Payload Telemetry System

Rocket payload telemetry system developed for the LASC 2025 NASHIRA mission (Latin American Space Challenge).

Demo

Rocket Attitude Visualization & System Testing

Rocket Attitude	System Testing

IMU Data Acquisition

Raw and filtered IMU readings with real-time visualization.

Features

• Extended Kalman Filter (EKF) for 3D position estimation fusing GPS, barometer, and IMU data
• Flight State Machine with automatic phase detection:
  • IDLE → ARMED → BOOST → COAST → APOGEE → DROGUE_DESCENT → MAIN_DESCENT → LANDED
• Quaternion-based attitude estimation with multiple filter options:
  • Complementary filter
  • Kalman filter
  • Madgwick filter
• Adaptive sensor fusion that adjusts filtering based on flight phase
• SD card data logging with FatFS (CSV format with full telemetry)
• LoRa telemetry transmission via E22-900T22D module
• Multi-constellation GNSS support with flight mode configuration

Hardware

Component	Model	Interface
MCU	STM32G431CBT6	-
IMU	ICM-20948	SPI
Barometer	ICP-10100	I2C
GNSS	Multi-constellation	UART
Radio	E22-900T22D (LoRa)	UART
Storage	SD Card	SPI (FatFS)
Ground Station	ESP32	-

Repository Structure

├── Payload_LASC2025_STM32G431CBT6/   # Main STM32 firmware
│   ├── Core/Inc/                      # Header files
│   │   ├── ROCKET.h                   # System management & EKF
│   │   ├── ROCKET_FSM.h               # Flight state machine
│   │   ├── IMU_FUSION.h               # Sensor fusion algorithms
│   │   ├── IMU.h                      # ICM-20948 driver
│   │   ├── BAR.h                      # ICP-10100 barometer driver
│   │   ├── GNSS.h                     # GNSS module driver
│   │   ├── LORA.h                     # E22-900T22D LoRa driver
│   │   └── SD.h                       # SD card interface
│   └── Core/Src/                      # Source files
├── Base_Receptor/                     # ESP32 ground station receiver
│   └── Base_Receptor.ino              # Arduino sketch for telemetry reception
├── Matlab Simulations/                # Analysis & visualization tools
│   ├── NASHIRA_MISSION_V6.m           # Flight data processing
│   ├── IMUDataViewer_Attitude.m       # Real-time attitude visualization
│   ├── IMUDataViewer_Orientation_and_Position.m
│   ├── mag_cal.m                      # Magnetometer calibration
│   └── ...
└── media/                             # Demo GIFs

Sensor Fusion & Algorithms

Extended Kalman Filter (EKF)

The EKF fuses data from multiple sensors for accurate 3D position and velocity estimation:

• State vector: [x, y, z, vx, vy, vz] in NED frame
• Prediction: Uses IMU accelerations (gravity-compensated)
• Update: GPS position and barometer altitude measurements
• Automatic symmetry enforcement to prevent numerical divergence

Attitude Estimation

Multiple filter implementations for robust attitude estimation:

• Complementary Filter: Fast, low computational cost
• Kalman Filter: Per-axis filtering with bias estimation
• Madgwick Filter: Quaternion-based AHRS with magnetometer fusion

Adaptive Filtering

The system automatically adjusts sensor fusion parameters based on flight phase:

• High-G detection during boost phase
• Gyro-only integration during motor burn
• Recovery algorithms post-boost for attitude reconvergence

Flight State Machine

The FSM automatically detects flight phases using sensor data:

State	Detection Criteria
IDLE	System powered, waiting
ARMED	RBF removed
BOOST	Acceleration > 2g threshold
COAST	Motor burnout detected (acc drop)
APOGEE	Velocity < threshold, altitude drop
DROGUE_DESCENT	Post-apogee descent
MAIN_DESCENT	Main parachute altitude
LANDED	Low velocity + acceleration for 3s

MATLAB Analysis Tools

Script	Description
NASHIRA_MISSION_V6.m	Complete flight data processing and analysis
IMUDataViewer_Attitude.m	Real-time 3D attitude visualization
IMUDataViewer_Orientation_and_Position.m	Combined orientation and position display
mag_cal.m / GetMagCal.m	Magnetometer calibration (hard/soft iron)
KalmanPedro.m	Kalman filter implementation reference

Building

STM32 Firmware

1. Open Payload_LASC2025_STM32G431CBT6/ in STM32CubeIDE
2. Build the project
3. Flash to STM32G431CBT6 via ST-Link

Ground Station

1. Open Base_Receptor/Base_Receptor.ino in Arduino IDE
2. Select ESP32 board
3. Upload to ESP32

Mission Context

• Event: LASC 2025 (Latin American Space Challenge)
• Rocket: NASHIRA
• Payload: Telemetry and data acquisition system

License

See LICENSE file.