AndreiMoraru123/2D-Tracker

Linear, Extended & Unscented Kalman filter Fusion Models for 2D tracking

Can you outrun the Big Bad Kalman filter ?

Some linear, extended and unscented movement tracking Kalman filters, with a fun twist

Run ObjectTracker.m and make sure all files are in the same directory. Set your scenarios using the dropdowns.

Press Play and enjoy :-)

Go for Developer Mode if you want to generate your own custom data and play around with the trackers:

Model Parameters	Filter Tuning	Extra Sensor

Note You can control the Seal if you own an Arduino + MPU IMU sensor suite, this is how it works.

To achieve this, you may choose Command Driven instead of Simulation for the Running Mode.

Demos

Noob level: defeat the linear Kalman filter

The Shark can only chase you in a linear fashion

Test each of your runs:

Experienced: defeat the extended Kalman filter

The Shark is getting help from a Seagull, who acts like a sensor for detecting your non-linear movements

Measure your performances:

Note You can trick the shark by moving fast in a non-linear manner

This way you can make the filter diverge due to wrong partial derivative computation

Legendary: defeat the unscented Kalman filter

No more linear covariance transforms, the Shark has unlocked the Unscented Transform ability

And see how far your can get:

How this madness was designed:

engineered:

and programmed:

with the following workflow:

and if you made it this far...

here is the whole thing explained in detail (Vampire language):

OneFilterToRuleThemAll.pdf