v3.12.3

LowLevelParticleFilters v3.12.3

Diff since v3.12.2

Merged pull requests:

• improve numerics in IMM (#175) (@baggepinnen)
• Rm dep (#176) (@baggepinnen)

v3.12.2

LowLevelParticleFilters v3.12.2

Diff since v3.12.1

Add experimental implementation of the "Interacting Multiple model filter" IMM

Merged pull requests:

• add Interacting Multiple Models (#173) (@baggepinnen)
• Generalize correct for APF (#174) (@baggepinnen)

v3.12.1

LowLevelParticleFilters v3.12.1

Diff since v3.12.0

Merged pull requests:

• handle low-dimensional noise (#172) (@baggepinnen)

Closed issues:

• Innovation function and other functions as inputs (#168)

v3.12.0

LowLevelParticleFilters v3.12.0

Diff since v3.11.0

New concept introduced: Measurement models. See Measurement models in the documentation.

Merged pull requests:

• make it possible to override Kalman-filter matrices (#165) (@baggepinnen)
• smoothing performance improvements (#166) (@baggepinnen)
• provide richer feedback on cholesky error (#167) (@baggepinnen)
• add option to supply custom covariance functions to UKF (#169) (@baggepinnen)
• optimize numerics in EKF smoothing (#170) (@baggepinnen)
• allow multiple measurement models in Kalman filters (#171) (@baggepinnen)

v3.11.0

LowLevelParticleFilters v3.11.0

Diff since v3.10.1

Merged pull requests:

• add ability to plot covariance in Kalman filter solution plot (#164) (@baggepinnen)

v3.10.1

LowLevelParticleFilters v3.10.1

Diff since v3.10.0

Merged pull requests:

• rm explicit deps (#162) (@baggepinnen)
• more robust detection of inplace measurement for MeasurementOop (#163) (@baggepinnen)

v3.10.0

LowLevelParticleFilters v3.10.0

Diff since v3.9.1

• Performance improvements for large systems
• Support for in-place dynamics and measurement functions in EKF/UKF
• Support for user-defined Jacobians in EKF
• Ability to plot output prediction and prediction error in Kalman-filter solution plot
• New tutorial: Adaptive Neural-Network training

Merged pull requests:

• accept user-defined jacobians in EKF (#158) (@baggepinnen)
• introduce optional non-unit sample interval (#159) (@baggepinnen)
• Performance optimizations for large systems (#160) (@baggepinnen)
• add adaptive neural-network tutorial (#161) (@baggepinnen)

v3.9.1

LowLevelParticleFilters v3.9.1

Diff since v3.9.0

Merged pull requests:

• Add mechanism for rejection of bad sigma points (#155) (@baggepinnen)
• add some tests (#156) (@baggepinnen)
• copy input arrays that are mutated as state (#157) (@baggepinnen)

v3.9.0

LowLevelParticleFilters v3.9.0

Diff since v3.8.0

• Add augmented UKF. This version of the UKF takes explicit noise terms as input to the dynamics and is thus able to handle non-additive noise.
• Move Distributions.jl to a weak dependency and thus reduce the number of transitive dependencies substantially. This should be non-breaking since nothing from Distributions.jl was previously exported. This means that users that only use Kalman filters do not have to depend on Distributions.jl. To handle the most common case of multivariate normal distributions, a LowLevelParticleFilters.SimpleMvNormal type is added.

Merged pull requests:

• CompatHelper: add new compat entry for MonteCarloMeasurements in [weakdeps] at version 1, (keep existing compat) (#151) (@github-actions[bot])
• CompatHelper: add new compat entry for Plots in [weakdeps] at version 1, (keep existing compat) (#152) (@github-actions[bot])
• move Distributions.jl to weak dep (#153) (@baggepinnen)
• add augmented UKF (#154) (@baggepinnen)

Closed issues:

• More flexible noise in UKF (#115)

v3.8.0

LowLevelParticleFilters v3.8.0

Diff since v3.7.1

This version

1. Moves from Requires.jl to package extensions for Plots.jl and MonteCarloMeasurements.jl
2. Removes a significant number of direct and transitive dependencies, corresponding to the following change:

(LowLevelParticleFilters) pkg> rm Requires SciMLBase SeeToDee SimpleNonlinearSolve
    Updating `~/.julia/dev/LowLevelParticleFilters/Project.toml`
  [ae029012] - Requires v1.3.0
  [0bca4576] - SciMLBase v2.59.2
  [1c904df7] - SeeToDee v1.3.0
  [727e6d20] - SimpleNonlinearSolve v2.0.0
    Updating `~/.julia/dev/LowLevelParticleFilters/Manifest.toml`
  [47edcb42] - ADTypes v1.9.0
  [7d9f7c33] - Accessors v0.1.38
  [70df07ce] - BracketingNonlinearSolve v1.1.0
  [7057c7e9] - Cassette v0.3.14
  [38540f10] - CommonSolve v0.2.4
  [a33af91c] - CompositionsBase v0.1.2
  [2569d6c7] - ConcreteStructs v0.2.3
  [187b0558] - ConstructionBase v1.5.8
  [e2d170a0] - DataValueInterfaces v1.0.0
  [a0c0ee7d] - DifferentiationInterface v0.6.22
  [4e289a0a] - EnumX v1.0.4
  [f151be2c] - EnzymeCore v0.8.5
  [e2ba6199] - ExprTools v0.1.10
  [6b7a57c9] - Expronicon v0.8.5
  [9aa1b823] - FastClosures v0.3.2
  [442a2c76] - FastGaussQuadrature v1.0.2
  [6a86dc24] - FiniteDiff v2.26.0
  [f62d2435] - FunctionProperties v0.1.2
  [069b7b12] - FunctionWrappers v1.1.3
  [77dc65aa] - FunctionWrappersWrappers v0.1.3
  [46192b85] - GPUArraysCore v0.2.0
  [3587e190] - InverseFunctions v0.1.17
  [82899510] - IteratorInterfaceExtensions v1.0.0
  [87fe0de2] - LineSearch v0.1.4
  [d8e11817] - MLStyle v0.4.17
  [bb5d69b7] - MaybeInplace v0.1.4
  [be0214bd] - NonlinearSolveBase v1.3.1
  [d236fae5] - PreallocationTools v0.4.24
  [731186ca] - RecursiveArrayTools v3.27.3
  [7e49a35a] - RuntimeGeneratedFunctions v0.5.13
  [0bca4576] - SciMLBase v2.59.2
  [19f34311] - SciMLJacobianOperators v0.1.1
  [c0aeaf25] - SciMLOperators v0.3.12
  [53ae85a6] - SciMLStructures v1.5.0
  [1c904df7] - SeeToDee v1.3.0
  [efcf1570] - Setfield v1.1.1
  [727e6d20] - SimpleNonlinearSolve v2.0.0
  [2efcf032] - SymbolicIndexingInterface v0.3.34
  [3783bdb8] - TableTraits v1.0.1
  [bd369af6] - Tables v1.12.0
  [a759f4b9] - TimerOutputs v0.5.25
  [8ba89e20] - Distributed
  [9fa8497b] - Future

The change should be non breaking, please open an issue if you experience any problems with this change!

Merged pull requests:

• remove unused R2d (#147) (@baggepinnen)
• rm SciML deps (#148) (@baggepinnen)
• rm mcm dep (#149) (@baggepinnen)
• introduce PlotsExt instead of Requires (#150) (@baggepinnen)