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https://github.com/baggepinnen/LowLevelParticleFilters.jl
State estimation, smoothing and parameter estimation using Kalman and particle filters.
https://github.com/baggepinnen/LowLevelParticleFilters.jl
bayesian-inference control-systems controls data-assimilation dynamical-systems estimation extended-kalman-filter kalman-filter monte-carlo-methods parameter-estimation particle-filter prediction-error-method sequential-monte-carlo state-estimation state-estimation-algorithms state-estimation-filters system-identification unscented-kalman-filter virtual-sensing virtual-sensors
Last synced: 3 months ago
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State estimation, smoothing and parameter estimation using Kalman and particle filters.
- Host: GitHub
- URL: https://github.com/baggepinnen/LowLevelParticleFilters.jl
- Owner: baggepinnen
- License: other
- Created: 2018-02-17T12:20:17.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-06-03T06:40:32.000Z (5 months ago)
- Last Synced: 2024-06-03T08:10:33.534Z (5 months ago)
- Topics: bayesian-inference, control-systems, controls, data-assimilation, dynamical-systems, estimation, extended-kalman-filter, kalman-filter, monte-carlo-methods, parameter-estimation, particle-filter, prediction-error-method, sequential-monte-carlo, state-estimation, state-estimation-algorithms, state-estimation-filters, system-identification, unscented-kalman-filter, virtual-sensing, virtual-sensors
- Language: Julia
- Homepage: https://baggepinnen.github.io/LowLevelParticleFilters.jl/stable
- Size: 191 MB
- Stars: 112
- Watchers: 5
- Forks: 15
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
- awesome-sciml - baggepinnen/LowLevelParticleFilters.jl: Simple particle/kalman filtering, smoothing and parameter estimation
README
# LowLevelParticleFilters
[](https://github.com/baggepinnen/LowLevelParticleFilters.jl/actions)
[](https://codecov.io/gh/baggepinnen/LowLevelParticleFilters.jl)
[](https://baggepinnen.github.io/LowLevelParticleFilters.jl/stable)
[](https://baggepinnen.github.io/LowLevelParticleFilters.jl/dev)
This is a library for state estimation, smoothing and parameter estimation.
# Estimator Types
We provide a number of filter types
- `ParticleFilter`: This filter is simple to use and assumes that both dynamics noise and measurement noise are additive.
- `AuxiliaryParticleFilter`: This filter is identical to `ParticleFilter`, but uses a slightly different proposal mechanism for new particles.
- `AdvancedParticleFilter`: This filter gives you more flexibility, at the expense of having to define a few more functions.
- `KalmanFilter`. A standard Kalman filter. Has the same features as the particle filters, but is restricted to linear dynamics (possibly time varying) and Gaussian noise.
- `SqKalmanFilter`. A standard Kalman filter on square-root form (slightly slower but more numerically stable with ill-conditioned covariance).
- `ExtendedKalmanFilter`: For nonlinear systems, the EKF runs a regular Kalman filter on linearized dynamics. Uses ForwardDiff.jl for linearization. The noise model must be Gaussian.
- `UnscentedKalmanFilter`: The Unscented kalman filter often performs slightly better than the Extended Kalman filter but may be slightly more computationally expensive. The UKF handles nonlinear dynamics and measurement model, but still requires an additive Gaussian noise model.
- `DAEUnscentedKalmanFilter`: An Unscented Kalman filter for differential-algebraic systems (DAE).
# Documentation
[](https://baggepinnen.github.io/LowLevelParticleFilters.jl/stable)
[](https://baggepinnen.github.io/LowLevelParticleFilters.jl/dev)