Battlefield Target Movement Prediction
Project Overview
This system combines geospatial data processing and machine learning to track and predict the movement of targets in a battlefield environment. It uses terrain-aware deep learning models to forecast target trajectories with confidence intervals, enabling military forces to anticipate enemy movements and optimize tactical decisions in real-time.
Visualization Capabilities
Real-time target trajectory prediction with confidence intervals
Terrain-aware movement prediction accounting for elevation and land features
Multiple target movements over time
Key Features
- Training prediction models on historical movement data
- Evaluating prediction accuracy against ground truth
- Testing models with various prediction durations
- Visualizing both actual movements and predicted trajectories
- Confidence interval estimation for predicted movements
- Terrain-aware predictions that adapt to geographical features
- Real-time animation of battlefield movements
Technical Architecture
Machine Learning Model
The system uses a sophisticated neural network architecture that combines:
- Positional encoding for sequence data processing
- Terrain encoder using convolutional neural networks
- Transformer-based decoder for sequence prediction
- Probabilistic output heads for uncertainty quantification
Geospatial Processing
- Integration with GDAL for geospatial data handling
- Raster and vector data processing for terrain analysis
- Dynamic terrain patch extraction for local context
- Elevation-aware movement prediction
Data Pipeline
- Historical target movement data processing
- Temporal feature extraction for time-aware predictions
- Target classification and tracking
- Batched prediction for multiple targets