cs-kalman-filters 1.0.1

Kalman Filters

Install-Package cs-kalman-filters -Version 1.0.1
dotnet add package cs-kalman-filters --version 1.0.1
<PackageReference Include="cs-kalman-filters" Version="1.0.1" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add cs-kalman-filters --version 1.0.1
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cs-kalman-filters

Kalman Filters implemented in .NET

Install

Run the following command to install the nuget package:

Install-Package cs-kalman-filters

Usage

The following sample codes show how to use the 1d and 2d kalman-filters:

using System;

namespace KalmanFilters
{
    class Program
    {
        static void Main(string[] args)
        {
            TestFilter1D();
            TestFilter2D();
        }

        public static void TestFilter1D()
        {
            double[] measurements = new double[5] { 5.0, 6.0, 7.0, 9.0, 10.0 };
            double[] motion = new double[5] { 1.0, 1.0, 2.0, 1.0, 1.0 };
            double measurement_sigma = 4.0;
            double motion_sigma = 2.0;
            double mu = 0;
            double sigma = 10000;

            KalmanFilter1D filter = new KalmanFilter1D(mu, sigma, measurement_sigma, motion_sigma);

            for (int t = 0; t < measurements.Length; ++t)
            {
                filter.Update(measurements[t]);
                filter.Predict(motion[t]);
                Console.WriteLine(filter.BeliefDistributionDescription);
            }
        }



        public static void TestFilter2D()
        {
            double[] measurements = new double[3] { 1, 2, 3 }; //measurement of locations at t = 1, 2, 3

            //apply kalman filter to predict the velocity and location at t = 4
            KalmanFilter2D filter = new KalmanFilter2D();

            for (int t = 0; t < measurements.Length; ++t)
            {
                filter.Update(measurements[t]);
                filter.Predict();
            }

            Console.WriteLine("x: {0}", filter.StateDescription);
            Console.WriteLine("P: {0}", filter.UncertaintyDescription);

        }
    }
}

cs-kalman-filters

Kalman Filters implemented in .NET

Install

Run the following command to install the nuget package:

Install-Package cs-kalman-filters

Usage

The following sample codes show how to use the 1d and 2d kalman-filters:

using System;

namespace KalmanFilters
{
    class Program
    {
        static void Main(string[] args)
        {
            TestFilter1D();
            TestFilter2D();
        }

        public static void TestFilter1D()
        {
            double[] measurements = new double[5] { 5.0, 6.0, 7.0, 9.0, 10.0 };
            double[] motion = new double[5] { 1.0, 1.0, 2.0, 1.0, 1.0 };
            double measurement_sigma = 4.0;
            double motion_sigma = 2.0;
            double mu = 0;
            double sigma = 10000;

            KalmanFilter1D filter = new KalmanFilter1D(mu, sigma, measurement_sigma, motion_sigma);

            for (int t = 0; t < measurements.Length; ++t)
            {
                filter.Update(measurements[t]);
                filter.Predict(motion[t]);
                Console.WriteLine(filter.BeliefDistributionDescription);
            }
        }



        public static void TestFilter2D()
        {
            double[] measurements = new double[3] { 1, 2, 3 }; //measurement of locations at t = 1, 2, 3

            //apply kalman filter to predict the velocity and location at t = 4
            KalmanFilter2D filter = new KalmanFilter2D();

            for (int t = 0; t < measurements.Length; ++t)
            {
                filter.Update(measurements[t]);
                filter.Predict();
            }

            Console.WriteLine("x: {0}", filter.StateDescription);
            Console.WriteLine("P: {0}", filter.UncertaintyDescription);

        }
    }
}

Release Notes

Kalman Filters in .NET 4.6.1

Dependencies

This package has no dependencies.

This package is not used by any popular GitHub repositories.

Version History

Version Downloads Last updated
1.0.1 292 5/1/2018