By Mike Stanley
Originally posted on Freescale’s The Embedded Beat Blog
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 Freescale’s sensors 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.
- Optimal State Estimation – Kalman, H-infinit and Nonlinear Approaches, Dan Simon, John Wiley & Sons, 2006
- 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
- Inertial Navigation Systems Design, Kenneth R. Britting, Artech House, 2010 (a classic text, this was first published in 1971)
- Strapdown Inertial Navigation Technology, 2nd Edition, D.H. Titterton and J.L. Weston, The Institute of Electrical Engineers, 2004
- Quaternions and Rotation Sequences, by Jack B. Kuipers, Princeton University Press, 1999
Original post: http://blogs.freescale.com/2011/08/03/what-is-sensor-fusion/