Congratulations, you have reached the end of this introduction course to linear common filter.
So let's recap everything that we've covered.
So we have covered how to probabilistically express the uncertainty using probability distribution.
So we've looked at using the Gaussian distribution to estimate the uncertainty inside the state and
measurements.
We have had a look how to convert differential equations into the states based representation for linear
and nonlinear representations.
We know how then to simulate these states based dynamic systems, whether they're linear or nonlinear.
We've also looked at version of estimation called least squares estimation to solve static estimation
problems.
And we've also then extended that into the linear come and fill it up and looked at how to solve optimal
estimation problems, which was the bulk of this course.
We know how to drive the different system matrices for the common filter and we know how to do this
in general so we can apply this to any filter, any problem that we want to apply to Linear Algebra
two.
We also know how to optimally tuned linear midfielder to get the best performance.
And finally, we have covered how to implement the linear common filter in Python.
But the equations are essentially same.
So we can carry this over to any different programming languages or the math is exactly the same.
All the concepts are exactly the same.
So in conclusion, this should be everything you need to know to get you started with the linear algebra
for sense of fusion and state estimation, you should be able to handle any problems or situation that
arise using the fundamental knowledge in this course.
We have also included the reference documents and cheat sheets, which are a handy reference for when
you go through and implement your own computer or any problems you run into.
So you are now definitely on your way to becoming a subject matter expert on all things common filtering.
So in conclusion, I'd like to personally thank you for purchasing this course and taking the time to
finish the course.
I hope you enjoyed the course and it's been very useful for you.
Please consider my other courses for any of your future learning adventures.