Kalman Filter Simulation Tutorials

This project is the reorganization of the code in the book Kalman-and-Bayesian-Filters-in-Python and draws on some content in EKF/UKF Toolbox for MATLAB.

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Goals

• Provide a set of easy-to-understand introductory tutorials
• Build a filter simulation toolkit that is friendly to beginners

Requirements

To build the environment, there are 3 options

• Install Anaconda
• Install Miniconda
• Install Miniforge

For option 2&3, you need to run the following command after installation

conda install matplotlib pandas scipy sympy jupyterlab

Then, clone this repo

git clone https://github.com/ivaquero/blog-filters.git

Finally, launch jupyterlab to run the code

cd [this repo] && jupyter lab

Examples

• filters-ghk.ipynb: α-β-γ filtering
• filters-bayes.ipynb: Basics of Bayesian Statistics
• filters-kf-basic.ipynb: Basics of Kalman Filtering
• filters-kf-design.ipynb: Kalman Filter Design
• filters-kf-plus.ipynb: Nonlinear Kalman Filtering
• filters-maneuver.ipynb: Maneuvering Target Tracking
• filters-pf.ipynb: Particle Filtering
• filters-smoothers.ipynb: Smoothers
• filters-task-fusion.ipynb: Data Fusion
• filters-task-tracking.ipynb: Target Tracking

Kit Structure

• filters: Filter-related module
  • bayes: Bayesian statistics
  • fusion: data fusion
  • ghk: α-β-γ filtering
  • ghq: Gaussian-Hermite numerical integration
  • imm: interactive multiple models
  • kalman_ckf: cubature Kalman filter
  • kalman_ekf: extended Kalman filter
  • kalman_enkf: ensemble Kalman filter
  • kalman_fm: fading-memory filter
  • kalman_hinf: H∞ filter
  • kalman_ukf: unscented Kalman filter
  • kalman: linear Kalman filter
  • lsq: the least squares filter
  • particle: particle filter
  • resamplers: sampler
  • sigma_points: Sigma point
  • smoothers: smoother
  • solvers: equation solvers (such as Runge-Kutta)
  • stats: statistical indicators
  • helpers: auxiliary tools
• models: Model-related module
  • const_acc: constant acceleration model
  • const_vel: constant velocity model
  • coord_ture: coordinated rotation model
  • singer: Singer model
  • noise: model noise
• ssmodel*: model base class
• plots: Plot-related module
  • plot_common: common plot (measurement, trajectory, residual)
  • plot_bayes: Bayes statistical plot
  • plot_nonlinear: nonlinear statistical plot
  • plot_gh: α-β-γ filter plot
  • plot_kf: Kalman filter plot
  • plot_kf_plus: nonlinear Kalman filter plot
  • plot_pf: particle filter plot
  • plot_sigmas: Sigma point plot
  • plot_adaptive: adaptive plot
  • plot_fusion: data fusion plot
  • plot_smoother: smoother plot
• simulators: Simulation-related module
  • datagen: common data generation
  • linear: linear motion model
  • maneuver: maneuver model
  • radar: ground radar model
  • robot: robot model
  • trajectory: projectile model
• cfg: Simulation configuration interface
• clutter: Clutter-related module
• tracker: Tracking-related module
  • associate: association
  • pda: probabilistic data association
  • estimators: state estimation
  • track*: trackers with association
• symbol: Symbol derivation module
  • datagen: data generation
  • models: motion model