Object Tracking via Sensor Fusion
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 |
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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 ofSimulation
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):
引用格式
Andrei Moraru (2025). Object Tracking via Sensor Fusion (https://github.com/AndreiMoraru123/2D-Tracker), GitHub. 检索时间: .
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版本 | 已发布 | 发行说明 | |
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1.0.1 | Hi! I had actually left some personal file paths so the code would crash on other machine. I commented them out |
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1.0.0 |
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