Invaya Systems
Featured Projects
Physics-Informed Neural Networks for Satellite Simulation
Accelerated satellite orbital simulation using Physics-Informed Neural Networks (PINN) and Physics-Informed Recurrent Neural Networks (PI-RNN), eliminating dependency on expensive synthetic data generation while ensuring physical consistency.
Challenges
- Generating large volumes of synthetic orbital data using standard ODE solvers (RK4) was computationally expensive and time-intensive
- Models trained only on synthetic data failed to adapt to real satellite telemetry due to unmodeled effects (solar radiation pressure, atmospheric drag, magnetic perturbations)
- This created a modeling gap where trained models performed well on simulation but drifted when applied to real-world conditions
Solutions
- Implemented Physics-Informed Neural Network (PINN) to embed physical laws directly into the learning process
- Trained PINN simultaneously on real telemetry and physics-based loss, eliminating need for large synthetic datasets
- Used dummy collocation points to enforce physics consistency without costly data generation
- Developed Physics-Informed Recurrent Neural Network (PI-RNN) for orbital propagation, capturing temporal dynamics and handling maneuvers as control inputs
Outcomes
- Synthetic data generation became feasible on standard laptop GPU (NVIDIA RTX) - no need for high-end compute clusters
- Successfully merged real-world telemetry with physical constraints, leading to more stable and physically consistent synthetic data
- Maintained physical validity while reducing computational costs
- Enabled faster iteration cycles for model development and testing
Satellite Recapture & Tracking (JPDA/MHT)
Developed algorithms to rapidly estimate new satellite orbits after unobserved maneuvers, reducing search space by 99.99% through mission priors and probabilistic constraints. Implementing JPDA/MHT for robust multi-target tracking.
Challenges
- During unobserved periods, enemy satellites may perform maneuvers, altering their orbits
- Modeling all possible maneuvers led to enormous search space, making it expensive to locate satellite using sensors
- Multiple unidentified objects appearing in radar/telescope scans required complex data association to link each detection to correct previous state
Solutions
- Applied mission priors and probabilistic constraints to reduce 99th percentile (p99) search space for maneuver possibilities
- Reduced search volume from 10,782,248 km³ (unconstrained) to 1,418 km³ (directional prior) - 99.99% reduction
- Implementing Joint Probabilistic Data Association (JPDA) to assign probabilities to each possible measurement match
- Developing Multiple Hypothesis Tracking (MHT) running several UKFs in parallel for different association sequences
Outcomes
- 99.99% reduction in search volume through progressive constraint application
- Fuel Budget Prior reduced search by ~100% from unconstrained baseline
- Individual ΔV Prior provided additional 48.4% reduction
- Directional Prior achieved further 25.8% reduction to final 1,418 km³
- Enabled rapid satellite recapture critical for tracking and mission planning
Control Systems & State Estimation
Advanced control algorithms and state estimation techniques for autonomous systems, including Kalman filtering, model predictive control, and adaptive control strategies for dynamic environments.
Challenges
- Handling uncertainty and disturbances in real-world autonomous operations
- Achieving optimal performance while guaranteeing safety constraints
- Adapting control strategies to varying operational scenarios and environmental conditions
Solutions
- Implemented Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) for nonlinear state estimation
- Developed model predictive control (MPC) with real-time constraint handling
- Created adaptive control algorithms that learn and adjust to changing system dynamics
- Built hierarchical decision-making architecture for mission planning and execution
Outcomes
- Robust state estimation even with noisy, uncertain measurements
- Smooth, precise autonomous operation in challenging conditions
- 100% safety compliance with zero constraint violations
- Successful deployment in GPS-denied and dynamic environments