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


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.


For more information Contact Us or Call (740) 876-4936