LEO-BASED PNT
NAVIGATION
SYSTEM
Resilient positioning using Low Earth Orbit satellites, engineered for contested and GNSS-denied environments.
Product
Project Goal
Develop a navigation system that uses LEO satellite signals (like Starlink/OneWeb) for positioning when GPS is jammed or unavailable.
Why LEO?
Higher signal power, faster geometry change for rapid convergence, and resistance to conventional GNSS jamming.
Resilience
Designed for contested, denied, and degraded environments where traditional GNSS cannot be trusted.
Signal Layer
Captures and processes real LEO downlink signals with Doppler signatures for robust tracking.
Navigation Engine
Doppler-based navigation fused with IMU data delivers continuous state estimates.
Visual Interface
Raspberry Pi dashboard for live position, velocity, and signal health monitoring.
Simulation
Full-stack simulation environment to validate algorithms before deployment.
Scalability
Architecture ready to support multiple LEO constellations and sensor configurations.
Future Proof
Built to evolve with emerging LEO networks and next-generation PNT concepts.
System Architecture
Simulation Layer
Generates synthetic LEO signals with Doppler, noise, and multipath effects using TLE orbits for robust algorithm testing.
Signal Front-End
SDR (HackRF) + LNA + DIY 1615 MHz helical antenna designed to capture real-world LEO satellite signals.
Navigation Engine
Doppler-based navigation algorithms fused with IMU data via Kalman Filter. Enables smooth switching from GNSS to LEO.
Output Layer
GUI running on Raspberry Pi displaying real-time position, velocity, and signal status metrics.
Algorithms
- Doppler Extraction: Precise frequency shift measurement from LEO carrier signals.
- Orbit Propagation: Real-time satellite position calculation using TLE data.
- Measurement Filtering: Signal processing to reduce noise and multipath errors.
- Multi-Sensor Fusion: Extended Kalman Filter (EKF) combining Doppler measurements with IMU data for continuous tracking.
Hardware Stack
Compute
Raspberry Pi 5
SDR
HackRF One + LNA
Sensors
NEO-M8N GNSS, MPU6050/6500 IMU
Antenna
DIY Helical (1615 MHz, 6-8 turns)
Power
USB-C 5V / PoE
Prototypes
Test Device
Integrated Pi + GNSS + IMU unit housed in a custom 3D printed case for field testing.
Simulation Env
Full Python environment for validating navigation algorithms before field deployment.
Signal Capture
SDR-based setup for recording and processing live LEO satellite signals.
Results & Future Work
Current Results
Successfully demonstrated Doppler curve extraction and initial position convergence using synthetic and recorded data. Sensor fusion stabilizes the track during signal gaps.
Future Work
Refining real-time processing efficiency, improving antenna gain, and conducting extensive field trials in urban canyon environments.