Navigation Board

📌 Overview

The Navigation Board is a custom PCB designed as a dedicated navigation and state estimation platform. It integrates GNSS positioning, inertial sensing, and wireless communication onto a single, compact board — enabling real-time vehicle attitude estimation via a Kalman filter and live data streaming to a host computer for visualization.

The hardware is complete and functional. Active development is now focused on firmware (EKF implementation) and the host-side Python visualization pipeline.

🖼️ Board Preview

Isometric Top	Isometric Bottom

⚡ Specifications

🏗️ System Architecture

┌─────────────┐     ┌─────────────────────────┐     ┌──────────────────┐     ┌──────────────────┐
│  GNSS       │     │                         │     │                  │     │                  │
│  NEO-M9N    │────▶│     STM32F4CEU6         │────▶│  nRF24L01        │────▶│  Host PC         │
│  (UART)     │     │   Kalman Filter /        │     │  2.4 GHz RF Link │     │  Python Viz      │
├─────────────┤     │   Sensor Fusion          │     │                  │     │                  │
│  IMU 9DOF   │     │                         │     │                  │     │                  │
│  (SPI/I2C)  │────▶│                         │     │                  │     │                  │
└─────────────┘     └─────────────────────────┘     └──────────────────┘     └──────────────────┘

Goal: Fuse absolute positioning (GNSS) with high-rate inertial data (IMU) using an Extended Kalman Filter to estimate real-time orientation and motion, then stream state estimates wirelessly to a host computer for visualization.

🔧 Design Details

Hardware Integration

The board centers around the STM32F4 MCU, which interfaces with:

• NEO-M9N GNSS receiver over UART for global positioning
• 9DOF IMU over SPI/I2C for high-rate motion sensing
• nRF24L01 providing a low-latency 2.4 GHz RF link to stream processed state estimates to an external computer

Power System

Powered by a single-cell LiPo battery with onboard USB-C charging circuitry. Independent regulation stages supply stable rails for:

• RF front-end
• Digital logic
• Analog sensing paths

This architecture minimizes noise coupling into sensitive IMU measurements.

PCB Stackup

A 6-layer stackup was chosen to provide:

• Controlled impedance for RF and GNSS antenna traces
• Solid ground referencing across all layers
• Clean power distribution

Dedicated power and ground planes minimize EMI and improve signal integrity throughout.

📊 PCB Stackup Details

The full layer stackup with impedance targets and material specifications is documented in the design spreadsheet:

📄 View Stackup Spreadsheet (Google Sheets)

🗺️ Roadmap

The hardware platform is complete. Next steps are focused on firmware and software:

• Implement Extended Kalman Filter (EKF) for GNSS + IMU fusion on STM32F4
• Tune process and measurement noise covariance matrices
• Stream state estimates over nRF24L01 RF link
• Develop Python visualization for real-time attitude display
• Validate filter output against ground truth measurements

🛠️ Tech Stack

👤 Author

Luis Martinez