In this video, we're going to have a quick look at the simulation exercises and examples that we're
going to be using for the unscented coming for a portion of this course.
So just like in the previous section when we used an extended family photo, we're going to be using
the same vehicle model and process model, the same problem.
However, we're going to be solving it using an unscented computer rather than an extended common filter.
So the 2D vehicle filter is the same as follows.
The goal is to estimate the position, velocity and orientation of a moving vehicle based on Leida like
measurements to known landmarks and features, also using GPS measurements and gyroscope measurements
of the vehicle.
Now, the problem is that we're going to assume that the vehicle can travel at a constant speed in a
2D explained in the direction it's facing, and we're going to let the inputs of the system be the turn
right or the vehicle from a gyroscope.
And we're going to assume that the acceleration can be modeled as a random variable.
And this is the process model.
So this is exactly the same as the previous model for the extended common filter.
We will assume that we get positional measurements of the vehicle's location from a GPS like sensor,
and this is going to be the first sensor model.
Now, when we use this in, this is a linear model.
So we're just going to be using the same equations as we did for the linear common filter and the extend
the common filter.
We don't need to do any special handling or anything different for the unscented method when it's only
a linear measurement model.
We also ensuring that we get a number of range and relative bearing measurements of known landmark locations,
i.e. this is from the ladylike sense and measurement model, and this is going to be the second measurement
model that we implement inside the unscented common field.
So basically, we're going to implement the process model, the prediction step and the update step
for the second sensor model for the Leida using the antenna filter.
So just like in all the previous exercises, the first step to get the simulation set up for the intended
coming filter is to take the common to underscore UCP understood, a student found and we want to rename
it to Come and Filter Seip.
So now we're going to be using this base file for the exercises in this following section.
And just like before, if you want a working example to play around with it, you can take this top
fall here.
The common theme to underscore U.K. if underscore answer, rename this to come in here and compile and
run that.
And that will give you a working version to compare against.
So now let's have a look at a working simulation, what the final result of all these exercises should
be once you program them of the uncynical method of running the simulation.
So here's a working example of the onset and common filter, and it's running the motion profile which
tests all the different senses at a constant speed, this simulation should give similar results as
the extent to come and filter as they both operate on the same process model and they're both operating
on the nonlinear system.