Swarm Characteristics Classification Using Neural Networks (2403.19572v2)
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-vs-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.
- Z. Kallenborn, Are drone swarms weapons of mass destruction? The counterproliferation papers. Future warfare series ; no. 60, Maxwell Air Force Base, Alabama: U.S. Air Force Center for Strategic Deterrence Studies, Air University, 2020.
- J. Sami, B. Silvère, V.-F. Santiago, P. Marion, and A. Jesus, “Real-time Classification of Aircrafts Manoeuvers,” Journal of Signal Processing Systems, vol. 95, pp. 425–434, Apr. 2023.
- C. Gingrass, D. I. Singham, and M. P. Atkinson, “Shape Analysis of Flight Trajectories Using Neural Networks,” Journal of Aerospace Information Systems, vol. 18, pp. 762–773, Nov. 2021.
- A. Teatini, “Movement Trajectory Classification Using Supervised Machine Learning,” Master’s thesis, KTH Royal Institute of Technology, Stockholm, Sweden, 2019.
- N. M. Foumani, L. Miller, C. W. Tan, G. I. Webb, G. Forestier, and M. Salehi, “Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey,” Feb. 2023. arXiv:2302.02515 [cs].
- A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention Is All You Need,” Dec. 2017. arXiv:1706.03762 [cs].
- S. Hauri and S. Vucetic, “Group Activity Recognition in Basketball Tracking Data – Neural Embeddings in Team Sports (NETS),” Aug. 2022. arXiv:2209.00451 [cs].
- H. I. Fawaz, G. Forestier, J. Weber, L. Idoumghar, and P.-A. Muller, “Deep learning for time series classification: a review,” Data Mining and Knowledge Discovery, vol. 33, pp. 917–963, July 2019. arXiv:1809.04356 [cs, stat].
- A. Bagnall, A. Bostrom, J. Large, and J. Lines, “The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms. Extended Version,” Feb. 2016. arXiv:1602.01711 [cs].
- Z. Wang, W. Yan, and T. Oates, “Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline,” Dec. 2016. arXiv:1611.06455 [cs, stat].
- T. Allam Jr. and J. D. McEwen, “Paying Attention to Astronomical Transients: Introducing the Time-series Transformer for Photometric Classification,” Nov. 2022. arXiv:2105.06178 [astro-ph].
- G. Zerveas, S. Jayaraman, D. Patel, A. Bhamidipaty, and C. Eickhoff, “A Transformer-based Framework for Multivariate Time Series Representation Learning,” Dec. 2020. arXiv:2010.02803 [cs].
- Y. Jin, L. Hou, and Y. Chen, “A Time Series Transformer based method for the rotating machinery fault diagnosis,” Neurocomputing, vol. 494, pp. 379–395, July 2022.
- G. M. Siouris, Missile Guidance and Control Systems. Springer-Verlag New York, Inc, 2004.
- PhD thesis, Naval Postgraduate School, Monterey, CA, 2013.
- R. Buettner, “Field Experimentation After Action Report,” tech. rep., Naval Postgraduate School, McMillan Airfield, Camp Roberts, California, Feb. 2017.
- J. J. Dawkins, F. L. Crabbe, and D. Evangelista, “Deployment and Flight Operations of a Large Scale UAS Combat Swarm: Results from DARPA Service Academies Swarm Challenge,” in 2018 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1271–1278, June 2018. ISSN: 2575-7296.
- O’Reilly Media, Inc., second ed., 2019.
- I. Goodfellow, Deep Learning. The MIT Press, Nov. 2016.
- S. Ruder, “An overview of gradient descent optimization algorithms,” 2017. arXiv:1609.04747.
- D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, pp. 533–536, Oct. 1986. Number: 6088 Publisher: Nature Publishing Group.
- N. C. Redder, “Trade-Off Analysis of Large-Scale Swarm Engagements,” Master’s thesis, Naval Postgraduate School, Monterey, CA, Dec. 2022.
- L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar, “Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization,” June 2018. arXiv:1603.06560 [cs, stat].