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KalmanFilter.cpp
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/*
This is a code that process Kalman Filtering of position date from GPS.
*/
#include "KalmanFilter.h"
/*Kalman Filter class Structor*/
KalmanFilter::KalmanFilter()
{
//i = 1;
cosLat = 0.0;
Rearth = 6378137;
}
/*Kalman Filter class Destroier*/
KalmanFilter::~KalmanFilter()
{
}
//
// public methods
//
/*This is a function to filter the GPS data from GPS
Parameter: lat-> Position data1
lon-> Position data2
filtered data->GPS_data:
*/
int64_t* KalmanFilter::KalmanProcessing(int64_t lat, int64_t lon){
time = millis();
prevGPS_data[0][0] = GPS_data[0][0];
prevGPS_data[0][1] = GPS_data[0][1];
prevGPS_data[0][2] = GPS_data[0][2];
GPS_data[0][0] = lat;
GPS_data[0][1] = lon;
GPS_data[0][2] = (uint64_t) time;
//if (i != 100){ //Initialize cosLat
Serial.print("\r\n ==================FirstLat: "); Serial.print((float)firstGPS_data[0][0]); Serial.print(" FirstLon: "); Serial.println((float)firstGPS_data[0][1]);
if (((firstGPS_data[0][0] == GPS_data[0][0])&&(firstGPS_data[0][1] == GPS_data[0][1]))||((firstGPS_data[0][0] == 0)&&(firstGPS_data[0][1] == 0))){
Serial.println("\r\n INIT FIRST!!! ===========================================");
firstGPS_data[0][0] = GPS_data[0][0];
firstGPS_data[0][1] = GPS_data[0][1];
firstGPS_data[0][2] = GPS_data[0][2];
cosLat = cos((float) firstGPS_data[0][0]/10000000*pi/180);
i = 100;
}
delta_T = ((float) (GPS_data[0][2] - prevGPS_data[0][2])) / 1000; //Time elapsed in seconds
float Amatrix[4][4] = {
{1, 0, delta_T, 0 },
{0, 1, 0, delta_T},
{0, 0, 1, 0 },
{0, 0, 0, 1 },
};
float nextXstateEstimate[4][1] = {
{0},
{0},
{0},
{0},
};
Matrix.Multiply((float*) Amatrix, (float*) Xstate, 4, 4, 1, (float*) nextXstateEstimate);
/*************Line 78 of Matlab code*************/
float PerrorCovarianceEstimate[4][4] = {
{0.0, 0.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0},
};
float AmatrixTranspose[4][4] = {
{0.0, 0.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0},
};
float IntermediateProductMatrix[4][4] = {
{0.0, 0.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0},
};
float IntermediateSumMatrix[4][4] = {
{0.0, 0.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0},
};
Matrix.Transpose((float*) Amatrix, 4 ,4, (float *) AmatrixTranspose);
Matrix.Multiply((float*) Amatrix, (float*) PerrorCovariance, 4,4,4, (float*) IntermediateProductMatrix);
Matrix.Multiply((float*) IntermediateProductMatrix, (float*) AmatrixTranspose, 4,4,4, (float*) IntermediateSumMatrix);
Matrix.Add((float*) IntermediateSumMatrix, (float*) QcovarianceMatrix, 4, 4, (float*) PerrorCovarianceEstimate);
/*************Line 81 of Matlab code*************/
float KalmanGain[4][2] = {
{0.0, 0.0},
{0.0, 0.0},
{0.0, 0.0},
{0.0, 0.0},
};
float IntermediateProductMatrix2[2][4] = {
{0.0, 0.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0},
};
float IntermediateProductMatrix3[2][2] = {
{0.0, 0.0},
{0.0, 0.0},
};
float IntermediateQuotientMatrix[4][2] = {
{0.0, 0.0},
{0.0, 0.0},
{0.0, 0.0},
{0.0, 0.0},
};
float IntermediateSumMatrix2[2][2] = {
{0.0, 0.0},
{0.0, 0.0},
};
Matrix.Multiply((float*) Hmatrix, (float*) PerrorCovarianceEstimate, 2,4,4, (float*) IntermediateProductMatrix2);
Matrix.Multiply((float*) IntermediateProductMatrix2, (float*) HmatrixTranspose, 2,4,2, (float*) IntermediateProductMatrix3);
Matrix.Add((float*) IntermediateProductMatrix3, (float*) RcovarianceMatrix, 2, 2, (float*) IntermediateSumMatrix2);
int flag = Matrix.Invert((float*) IntermediateSumMatrix2, 2); //The argument matrix gets inverted; no need to create temp matrix.
