Merge pull request #1 from vortexntnu/rework/bayesian-framework

.clang-format

@@ -1,4 +1,5 @@
 ---
+# Documentation: https://releases.llvm.org/17.0.1/tools/clang/docs/ClangFormatStyleOptions.html
 Language:        Cpp
 # BasedOnStyle:  LLVM
 AccessModifierOffset: -2
@@ -132,5 +133,5 @@ StatementMacros:
   - QT_REQUIRE_VERSION
 TabWidth:        2
 UseCRLF:         false
-UseTab:          ForIndentation
+UseTab:          Never
 ...

.github/workflows/clang-formatter.yml

@@ -13,15 +13,15 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v2
-    - uses: DoozyX/clang-format-lint-action@v0.14
+    - uses: actions/checkout@v3
+    - uses: DoozyX/clang-format-lint-action@v0.17
       with:
         source: '.'
         exclude: './lib'
         extensions: 'h,hpp,cpp,c'
-        clangFormatVersion: 14
+        clangFormatVersion: 17
         inplace: True
-    - uses: EndBug/add-and-commit@v4
+    - uses: EndBug/add-and-commit@v9
       with:
         author_name: Clang Robot
         author_email: [email protected]

README.md

@@ -1,26 +1,8 @@
-Simple Kalman Filter for estimating objects in odom frame. Includes a general ekf implementation which can be configured for
-use with generic process and sensor model.
-
-Kalman Filter:
-
-state vector:       x = [x, y, z, phi, theta, psi]
-measurement vector: y = [x, y, z, phi, theta, psi]
-
-process model:     x_dot = 0 + v
-measurement model: y = I_6x6 * x
-
-
-Important Files in ekf_python2:
-
-dynamicmodels_py2.py: contains a parent class for dynamic models and as many different dynamic models as need to be defined 
-as subclasses
-
-measurementmodels_py2.py: contains a parent class for sensor models and as many different sensor models as need to be defined 
-as subclasses
-
-ekf_py2.py: contains a class implementing the ekf equations. In the case of linear model this will collapse to a standard Kalman Filter
-
-gaussparams_py2.py: contains a class which implements multivariate gaussians
-
-Pip3/pip requirement:
-pip install dataclasses
+# Vortex VKF
+## Introduction
+This is a C++ library for state estimation.
+
+## Packages
+### vortex-filtering
+Currently, it has a pure C++ implementation of the Unscented Kalman Filter (UKF) and the Extended Kalman Filter (EKF) as well as model
+[More info](vortex-filtering/README.md)

docs/vkf_class_diagram.md

@@ -0,0 +1,71 @@
+<!-- Can be viewed in vscode -->
+
+```mermaid
+classDiagram
+
+    DynamicModel <|-- DynamicModelLTV
+    SensorModel <|-- SensorModelLTV
+    DynamicModelLTV <|-- ConstantVelocity
+    DynamicModelLTV <|-- ConstantAcceleration
+    DynamicModelLTV <|-- CoordinatedTurn
+
+    EKF -- DynamicModelLTV
+    EKF -- SensorModelLTV
+
+    UKF -- DynamicModel
+    UKF -- SensorModel
+
+
+
+    class EKF{
+        +predict()
+        +update()
+        +step()
+    }
+
+    class UKF{
+        +predict()
+        +update()
+        +step()
+        -get_sigma_points()
+        -propagate_sigma_points_f()
+        -propagate_sigma_points_h()
+        -estimate_gaussian()
+    }
+
+    class DynamicModel{
+        +virtual f_d() Vec_x
+        +virtual Q_d() Mat_vv
+        +sample_f_d() Vec_x
+    }
+
+    class SensorModel{
+        +virtual h() Vec_z
+        +virtual R() Mat_ww
+        +sample_h() Vec_z
+    }
+
+    class DynamicModelLTV {
+        +overide f_d() Vec_x
+        +virtual A_d() Mat_xx
+        +virtual Q_d() Mat_vv
+        +vurtual G_d() Mat_xv
+        +pred_from_est() Gauss_x
+        +pred_from_state() Gauss_x
+    }
+
+    class SensorModelLTV {
+        +override h(x) Vec_z
+        +virtual R(x) Mat_ww
+        +virtual C(x) Mat_zx
+        +virtual H(x) Mat_zw
+        +pred_from_est(x_est) Gauss_z
+        +pred_from_state(x) Gauss_z
+    }
+
+    class ConstantVelocity
+    class CoordinatedTurn
+    class ConstantAcceleration
+
+```
+<!-- Can be edited at https://mermaid.live/edit -->