QGRAD vs. Kalman Filter
“Like most, we started out using a Kalman filter, but we wanted a faster update rate. The Kalman filter was consuming a large amount of our processor cycle, and we thought we could find a way to do it more efficiently. We also wanted to get our filter running in a smaller codespace without sacrificing accuracy.” Paul Yost, Yost Labs
| Kalman Filter | QGRAD2™ Filter | |
|---|---|---|
| Filter Duration | 2.0 ms | 0.2 ms |
| Relative Duration | 1x | 10x |
| Update Interval | 10.0 ms | 3.2 ms |
| Relative Update Speed | 1x | 3x |
| Update Rate | 100 Hz | 312 Hz |
Background
Since the 1960’s, computer navigation systems have used sensor fusion algorithms to overcome the inherently flawed nature of single-component sensor systems. Sensor fusion algorithms combine several inputs to improve the accuracy of output results. In the case of IMU/AHRS applications, gyroscope, magnetometer, and accelerometer inputs are fused to produce accurate position, orientation, and velocity data.
A New Solution
QGRAD2™ takes sensor fusion performance to the next level:
- QGRAD2™ accomplishes precise sensor fusion with a smaller code footprint and fewer instructions per update.
- Fewer computations = improved efficiency. QGRAD2™ is 10x more efficient than existing Kalman filter algorithms.
- The efficiency of QGRAD2™ can be applied to reduce update rate by over 300%, or can be tuned to reduce heat and improve battery life in wearable applications.
- QGRAD2™ has an available dynamic smoothing process which self-tunes sensors to improve responsiveness and precision under highly dynamic conditions, while still providing stability in static conditions.
How to Get QGRAD2™
Embedded Sensors
Install our high-performance, ultra-low-profile, QGRAD2™-powered sensors in your product.
Packaged Sensors
A range of specialized and customizable bodies make our sensors the simplest, most versatile way to experience QGRAD2™ performance.