This repository contains implementations of a Kalman filter in multiple programming languages. Currently, the following languages are supported:
- C#
- Python
- C++
Each implementation can be found in the respective directory under src/
.
The C# implementation can be found in the src/csharp
directory. This implementation uses the MathNet.Numerics library.
The C# implementation uses the following libraries:
- MathNet.Numerics (MIT License)
using MathNet.Numerics.LinearAlgebra;
using MathNet.Numerics.Distributions;
// Initialize model parameters
Matrix<float> F = ...; // State transition matrix
Matrix<float> H = ...; // Observation matrix
Matrix<float> B = ...; // Control matrix
Matrix<float> x = ...; // Initial state estimate
Matrix<float> P = ...; // Initial error covariance estimate
Matrix<float> Q = ...; // Estimated error in process
Matrix<float> R = ...; // Estimated error in measurements
// Create an instance of the KalmanFilter class
var kf = new KalmanFilter(F, H, B, x, P, Q, R);
// Control vector
Matrix<float> u = ...;
// Perform the prediction step
kf.Predict(u);
// Measurement vector
Matrix<float> z = ...;
// Perform the update step
kf.Update(z);
// Get the current state estimate
var currentState = kf.GetCurrentState();
Please replace the ... with appropriate data.
The Python implementation can be found in the src/python
directory.
The Python implementation uses the following libraries:
import numpy as np
from kalman_filter import KalmanFilter
# Initialize the Kalman Filter
F = np.array([[...]]) # State transition matrix
H = np.array([[...]]) # Observation matrix
B = np.array([[...]]) # Control matrix
x = np.array([[...]]) # Initial state estimate
P = np.array([[...]]) # Initial error covariance estimate
Q = np.array([[...]]) # Estimated error in process
R = np.array([[...]]) # Estimated error in measurement
kf = KalmanFilter(F, H, B, x, P, Q, R)
# Update the state transition matrix
dt = ... # Time step
kf.update_state_transition_matrix(dt)
# Predict the next state
u = np.array([[...]]) # Control input
kf.predict(u)
# Update the state with the observation
z = np.array([[...]]) # Observation
kf.update(z)
# Get the current state estimate
x = kf.get_current_state()
Please replace the ... with appropriate data.
The C++ implementation can be found in the src/cpp
directory.
The C++ implementation uses the following libraries:
#include "KalmanFilter.h"
#include <Eigen/Dense>
#include <boost/math/distributions/chi_squared.hpp>
// Initialize model parameters
Eigen::MatrixXd F = ...; // State transition matrix
Eigen::MatrixXd B = ...; // Control matrix
Eigen::MatrixXd H = ...; // Observation matrix
Eigen::MatrixXd x = ...; // Initial state estimate
Eigen::MatrixXd P = ...; // Initial error covariance estimate
Eigen::MatrixXd Q = ...; // Estimated error in process
Eigen::MatrixXd R = ...; // Estimated error in measurements
// Create an instance of the KalmanFilter class
KalmanFilter kf(F, B, H, x, P, Q, R);
// Time step
double dt = ...;
// Update the state transition matrix
kf.update_state_transition_matrix(dt);
// Control vector
Eigen::MatrixXd u = ...;
// Perform the prediction step
kf.predict(u);
// Measurement vector
Eigen::MatrixXd z = ...;
// Perform the update step
kf.update(z);
// Get the current state estimate
Eigen::MatrixXd currentState = kf.get_current_state();
Please replace the ... with appropriate data.