In embedded robotics, drones, and advanced industrial monitoring, relying on a single sensor to determine the state of a system is inherently risky. Every physical sensor has structural flaws:
- Accelerometers are excellent at measuring gravity to determine immediate tilt but are notoriously susceptible to high-frequency vibrational noise (e.g., from a spinning motor).
- Gyroscopes are immune to linear vibration and provide excellent short-term rotational data, but they suffer from “drift” over time—a stationary gyroscope will slowly report that it is rotating.
- Magnetometers (Compasses) provide absolute heading relative to the Earth’s magnetic north but are easily confused by nearby ferrous metals or electromagnetic interference from power lines.
Sensor Fusion is the mathematical discipline of taking these disparate, flawed data streams and combining them to produce a stable, highly accurate result (often the orientation or position of the device).
How Sensor Fusion Works
Sensor fusion is not simply averaging values together. It requires advanced mathematical modeling, most commonly implemented via a Kalman Filter or a Mahony/Madgwick Filter.
These algorithms run continuously in a feedback loop:
- Predict: The algorithm uses the immediate, fast data from the gyroscope to predict the device’s new orientation.
- Update (Correct): The algorithm uses the slower but drift-free absolute data from the accelerometer (gravity vector) and magnetometer (magnetic north) to correct the gyroscope’s prediction, mathematically shearing off the accumulated drift.
By optimally blending these inputs based on their known statistical noise profiles, the resulting output (often expressed as a Quaternion) is both highly responsive to rapid motion and completely stable over long periods.
Sensor Fusion in Modern Hardware
Historically, sensor fusion algorithms required complex floating-point mathematics running on a powerful central processor. Today, IMUs (Inertial Measurement Units) are available as “smart sensors.”
Chips like the Bosch BNO085 or InvenSense ICM-20948 integrate the MEMS accelerometer, gyroscope, and magnetometer onto the same silicon die alongside a dedicated low-power Cortex-M0+ core. The internal core runs the proprietary sensor fusion algorithms and outputs ready-to-use Quaternions or Euler angles directly over I2C/SPI. This dramatically offloads the host MCU, saving critical battery life and CPU cycles for higher-level application logic.
The Inovasense Approach to Mixed-Signal Fusion
While IMU orientation tracking is the most classic example of sensor fusion, Inovasense applies the same mathematical principles to predictive maintenance and Edge AI.
For instance, in detecting the imminent failure of a heavy industrial bearing, a single vibration sensor might trigger false positives due to normal operational shocks. By utilizing Sensor Fusion, we stream the high-frequency vibration data (accelerometer), surface temperature (IR sensor), and the motor’s current draw (current transformer) into a localized TinyML model running on an NPU.
The fusion algorithm recognizes the correlation between these distinct phenomenons: a spike in vibration perfectly synchronized with a gradual rise in temperature and a 5% increase in electrical load provides an unshakeable, 99.9% confidence interval that the bearing is entering failure, eliminating expensive false alarms on the factory floor.