Predicting Unit Behavior in Tactical Scenarios Using Deep Learning
| Title | Predicting Unit Behavior in Tactical Scenarios Using Deep Learning |
|---|---|
| Summary | Develop and evaluate deep learning models capable of predicting future unit behavior and movement trajectories in tactical scenarios, using a combination of historical trajectory data, environmental context, and mission objectives. |
| Keywords | |
| TimeFrame | |
| References | 1. Fernandes, R., Hieb, M. R., & Costa, P. C. “Levels of Autonomy: Command and Control of Hybrid Forces”, 21st ICCRTS, 2016.
2. Dunin-Keplicz, B., & Verbrugge, R. “Teamwork in Multi-agent Systems: A formal approach”, John Wiley & Sons, 2011. 3. Alberts, D. S. “The Agility Advantage: A Survival Guide for Complex Enterprises and Endeavors”, CCRP Publication Series, 2011. |
| Prerequisites | |
| Author | |
| Supervisor | EDISON PIGNATON DE FREITAS |
| Level | Master |
| Status | Open |
Problem Statement
In tactical military operations, situational awareness and timely decision-making are critical for mission success. Commanders often rely on historical movement patterns, environmental conditions, and mission objectives to anticipate the future actions of friendly and adversarial units. However, manual or rule-based predictive models fail to capture the complexity and non-linear dynamics of real-world tactical behavior.
Recent advances in deep learning — particularly Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Transformers — have shown strong performance in sequence modeling, trajectory prediction, and spatiotemporal reasoning. Applying these techniques to predict the behavior and movement of units could enhance proactive decision-making in dynamic environments, improve coordination in coalition networks, and anticipate adversary maneuvers.
Project Goal Develop and evaluate deep learning models capable of predicting future unit behavior and movement trajectories in tactical scenarios, using a combination of historical trajectory data, environmental context, and mission objectives.
Proposed Solution & Specific Tasks 1. Literature Review - Survey existing work on trajectory prediction, spatiotemporal deep learning, and military/tactical movement modeling. - Identify key datasets, modeling techniques, and challenges (e.g., uncertainty, adversarial behaviors, incomplete data).
2. Dataset Preparation - Collect or simulate trajectory datasets relevant to tactical scenarios: - Public datasets (e.g., CAIDA military mobility traces, CRAWDAD mobility datasets, OpenSky flight data). - Synthetic datasets generated using NS-3, OMNeT++ or SUMO (for vehicle/unit movement). - Include contextual information: terrain features, mission objectives, coalition/adversary interactions.
3. Model Development - Implement baseline trajectory prediction models (e.g., RNN, LSTM). - Explore transformer-based models (e.g., Temporal Fusion Transformers, Trajectory Transformers). - Integrate environmental context embeddings (terrain, weather, obstacles) and mission constraints into the model.
4. Scenario Simulation - Develop tactical scenarios (e.g., convoy movements, UAV swarms, adversarial pursuit/avoidance). - Train models on historical/simulated data, then predict future positions and actions.
5. Evaluation & Analysis - Compare performance of RNN, LSTM, and transformer-based approaches. - Analyze predictive accuracy under different tactical conditions (dense vs. sparse units, contested vs. uncontested environments). - Investigate uncertainty estimation (e.g., Monte Carlo dropout, Bayesian DL).
Evaluation Criteria: - Prediction Accuracy:
Mean Squared Error (MSE), Average Displacement Error (ADE), Final Displacement Error (FDE). Accuracy of predicting mission-relevant behaviors (e.g., retreat, advance, encirclement).
- Robustness:
Model performance with noisy or incomplete trajectory data. Ability to generalize across different terrains/missions.
- Operational Relevance:
Improvement in situational awareness (quantified as lead-time advantage in decision-making). Computational efficiency for deployment in constrained tactical systems.
Tools & Frameworks: - Deep Learning Libraries: PyTorch, TensorFlow/Keras. - Simulation Tools (for synthetic data): NS-3, OMNeT++, SUMO (for vehicle/convoy scenarios), UAVSim.
Datasets: - CRAWDAD (military mobility traces, wireless connectivity). - OpenSky Network (UAV/aircraft movements). - Synthetic tactical datasets generated via simulators.
Visualization: Matplotlib, Seaborn, Plotly (trajectory plots, heatmaps).
