Tutorials

Here are some tutorials on using Stone Soup that also introduce some topics of state estimation and tracking.

1 - An introduction to Stone Soup: using the Kalman filter

1 - An introduction to Stone Soup: using the Kalman filter

2 - Non-linear models: extended Kalman filter

2 - Non-linear models: extended Kalman filter

3 - Non-linear models: unscented Kalman filter

3 - Non-linear models: unscented Kalman filter

4 - Sampling methods: particle filter

4 - Sampling methods: particle filter

5 - Data association - clutter tutorial

5 - Data association - clutter tutorial

6 - Data association - multi-target tracking tutorial

6 - Data association - multi-target tracking tutorial

7 - Probabilistic data association tutorial

7 - Probabilistic data association tutorial

8 - Joint probabilistic data association tutorial

8 - Joint probabilistic data association tutorial

9 - Initiators & Deleters

9 - Initiators & Deleters

10 - Tracking in simulation: bringing all components together

10 - Tracking in simulation: bringing all components together

Data association

Here are some tutorials which cover additional data association techniques.

k-d trees and TPR trees

k-d trees and TPR trees

Filters

Here are some tutorials which cover additional filtering techniques.

Accumulated States Densities - Out-of-Sequence measurements

Accumulated States Densities - Out-of-Sequence measurements

Gaussian mixture PHD tutorial

Gaussian mixture PHD tutorial

Information filter tutorial

Information filter tutorial

Sensor Management

Here are some tutorials that introduce sensor management using Stone Soup classes.

1 - Single Sensor Management

1 - Single Sensor Management

2 - Multiple Sensor Management

2 - Multiple Sensor Management

3 - Optimised Sensor Management

3 - Optimised Sensor Management