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I am designing an unmanned aerial vehicle, which will include several types of sensors:

  • 3-axis accelerometer
  • 3-axis gyroscope
  • 3-axis magnetometer
  • horizon sensor
  • GPS
  • downward facing ultrasound.

A friend of mine told me that I will need to put all of this sensor data through a Kalman filter, but I don't understand why. Why can't I just put this straight into my micro controller. How does the Kalman filter help me about my sensor data?

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You do connect all these sensors directly to a microcontroller. The Kalman filter is not an electronic filter like a LRC filter that goes between the sensors and the microcontroller. The Kalman filter is a mathematical filter implemented as software routine inside the microcontroller.

The sensors you have listed give the microcontroller 14 or 15 raw numbers each time they are all updated.

When I fly a little aircraft, what I really want to know is its position and orientation and how far it is above the ground -- 7 numbers.

I need something that gives me those 7 numbers.

Ideally I want a new estimate of those 7 numbers every time through my control loop. The once-per-second updates I get from my cheap GPS aren't nearly fast enough. (People at What frequency does my quadcopter output-sense-calculate-output update loop need to stay stable? are telling me even 50-times-per-second isn't going to be fast enough).

Somehow I'm going to have to reduce those 14 or 15 raw numbers that I have, some of which only occasionally get updated, into (estimates of) the 7 numbers that I really want.

As Josh pointed out, there are many ad-hoc ways to convert those raw numbers into usable data. Any routine that converts 15 numbers into 7 numbers can be described as a "filter".

You don't have to use the optimum filter. But you will use some kind of filter -- i.e., something that converts from the 15 numbers of raw data you have into (estimates of) the 7 numbers you really want.

The Kalman Filter is, in some conditions, the "optimum" filter, the best way of converting that raw data into the 7 numbers I really want.

It may take less work on your part to use a Kalman filter that someone else has already written and debugged, than to write some other filter from scratch, debug it, and keep adding stuff to it until it is usable -- a filter that will inevitably turn out to be sub-optimum.

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The short, snide answer is "try it without one." The better answer is an example: When your accellerometers say you are 10 degrees from vertical, but your gyro says you haven't rotated away from vertical, and your magnetometers are reporting a 30 deg offset from north but your gyro says 32 degree ... what is the current heading and tilt?

You'll probably come up with a million ad-hoc ways which seem to work in one example, but fail in others. The Kalman Filter (Extended Kalman Filter (EKF) for this task!) will provide for you a rigorous way to answer these questions. The quality of the answers is still being researched--though the EKF's track record is very good--but at least everyone will agree what the answers are.

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    $\begingroup$ Exactly the answer I was looking for. "What would happen if I don't use Kalman Filter". Thanks! $\endgroup$ – Manav Kataria Mar 29 '14 at 0:04
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Sensor data is noisy. If you do not filter it, then your vehicle would at least act erratically if it were even stable enough to fly. Filtering, via a Kalman filter or otherwise, can reduce the noise when done correctly, improving stability in turn.

A Kalman filter is a particularly powerful filter. It takes a model of the system and noise models for both the system and your sensors. It then estimates the state of the vehicle based on a provided state estimate and the controls applied at any moment in time. This estimated state will be more accurate than what the sensors report.

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You could use particle filters as well. For the basic intro to Particle Filters, you could have a look at Professor Thrun's videos in Programming a Robotic Car.

http://www.youtube.com/watch?v=H0G1yslM5rc

http://www.youtube.com/watch?v=QgOUu2sUDzg

Particle filters are more robust and have a far lesser probability of the loop closure error, which commonly occurs while implementing an EKF.

The videos describe the functioning of a particle filter.

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  • $\begingroup$ Generally, answers which contain not more than a link aren't preferred. If you could write a paragraph or two on the gist of the video, that would be nice.. $\endgroup$ – Manishearth Nov 20 '12 at 6:52
  • $\begingroup$ speak for yourself. I see little gain by restating information in another link. The link may contain information I was not aware of and someone does not need to retype it to make me aware of it. I can click and read very easily, thank you. $\endgroup$ – Spiked3 Nov 24 '12 at 6:08
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A Kalman Filter is an algorithm that is commonly used in UAVs to fuse multiple sensor measurements together to provide an "optimal" estimate of the position and/or orientation of the UAV. For example, a Kalman Filter can fuse accelerometer, gyro and magnetometer measurements with a velocity estimate to estimate the UAV's yaw, pitch and roll.

For more information on the sensors and algorithms used in UAV state estimation, try the stand-alone article Fundamentals of Small Unmanned Aircraft Flight.

The article also links to accompanying Matlab code implementing the described Kalman Filter UAV state estimation algorithms.

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