DLVS3: Synthetic Training Data Generation & Custom GNC Neural Networks for Satellite Missions

DLVS3 (Deep Learning Visual Space Simulation System) solves one of the most persistent challenges in spacecraft autonomy development: the scarcity of annotated training data for mission-critical operations. Our service combines physics-based simulation with custom neural network development to deliver complete AI solutions for spacecraft Guidance, Navigation, and Control applications.

The Training Data Challenge

Developing AI systems for spacecraft operations has historically confronted a fundamental obstacle: obtaining sufficient training data. Approach maneuvers, docking procedures, formation flying, and proximity operations occur infrequently, under particular conditions, with limited opportunities to capture diverse scenarios. Traditional flight data collection cannot provide the millions of annotated samples required to train robust neural networks, while the consequences of AI failures in space operations are unacceptable.

Our Comprehensive Solution

DLVS3 addresses this challenge by generating realistic synthetic data grounded in accurate physics and mission-specific parameters. Satellite manufacturers provide us with spacecraft CAD models and mission profiles, including orbital parameters, target objects, and operational scenarios. We integrate this information with NASA JPL's SPICE system, ensuring astronomical precision in celestial mechanics, planetary positions, and time-dependent phenomena.

The simulation environment generates millions of annotated training samples encompassing diverse operational conditions: varying lighting angles from solar illumination, different phases of target bodies, spacecraft attitude variations, sensor noise characteristics, and the full range of geometric configurations relevant to your mission. Each generated image includes precise ground truth data for position, attitude, relative motion, and any other parameters critical to your GNC algorithms.

Beyond Data: Complete GNC Neural Network Development

DLVS3 represents more than a data generation tool—we deliver complete neural network solutions for spacecraft autonomy. Our service includes developing custom architectures optimized for your specific mission requirements, from relative navigation during rendezvous to visual servoing for robotic arm operations. We train these networks using the synthetic datasets we generate, validate performance across operational envelopes, and prepare systems for deployment on your spacecraft computing platforms.

Our neural networks are designed for the realities of space operations: computational efficiency suitable for space-qualified processors, robustness to lighting variations and sensor degradation, graceful handling of off-nominal conditions, and verifiable performance characteristics required for mission assurance. We work within your mission's constraints regarding computing resources, power budgets, and latency requirements.

Technical Architecture

The DLVS3 system models complete optical chains from scene generation through sensor simulation. We accurately represent camera characteristics, including lens distortions, detector noise, pixel response variations, and optical filters. Solar illumination is computed using precise ephemeris data, while spacecraft and target body materials reflect light according to validated bidirectional reflectance distribution functions. This physics-based approach ensures realistic training data, enabling neural networks to perform reliably with actual flight imagery.

Our simulation environment maintains connections with external systems through robust APIs, enabling integration with your mission planning tools, spacecraft simulators, and verification frameworks. We can generate data synchronized with your development timeline, supporting iterative refinement as mission requirements evolve or spacecraft designs are updated.

Real-Time Testing Capability

Beyond training data generation, DLVS3 provides real-time simulation for testing deployed neural networks. This capability enables hardware-in-the-loop validation where your GNC computer runs actual flight software while receiving synthetic sensor inputs from our simulation. This testing approach validates system performance across scenarios that are too dangerous or expensive to replicate in physical testing, while providing complete ground truth for quantitative performance assessment.

Applications Across Mission Types

Our technology serves diverse spacecraft autonomy needs. Satellite constellation operators can develop formation-flying algorithms without the need for expensive on-orbit demonstrations. Lunar and planetary missions can train landing systems across diverse terrain without waiting for actual approach opportunities. On-orbit servicing missions can rehearse proximity operations with target spacecraft before launch. Space debris removal systems can practice capture techniques across different object types and tumbling rates.