching through the Performance Landscape

Scheduling

Reward Hacking

Anytime Algorithms

Centralized, Distributed, and Decentralized Optimization

Centralized Optimization

Distributed Optimization

Decentralized Optimization

Summary

Electronic Battle Management

Planning

Planning Basics: Problem Definition, and Search

Hierarchical Task Networks

Action Uncertainty

Information Uncertainty

Temporal Planning and Resource Management

Multiple Timescales

Game Theory

Human-Machine Interface

System Designers

Commanders, Mission Planners, and EWOs

Understanding the Human Users

Real-Time In-Mission Planning and Learning

Execution Monitoring

EW Battle Damage Assessment

In-Mission Replanning

In-Mission Learning

Updating Models

Exploitation, Exploration, and Active Learning

Reinforcement Learning Approaches

Data Management

Data Quality Control

Data Curation

Bias and Noise

Traceability

Data Modeling: Ontologies, Metadata, and Schemas

Ontologies

Metadata

Schemas

Data Management Practice

Data in an Embedded System

Data Diversity

Data Augmentation

Forgetting Data

Data Privacy and Security

Architecture

Software Architecture: Interprocess

Software Architecture: Intraprocess

Language Choices

Hardware Choices

Test &amp

Evaluation

Paradigm Shifts

Validating the Learning Process

Iterative Design and Test

Learning Assurance Process

Evaluate Learning Goals

Determine Range of Operational Effectiveness

Mixed-Fidelity Closed-Loop Testing.

Closed-Loop Test Framework

Multifidelity Vertically Integrated Test Framework

Mixed-Fidelity Test Framework

Integrating Third-Party Tools for System Analysis

Behavior-Based Models with Closed-Loop Effects

Stimulus-Response Effects

High-Level Behaviors

Detailed Transceiver Characteristics

Possible Future Capabilities

Smart Experimental Design

Computing Accuracy and Adequacy

Regression and Normalized RMSE

Classification and Confusion Matrices

Evaluating Strategy Performance

Adequacy: Strategy Performance over a Scenario

Ablation Testing

Verification Approaches

Formal Verification Methods

Empirical and Semiformal Verification Methods

Example Implementation: Scenario Driver

Getting Started: First Steps

Engineering Resilient Systems

Development Considerations

Choices: AI or Traditional?

ML Toolkits

RF Datasets and RF Data-Generation Tools

RF Datasets for ML

RF Data-Generation Tools

Projects

Project Idea: RF Fingerprinting

Project Idea: Open-Set Recognition

Project Idea: Detect Causal Connections

Project Idea: Describe Emitters

Project Idea: Classify WiFi and Bluetooth Interference

Project Idea: GNSS Signal Integrity and Spoofing Detection

Project Idea: Multisensor Fusion for Localization

Project Idea: Multiplatform Route Planning

Project Idea: Decision Loop and Online Learning

Project Idea: EW Decision Making with 5G Technologies

Project Idea: Augment Existing Data

Project Idea: Evaluate ML Models

Project Idea: Implement a DeepNet on an FPGA

Project Idea: Data Compression

Acronyms

About the Authors

Index.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

1-68569-104-8