What is sensor fusion?

When a colleague of mine recently asked me “What is sensor fusion?” I had to stop and think. Like Justice Potter Stewart once said, “I know it when I see it.” But as an engineer dealing with this topic every day, I should be able to do better. Eventually I came up with the following:

Sensor fusion encompasses a variety of techniques which can:

  • Trade off strengths and weaknesses of the various sensors to compute something more than can be calculated using the individual components;
  • Improve the quality and noise level of computed results by taking advantage of:
    • Known data redundancies between sensors
    • Knowledge of system transfer functions, dynamics and/or kinematics

Good lord! Sounds like something out of one of my textbooks. It’s more fun to look at it by example.

Accelerometers return a measured quantity which includes inertial acceleration as well as gravity. When not moving, they make a great tilt meter. But they can’t detect rotation about the gravity vector. Magnetometers have a similar problem detecting rotation about the earth’s magnetic field. But combine the two, and you have a case where each complements the other to achieve something that neither can do alone.

MEMS gyros are used to measure angular rotation, and can typically respond to changes in rotation quickly. They also often have considerable offset and drift over time. Magnetometers provide a way to place bounds on those offset and drift terms. And conversely, the gyro data is useful as a second check against magnetic interference.

You can see techniques like these in use in the great variety of iPhone and Android sensor applications which can be downloaded to your phone today. And sometimes, you can see cases where the developer SHOULD have used techniques like these!

One of the sensor fusion applications I love to show people is the “3D Compass” application that I’ve downloaded to my Xoom from the Android Market. This augmented reality application fuses magnetometer, accelerometer and GPS information to show you not only where you are, but what your current perspective is. The application screen provides a current camera view, overlay-ed with a virtual compass and map oriented the same way you are facing and slowing your current location. Sweet!

Augmented Reality Utilizing Sensor Fusion

I hope we see more creative applications like this brought to market in coming months.

In the next couple of years, we will see applications built on algorithms that model the behavior of the system under study, including statistical noise behavior of the sensors included in the system. By comparing measured quantities with predicted ones, it is often possible to tease signals out of what would otherwise look like nothing more than noise.

Low cost MEMS and solid state sensors are enabling consumer products and applications that were cost prohibitive a few years ago. We are fortunate in that most of the sensor fusion problems we are dealing with at the micro level were addressed at the macro level by NASA and the aeronautics business 30 or more years ago. Since joining the sensor design team, I’ve had to brush up on my math and control theory and invest in a number of good textbooks. If you share my passion for the topic, you could do worse than to obtain some of those listed in the references below. And if you have a better definition for sensor fusion, please share by responding to this posting.


  1. Optimal State Estimation – Kalman, H-infinit and Nonlinear Approaches, Dan Simon, John Wiley & Sons, 2006
  2. Introduction to Random Signals and Applied Kalman Filtering with Matlab Exercises and Solutions, 3rd edition, Robert Grover Brown and Patrick Y.C. Hwang, John Wiley & Sons, 1997
  3. Inertial Navigation Systems Design, Kenneth R. Britting, Artech House, 2010 (a classic text, this was first published in 1971)
  4. Strapdown Inertial Navigation Technology, 2nd Edition, D.H. Titterton and J.L. Weston, The Institute of Electrical Engineers, 2004
  5. Quaternions and Rotation Sequences, by Jack B. Kuipers, Princeton University Press, 1999
Michael Stanley
Michael Stanley
Mike Stanley develops advanced algorithms and applications for MCUs and sensors, including sensor fusion and sensor data analytics. He is a founding member of the MEMS Industry Group’s Accelerated Innovation Community and a contributor to the IEEE Standard for Sensor Performance Parameter Definitions (IEEE 2700-2014). He is co-author of a chapter on intelligent sensors in “Measurement, Instrumentation, and Sensors Handbook” (volume two), and speaks on sensor topics. When the Arizona temperature drops below 100 degrees, you'll find Mike flying his F450 quadcopter . Follow him @SensorFusion.


  1. […] posted on Freescale’s The Embedded Beat […]

  2. Avatar John Smith says:

    It’s all about power consumption ….
    Sensors are used (at least for now) mostly in power-sanative devices, e.g. smartphones, tablets and considering each sensor generates a lots interrupts and traffic over the i2c bus (which additionally adds to the pins count)
    Currently sensors are being when the application processor is enabled and the LCD, which is the major power “drainer” is working — Gaming, AR — but there are future use cases in development in which the sensors will be required to be active while the application processor is power-down, e.g. alerting when certain speed is reached (when GNSS is not available), the device falls of the table, the device is been moved (from your table).

  3. Avatar kasiraj says:

    It is a really good topic. Can any one get me some more detail information and its applications.

  4. […] at least ten sensors. Beyond sensor performance, a challenge facing the industry is sensor fusion (Freescale sensor engineer Mike Stanley recently wrote about it). The difficulties of effectively aggregating data from different sensors will become as or more […]

  5. […] sensing platform, which overcomes the complexity and improves the efficiency of sensor fusion. (See “What is sensor fusion?” by Mike Stanley for a definition of this […]

  6. […] sensing platform, which overcomes the complexity and improves the efficiency of sensor fusion. (See “What is sensor fusion?” by Mike Stanley for a definition of this […]

Buy now