s4k10503 / KalmanFilterImplementations

An open-source collection of Kalman Filter implementations in various programming languages.

Multi-Language Kalman Filter Implementations

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/.

C# Implementation

The C# implementation can be found in the src/csharp directory. This implementation uses the MathNet.Numerics library.

Dependencies

The C# implementation uses the following libraries:

• MathNet.Numerics (MIT License)

Usage

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.

Python Implementation

The Python implementation can be found in the src/python directory.

Dependencies

The Python implementation uses the following libraries:

• numpy (BSD License)
• scipy (BSD License)

Usage

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.

C++ Implementation

The C++ implementation can be found in the src/cpp directory.

Dependencies

The C++ implementation uses the following libraries:

• Eigen (MPL2 License)
• Boost (Boost Software License)

Usage

#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.