⚡️Kalman Filter

A package implementing Kalman filtering and smoothing for continuous time-indexed models with non-uniform time transitions. A collection of models are also provided, including a constant velocity motion model and a Brownian motion model. Support for prediction, filtering, smoothing and sensor fusion are currently implemented.

Design

The package has two main components

Kalman Filter/Smoother

These implement state space estimation for a given model and measurements.

Model

The model (such as models.LinearModel) provide common example models for modelling time series data. For example models.ConstantVelocityModel, models the position and velocity of a particle over time.

Examples

For runnable examples, see /examples. Below, a full runnable example of filtering a noisy time series. The model here is just a Brownian motion model, which assumes that the time series represents a hidden value that is static apart from a Brownian noise component.

package main

import (
	"fmt"
	"time"
	"github.com/rosshemsley/kalman"
	"github.com/rosshemsley/kalman/models"
)

func main() {
	var t time.Time
	values := []float64{1.3, 10.2, 5.0, 3.4}

	model := models.NewSimpleModel(t, values[0], models.SimpleModelConfig{
		InitialVariance:     1.0,
		ProcessVariance:     1.0,
		ObservationVariance: 2.0,
	})
	filter := kalman.NewKalmanFilter(model)

	for _, v := range values {
		t = t.Add(time.Second)
		filter.Update(t, model.NewMeasurement(v))
		fmt.Printf("filtered value: %f\n", model.Value(filter.State()))
	}
}