Why Sensor Fusion?
No single sensor is perfect. Each has strengths and weaknesses — fusion combines them to get the best estimate. Think of it like a team of experts: the accelerometer is great at detecting gravity but noisy during motion, the gyroscope is clean during rotation but drifts over time, and the magnetometer provides heading but gets confused near metal.
Complementary Filter
The simplest fusion approach. It trusts the gyroscope for fast changes (high-pass) and the accelerometer for slow/steady changes (low-pass).
Madgwick Filter
The Madgwick filter is the most popular choice for drone AHRS. It uses gradient descent to minimize the error between predicted and measured orientations.
Kalman Filter: Full Mathematical Framework
The Kalman filter maintains a state estimate and its uncertainty, optimally combining predictions with measurements.
Hands-On Project: 6-State EKF Implementation
Filter Comparison
| Filter | CPU | Accuracy | Tuning | Best For |
|---|---|---|---|---|
| Complementary | Very Low | Good | Simple (α) | Basic drones |
| Madgwick | Low | Very Good | Moderate (β) | Most applications |
| Mahony | Low | Good | Moderate (Kp, Ki) | Outdoor flight |
| EKF | Medium | Excellent | Complex | Professional systems |
Key Takeaways
- Sensor fusion combines multiple sensors for better estimates than any single sensor
- Complementary filter: simplest, good for basic applications
- Madgwick filter: best balance of performance and simplicity for most drones
- Kalman/EKF: most accurate but complex to tune and implement
- Always calibrate sensors before fusion — garbage in, garbage out
- Gyro bias estimation is critical for long-term accuracy