EmrysFilter 0.1.0
State estimation utilities in C++23 using Eigen
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Models.h
Go to the documentation of this file.
1#pragma once
2
8
138
139#include <EmrysFilter/Models/Batched.h>
140#include <EmrysFilter/Models/Condition.h>
141#include <EmrysFilter/Models/Consistency.h>
142#include <EmrysFilter/Models/CramerRao.h>
143#include <EmrysFilter/Models/Emission.h>
144#include <EmrysFilter/Models/FormSteps.h>
145#include <EmrysFilter/Models/IteratedUpdate.h>
146#include <EmrysFilter/Models/Likelihood.h>
147#include <EmrysFilter/Models/Linearization.h>
148#include <EmrysFilter/Models/Measurement.h>
149#include <EmrysFilter/Models/MeasurementModel.h>
150#include <EmrysFilter/Models/MeasurementReads.h>
151#include <EmrysFilter/Models/MeasurementUpdate.h>
152#include <EmrysFilter/Models/Methods.h>
153#include <EmrysFilter/Models/Placement.h>
154#include <EmrysFilter/Models/Policies.h>
155#include <EmrysFilter/Models/ProcessModel.h>
156#include <EmrysFilter/Models/Reduction.h>
157#include <EmrysFilter/Models/Simulation.h>
158#include <EmrysFilter/Models/SwitchingModel.h>
159#include <EmrysFilter/Models/Transform.h>
160#include <EmrysFilter/Models/TransformArgs.h>
Batched.h
Batched functions (decisions.md [batched-functions]): batched(), the mark that a function takes every...
Condition.h
The Kalman conditioning of a marked input on an observation of a transform's output (decisions....
Consistency.h
Consistency checks (decisions.md [composed-features]): NisMonitor, a windowed chi-squared test on the...
CramerRao.h
PosteriorCramerRaoBound: the Fisher information of the state along true trajectories,...
Emission.h
The measurement of a discrete state: the EmissionModel concept, which fills one log-likelihood per va...
FormSteps.h
The prediction and update steps of the square-root and information forms of the Gaussian (decisions....
IteratedUpdate.h
The iterated measurement update, all internal (decisions.md [iterated-update], [sigma-point-sets]): t...
Likelihood.h
The likelihoods that sample-based filters weight by (decisions.md [likelihood-measurements],...
Linearization.h
What a transform returns and what a measurement update reads from it (decisions.md [linearization-for...
MeasurementModel.h
Measurement models and their observations (decisions.md [model-policies], [sensors],...
MeasurementReads.h
How an update reads a measurement, all internal (decisions.md [model-policies], [measurement-space-fu...
MeasurementUpdate.h
What an update returns (decisions.md [weighted-branches], [innovation-statistics]): an UpdateBranch p...
Measurement.h
The update of a Gaussian by a measurement (decisions.md [templated-measurements], [weighted-branches]...
Methods.h
The transform methods, as policies made by their factories (decisions.md [policy-factories],...
Placement.h
Models placed in a larger state and measurement models side by side (decisions.md [model-placement],...
Policies.h
The policies a model is built from, and the marks on a step's arguments (decisions....
ProcessModel.h
Process models as functions with reads and writes (decisions.md [model-policies], [continuous-time-no...
Reduction.h
Reducing an update's branches (decisions.md [weighted-branches], [reduction-policies],...
Simulation.h
simulate(): a draw of a process model's next true state, the ground truth that a simulation runs a fi...
SwitchingModel.h
SwitchingModel (decisions.md [switching-models], [imm]): a Markov chain on a discrete mode space and ...
TransformArgs.h
The arguments of transform() and the joint input they form (decisions.md [prediction-inputs],...
Transform.h
Propagating a distribution through a function (decisions.md [method-per-measurement],...