ZYRANAV
LEO-PNT
SYSTEM
A resilient navigation engine that derives position using open LEO signals, Doppler tracking, IMU prediction, and fusion algorithms—built for GNSS-denied environments.
Product
Purpose
ZyraNav ensures reliable navigation when GNSS is unavailable by processing Doppler shifts from open LEO satellite signals combined with advanced estimation methods.
LEO Signal Capture
Receives real LEO transmissions using HackRF, LNA, and custom antennas. Extracts frequency variations with high precision for Doppler analysis.
Doppler Navigation
Converts frequency shift into relative velocity and range-rate, enabling positioning without GNSS reliance.
Orbit Modeling
TLE-based orbit prediction ensures accurate satellite position computation for Doppler comparison.
Fusion Engine
Combines IMU prediction, Doppler measurements, and last known GNSS fix through an Extended Kalman Filter for continuous tracking.
Live UI
Raspberry Pi dashboard visualizes Doppler curves, satellite geometry, position estimates, and navigation confidence metrics.
Prototype Ready
Integrated setup combining SDR, IMU, LEO, power, and compute hardware inside a field-ready enclosure.
Satellite Compatibility
Works with open-access LEO signals useful for Doppler measurement and research-grade navigation experiments.
Resilience
ZyraNav maintains navigation by using LEO Doppler signals as the primary source of position information. When satellite visibility reduces or signals fluctuate, the IMU bridges the gap through short-term prediction, ensuring the navigation solution remains stable and continuous even in GNSS-denied conditions.
System Architecture
Simulation Layer
Generates synthetic Doppler signals, noise models, and motion scenarios for validating algorithms.
Signal Front-End
HackRF + LNA + custom antennas capture real LEO signals with sufficient SNR for Doppler extraction.
Navigation Engine
Blends Doppler measurements, IMU prediction, and orbital models through EKF to estimate position and velocity.
Output Layer
Raspberry Pi interface displays real-time position, velocity, Doppler curves, and system diagnostics.
System Diagrams
Product Design
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Whole Workflow
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GNSS & LEO Errors & Solution
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GNSS Principle
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TLE Workflow
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Receiver Architecture
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Algorithms
- Doppler Extraction: FFT + PLL-based frequency tracking to obtain stable Doppler measurements.
- Orbit Propagation: Real-time satellite position estimation using SGP4 and TLE data.
- Error Filtering: Outlier rejection, smoothing, and noise shaping for clean Doppler curves.
- Sensor Fusion: EKF blending IMU, Doppler, and GNSS fallback for continuous navigation.
Hardware Stack
Compute
Raspberry Pi 5
SDR
HackRF One + LNA + Filtering
Sensors
IMU (MPU6050/6500) + GNSS (NEO-M8N)
Antenna
L-band Helical / Patch + UHF antennas
Power
Battery + USB-C Supply
Prototypes
Integrated Unit
A compact field device containing SDR, IMU, GNSS, power modules, and compute hardware for real-time tests.
Simulation Suite
Python-based Doppler simulation toolkit to validate algorithms before field trials.
Signal Capture Rig
Portable SDR setup for collecting real LEO transmission recordings for analysis and model tuning.
Results & Future Work
Current Results
Successfully extracted Doppler shifts, achieved initial position estimation, and demonstrated IMU-supported continuity during signal gaps.
Future Work
Enhancing antenna gain, improving Doppler SNR, refining fusion algorithms, and executing broader outdoor test campaigns.
Team
Sangsai S
Team Lead
Ravichandran P
Algorithm&Software Engineer
Abinithi S
Hardware & SDR Integration Engineer
Ragini R
UI/UX Designer
Keerthika A
Researcher
Praveen R
Field Operations & Testing Support
Mentors
Prof.Baskar R
Techinical Architect
