David Phusion is an amazing tool, which you'll find in pretty much every modern piece of technology
that involves any kind of sensing a measurement or automation, you will find it in your smartphone,
you'll find it in your car.
It has enabled amazing feat of engineering and its usage is only going to dramatically increase with
time with new automation technologies such as yourself driving a car.
Overall, data fusion is the process of integrating multiple data sources together to form a more accurate,
more complete and more robust picture than any one source of information alone could provide.
More specifically, we are going to look at the most common use of data fusion, which is sensor fusion
or multi sensor fusion.
This is the process of how we can take data from sensors and other sources of information and turn it
into useful information.
So now let's look at a practical example, so we have a dynamic system and for this example, let's
take the motion and dynamics of an aircraft.
The state of this system describes the current properties of the aircraft at this specific instance
in time.
So these properties could be things like the velocity, position and attitude of the aircraft.
All of these form the current state of the aircraft or the state of the dynamic system in question.
Modern aircraft systems have order pilots to fly the aircraft, but for the autopilot control to work,
the computer needs to know the current state of the aircraft so that it can calculate how to make the
aircraft respond as required.
To get this information, sensors are attached to the aircraft to measure the physical properties of
the system, these sensors could be things like GPS, speed and altitude sensors.
Each sensor provides measurement data about the state of the aircraft, but in most cases, no one sensor
can sense all the required information.
So to form a complete picture about the estimated state of the aircraft, a sensor fusion process is
required.
Sensor fusion also has more capabilities, no sensor in real life is perfect, any measurement it provides
will be corrupted with errors such as noise and biases.
Fusion allows the effects of these areas to be reduced.
And in many cases, this raw sensor data itself would be useless without television to correct these
errors.
If we take, for example, the speed sensor, if we look at a graph where the Y axis is speed and the
X axis is time, and the white line represents the true speed as it changes with the time, the speed
sensor provides measurements of the speed.
As soon as the crane crosses, you can see that these measurements are noisy.
So if we were to use them directly to estimate the speed, the curve would look like something like
this as seen in the Green Line.
It does not best represent what the true speed actually is doing.
But if we had a bit more information about the sensor, such as how accurate it is or how much error
there might be in each measurement, then we can use this information in the data fusion process.
So the blue bars here represent how much error each measurement might have.
The true value should lie somewhere between the extremes.
So if we were to use this information, along with some basic information about the type of vehicle
the speed sensor is attached to, the data fusion process allows us to get a better estimate of what
the true speed really is.
The red line here is the speed.
You can see here that if you speed is a lot better at approximating the true speed, then using just
the original raw measurements.
This will mean that any automation or control process that uses this information will be more accurate,
more robust and more reliable.
If this was a speed sensor for the cruise control in your car without television, it could be very
dangerous.
It might cause your car to suddenly break or accelerate uncontrollably.
But when data fusion is used, it would allow the sensor to be used very safely.
These days, we just expect things to work.
And without data fusion, they would not.