Swarm Characteristics Classification Using Neural Networks

2024-03-28

IEEE Transactions on Aerospace and Electronic Systems

AI Summary

Addressing the challenge of autonomous swarm tactical recognition in defense scenarios, this paper proposes a supervised neural network-based time-series classification method. It enables real-time inference of communication states and proportional-navigation behaviors from short-duration (20-step), high-noise (50% measurement error), and multi-scale (10–100 agents) trajectory data, thereby classifying four mutually exclusive swarm tactics. This work constitutes the first systematic investigation into applying neural networks for swarm behavioral classification, introducing two key innovations: noise-robust training strategies and explicit temporal modeling of trajectory dynamics. Experimental results demonstrate classification accuracies of 97% under noise-free conditions and 80% under 50% measurement noise, with inference latency in the millisecond range. The method significantly enhances the timeliness of countermeasure decision-making and establishes a novel paradigm for intelligent perception and responsive action in dynamic adversarial environments.

Technology Category

• Multiagent Systems: Adversarial Agents
• Intelligent Robots: State Estimation
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Understanding the characteristics of swarming autonomous agents is critical for defense and security applications. This article presents a study on using supervised neural network time series classification (NN TSC) to predict key attributes and tactics of swarming autonomous agents for military contexts. Specifically, NN TSC is applied to infer two binary attributes—communication and proportional navigation—which combine to define four mutually exclusive swarm tactics. We identify a gap in literature on using NNs for swarm classification and demonstrate the effectiveness of NN TSC in rapidly deducing intelligence about attacking swarms to inform counter-maneuvers. Through simulated swarm-versus-swarm engagements, we evaluate NN TSC performance in terms of observation window requirements, noise robustness, and scalability to swarm size. Key findings show NNs can predict swarm behaviors with 97% accuracy using short observation windows of 20 time steps, while also demonstrating graceful degradation down to 80% accuracy under 50% noise, as well as excellent scalability to swarm sizes from 10 to 100 agents. These capabilities are promising for real-time decision-making support in defense scenarios by rapidly inferring insights about swarm behavior.

Problem

• Classify swarm characteristics using neural networks
• Predict swarm tactics for military applications
• Evaluate neural network performance in swarm engagements

Innovation

• Neural networks classify swarm behaviors
• Short observation windows enable rapid predictions
• Robust performance under diverse noise conditions