Examples

Here are a selection of examples of Stone Soup features.

Classification

Classification Using Hidden Markov Model

Classification Using Hidden Markov Model

Disjoint Tracking and Classification

Disjoint Tracking and Classification

Control Models

Control Models: Inference with Linear Gaussian Control Model

Control Models: Inference with Linear Gaussian Control Model

Data Association

Comparing Efficient Hypothesis Management (EHM) with probability associators

Comparing Efficient Hypothesis Management (EHM) with probability associators

Data Association with Loopy Belief Propagation

Data Association with Loopy Belief Propagation

Multi-Frame Assignment example

Multi-Frame Assignment example

Generic One-to-One Association Examples

Generic One-to-One Association Examples

Probabilistic Multi-Hypothesis Tracker

Probabilistic Multi-Hypothesis Tracker

General Multi Hypotheses tracking implementation example

General Multi Hypotheses tracking implementation example

Extended Object Tracking

Extended Object Tracking Example (MTT-EOT)

Extended Object Tracking Example (MTT-EOT)

Filters

Expected Likelihood Particle Filters

Expected Likelihood Particle Filters

Ensemble Filter Example

Ensemble Filter Example

Comparing Multiple Trackers On Manoeuvring Targets

Comparing Multiple Trackers On Manoeuvring Targets

Gromov Particle Flow Filter

Gromov Particle Flow Filter

Bearings-only tracking example

Bearings-only tracking example

Metrics

CLEAR MOT example

CLEAR MOT example

Metrics example

Metrics example

Out of Sequence Measurements

Kalman filter with Out-of-Sequence measurements

Kalman filter with Out-of-Sequence measurements

Particle filtering with Out-of-sequence Measurements

Particle filtering with Out-of-sequence Measurements

Handling OOSM using inverse time dynamics

Handling OOSM using inverse time dynamics

Dealing with Out-Of-Sequence Measurements with a fixed lag storage

Dealing with Out-Of-Sequence Measurements with a fixed lag storage

Plotting

Animated Plotters

Animated Plotters

Polar Plotting Example

Polar Plotting Example

Density Plot Example

Density Plot Example

3D plotting example

3D plotting example

Readers

Creating a Custom Reader - Pandas DataFrame

Creating a Custom Reader - Pandas DataFrame

Road Networks

Joint Tracking & Destination Estimation

Joint Tracking & Destination Estimation

Sensor Fusion

Performance comparison between Kalman and Particle Filters

Performance comparison between Kalman and Particle Filters

Sensor Management

Autonomous Source Term Estimation

Autonomous Source Term Estimation

Creating an Actionable Sensor

Creating an Actionable Sensor

Monte Carlo Tree Search for Autonomous Source Term Estimation

Monte Carlo Tree Search for Autonomous Source Term Estimation

Reinforcement Learning Sensor Manager

Reinforcement Learning Sensor Manager

Actionable Platforms

Actionable Platforms

Introduction to Bayesian Search - A Simple 1D Example

Introduction to Bayesian Search - A Simple 1D Example

Bayesian Search with a Moving Platform

Bayesian Search with a Moving Platform

Simulation

Multi-Target Tracking in 3D Using Platform Simulation

Multi-Target Tracking in 3D Using Platform Simulation

Multi-Sensor Moving Platform Simulation Example

Multi-Sensor Moving Platform Simulation Example

Using linearised ODEs from non-linear dynamic models in Stone Soup

Using linearised ODEs from non-linear dynamic models in Stone Soup

Sensor Platform Simulation

Sensor Platform Simulation

Creating Smooth Transitions Between Coordinates

Creating Smooth Transitions Between Coordinates

RangeRangeRateBinning measurement model example

RangeRangeRateBinning measurement model example

Track Fusion

Multi-Sensor Fusion: Covariance Intersection Using Tracks as Measurements

Multi-Sensor Fusion: Covariance Intersection Using Tracks as Measurements

Track Stitching Example

Track Stitching Example

Comparing different filters in the context of track fusion

Comparing different filters in the context of track fusion