using System;
using System.Collections;
using System.Collections.Generic;
using UnityEngine;
public class KalmanLatLong
{
private float MinAccuracy = 1;
private float Q_metres_per_second;
private long TimeStamp_milliseconds;
private double lat;
private double lng;
private float variance; // P matrix. Negative means object uninitialised. NB: units irrelevant, as long as same units used throughout
public KalmanLatLong(float Q_metres_per_second) { this.Q_metres_per_second = Q_metres_per_second; variance = -1; }
public long get_TimeStamp() { return TimeStamp_milliseconds; }
public double get_lat() { return lat; }
public double get_lng() { return lng; }
public float get_accuracy() { return (float)Math.Sqrt(variance); }
public void SetState(double lat, double lng, float accuracy, long TimeStamp_milliseconds)
{
this.lat = lat; this.lng = lng; variance = accuracy * accuracy; this.TimeStamp_milliseconds = TimeStamp_milliseconds;
}
///
/// Kalman filter processing for lattitude and longitude
///
/// new measurement of lattidude
/// new measurement of longitude
/// measurement of 1 standard deviation error in metres
/// time of measurement
/// new state
public void Process(double lat_measurement, double lng_measurement, float accuracy, long TimeStamp_milliseconds)
{
if (accuracy < MinAccuracy) accuracy = MinAccuracy;
if (variance < 0)
{
// if variance < 0, object is unitialised, so initialise with current values
this.TimeStamp_milliseconds = TimeStamp_milliseconds;
lat = lat_measurement; lng = lng_measurement; variance = accuracy * accuracy;
}
else
{
// else apply Kalman filter methodology
long TimeInc_milliseconds = TimeStamp_milliseconds - this.TimeStamp_milliseconds;
if (TimeInc_milliseconds > 0)
{
// time has moved on, so the uncertainty in the current position increases
variance += TimeInc_milliseconds * Q_metres_per_second * Q_metres_per_second / 1000;
this.TimeStamp_milliseconds = TimeStamp_milliseconds;
// TO DO: USE VELOCITY INFORMATION HERE TO GET A BETTER ESTIMATE OF CURRENT POSITION
}
// Kalman gain matrix K = Covarariance * Inverse(Covariance + MeasurementVariance)
// NB: because K is dimensionless, it doesn't matter that variance has different units to lat and lng
float K = variance / (variance + accuracy * accuracy);
// apply K
lat += K * (lat_measurement - lat);
lng += K * (lng_measurement - lng);
// new Covarariance matrix is (IdentityMatrix - K) * Covarariance
variance = (1 - K) * variance;
}
}
}