ALPAKA/sta-peak
1
0
Fork 0
mirror of https://git.intern.spaceteamaachen.de/ALPAKA/sta-peak.git synced 2025-06-11 18:35:59 +00:00
Code Issues Actions Packages Projects Releases Wiki Activity

Optimize correct

Browse Source
This commit is contained in:
Milo Priegnitz 2024-07-15 17:25:15 +02:00 committed by dario
parent ee10ce1189
commit d5914f2d1c
3 changed files with 13 additions and 12 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
include/sta/math/algorithms/kalmanFilter.hpp
View File

@ -78,7 +78,7 @@ public:
* @param R The measurement noise covariance matrix.
* @return The corrected state of the Kalman filter.
*/
static KalmanState correct(KalmanState state, matrix z, matrix H, matrix R);
KalmanState correct(KalmanState state, matrix z, matrix H, matrix R);
};
} // namespace math

18
src/algorithms/kalmanFilter.cpp
View File

@ -2,6 +2,7 @@
#include <sta/math/linalg/linalg.hpp>
#include <sta/debug/debug.hpp>
#include <sta/debug/assert.hpp>
#include <sta/debug/profile.hpp>
namespace sta
{
@ -30,9 +31,9 @@ KalmanState KalmanFilter::predict(KalmanState state, matrix u)
{
// Predict step implementation
// Update the state based on the system dynamics
state.x = F_ * state.x + B_ * u;
state.x = (F_ * state.x) + (B_ * u);
// Update the error covariance matrix
state.error = F_ * state.error * F_.T() + Q_;
state.error = F_ *( state.error * F_.T()) + Q_;
return state;
}
@ -40,11 +41,13 @@ KalmanState KalmanFilter::correct(KalmanState state, matrix z)
{
// Correct step implementation
// Calculate the Kalman gain
matrix K = state.error * H_.T() * linalg::inv(H_ * state.error * H_.T() + R_);
K.show_serial();
matrix K;
{
K = state.error * H_.T() * linalg::inv(H_ * state.error * H_.T() + R_);
}
// Update the state based on the measurement
state.x = state.x + K * (z - H_ * state.x); //TODO check transpose
state.x.show_serial();
// Update the error covariance matrix
state.error = (identity_ - K * H_) * state.error;
return state;
@ -55,12 +58,11 @@ KalmanState KalmanFilter::correct(KalmanState state, matrix z, matrix H, matrix
// Correct step implementation
// Calculate the Kalman gain
matrix K = state.error * H.T() * linalg::inv(H * state.error * H.T() + R);
K.show_serial();
// Update the state based on the measurement
state.x = state.x + K * (z - H * state.x); //TODO check transpose
state.x.show_serial();
// Update the error covariance matrix
state.error = (matrix::eye(state.x.get_rows()) - K * H) * state.error;
//state.error = (matrix::eye(state.x.get_rows()) - K * H) * state.error;
state.error = (identity_ - K * H) * state.error;
return state;
}

5
src/linalg/linalg.cpp
View File

@ -4,6 +4,7 @@
#include <cmath>
#include <sta/debug/debug.hpp>
#include <sta/debug/assert.hpp>
#include <sta/debug/profile.hpp>
namespace sta{
@ -13,7 +14,6 @@ namespace linalg {
matrix dot(matrix a, matrix b) {
STA_ASSERT_MSG(a.get_cols() == b.get_rows(), "Matrix dimension mismatch");
uint8_t k = a.get_cols();
@ -119,7 +119,6 @@ matrix skew_symmetric(matrix m) {
};
matrix add(matrix a, matrix b) {
STA_ASSERT_MSG( a.get_rows() == b.get_rows() && a.get_cols() == b.get_cols(), "Matrix dimensions mismatch!" );
matrix output = a.clone();
@ -142,7 +141,7 @@ matrix subtract(matrix a, matrix b) {
return output;
};
matrix dot(matrix m, float s) {
float size = m.get_size();
matrix output = m.clone();