Sensor Fusion – MiroLab

POSITION AND ATTITUDE MEASURING SYSTEMS FOR AUTONOMOUS VEHICLES

SENSOR FUSION IN FREQUENCY

Some material that I used to give the lesson (part is in Italian, sorry in the Doebelin version in English the complementary filtering seems not to be present):

Doebelin material
Notes on the filter order needed to compensate for the different kind of drifts (constant, linear, parabolic, etc)
How to choose the cutting frequency?
Laplace transform relations

The Simulink file to simulate the Baro-inertial altimeter:

Simulink File (this link was cancelled this year)

During the lesson I composed the following notes in English:

Baro-inertial altimeter
General considerations about a measurement system: wide band noise and its effects (the low frequency and high frequency components). How the integration operation change its behavior?
Here a note on how the two sensors output are fed to the two complementary filters given their intrinsic noise behaviors
How to choose the cutting frequency?
Comparative table between the two sensors
How to design the two complementary filters in Matlab
How to model in time-frequency and compensate a constant Bias
How to model in time-frequency and compensate a linear Drift
How to reduce the transitory in complementary filtering: First Step (increase cutting frequency at the expense of high frequency noise introduction) and Second Step (increase filters order to increase the low-pass filtering efficiency)

How to use Simulink to simulate whichever differential equation, either linear or non-linear:

Using integrators
or Transfer Function Blocks
Using derivative blocks (not to use)
How to avoid derivative blocks (for example in higher order filters where you have high order input derivatives in the basic differential equations)
Another way to avoid derivative blocks with Direct Form n1, pdf doc 1 and doc 2

Work in class to simulate Complementary filtering in Baro-inertial altitude estimation:

Download this file that contains instructions that you shall follow step to step

Gyro-inclinometer fusion to estimate angle:

Gyroscope + capacitive inclinometer (capacitances enclosed with a dielectric fluid): the gyro has the problem of a linear drift, the inclinometer slow response. So the two instruments are fused with 2° order complementary filters.
In the above link there is also the method to implement in real time the designed complementary filters via recursive equations that can be implemented in a WHILE LOOP.
Simulink scheme to run the simulation via a matlab script

SENSOR FUSION IN THE STATISTICAL DOMAIN