if (flag == 0){
Serial.println("CANT INVERT");
}
Matrix.Multiply((float*) PerrorCovarianceEstimate, (float*) HmatrixTranspose, 4,4,2, (float*) IntermediateQuotientMatrix);
Matrix.Multiply((float*) IntermediateQuotientMatrix, (float*) IntermediateSumMatrix2, 4,2,2, (float*) KalmanGain);
data[0] = (float) (GPS_data[0][0]);///10000000.0;//-firstGPS_data[0][0]/10000000.0);//10000000.0;//*pi/180*Rearth/10000000.0;
data[1] = (float) (GPS_data[0][1]);///10000000.0;//-firstGPS_data[0][1]/10000000.0);///10000000.0;//*pi/180*Rearth*cosLat/10000000.0;
Serial.print("\r\n i: "); Serial.print(i);
Serial.print("\r\n FirstLat: "); Serial.print((float)firstGPS_data[0][0]); Serial.print(" FirstLon: "); Serial.println((float)firstGPS_data[0][1]);
Serial.print("\r\n LstLat: "); Serial.print((float)GPS_data[0][0]); Serial.print(" LastLon: "); Serial.println((float)GPS_data[0][1]);
Serial.print("\r\n Lat: "); Serial.print(data[0]); Serial.print(" Lon: "); Serial.println(data[1]);
float ZkTranspose[2][1] = { //We only need the transposed version of this, so we do it right here.
{data[0]},
{data[1]},
};
float IntermediateProductMatrix4[2][1] = {
{0.0},
{0.0},
};
float IntermediateProductMatrix5[4][1] = {
{0.0},
{0.0},
{0.0},
{0.0},
};
float IntermediateSubtractionMatrix[2][1] = {
{0.0},
{0.0},
};
Matrix.Multiply((float*) Hmatrix, (float*) nextXstateEstimate, 2,4,1, (float*) IntermediateProductMatrix4);
Matrix.Subtract((float*) ZkTranspose, (float*) IntermediateProductMatrix4, 2, 1, (float*) IntermediateSubtractionMatrix);
Matrix.Multiply((float*) KalmanGain, (float*) IntermediateSubtractionMatrix, 4,2,1, (float*) IntermediateProductMatrix5); //Reuse intermediate matrix because it has appropriate dimensions
Matrix.Add((float*) nextXstateEstimate, (float*) IntermediateProductMatrix5, 4,1, (float*) Xstate); //NO NEED TO TRANSPOSE X STATE. WE DID THAT IN MATLAB FOR CONVENIENCE
/*************Line 84 of Matlab code*************/
Matrix.Multiply((float*) KalmanGain, (float*) Hmatrix, 4,2,4, (float*) IntermediateProductMatrix); //Reuse this intermediate matrix because it's 4x4 and we need 4x4
float IdentityMatrix[4][4] = {
{1,0,0,0},
{0,1,0,0},
{0,0,1,0},
{0,0,0,1},
};
Matrix.Subtract((float*) IdentityMatrix, (float*) IntermediateProductMatrix, 4,4, (float*) IntermediateQuotientMatrix); //Reuse this intermediate matrix because it's 4x4 and we need 4x4.
Matrix.Multiply((float*) IntermediateQuotientMatrix, (float*) PerrorCovarianceEstimate, 4,4,4, (float*) PerrorCovariance);
Matrix.Print((float*) Xstate, 4, 1, "Xstate Update: ");
Matrix.Print((float*) ZkTranspose, 2, 1, "ZK transpose: ");
Matrix.Print((float*) nextXstateEstimate, 4, 1, "Next X State Estimate: ");
//i = i + 1;
GPS_data[0][0] = Xstate[0][0];
GPS_data[0][1] = Xstate[1][0];
return GPS_data[0];
}
int64_t* KalmanFilter::KalmanNoData(){
uint64_t currentTime = (int32_t) millis();
delta_T = (float) (currentTime - time) / 1000; //Time elapsed in seconds
float Amatrix[4][4] = {
{1, 0, delta_T, 0 },
{0, 1, 0, delta_T},
{0, 0, 1, 0 },
{0, 0, 0, 1 },
};
float nextXstateEstimate[4][1] = {
{0},
{0},
{0},
{0},
};
Matrix.Multiply((float*) Amatrix, (float*) Xstate, 4, 4, 1, (float*) nextXstateEstimate);
Xstate[0][0] = nextXstateEstimate[0][0];
Xstate[1][0] = nextXstateEstimate[1][0];
Xstate[2][0] = nextXstateEstimate[2][0];
Xstate[3][0] = nextXstateEstimate[3][0];
/*************Line 78 of Matlab code*************/
float AmatrixTranspose[4][4] = {
{0.0, 0.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0},
};
float IntermediateProductMatrix[4][4] = {
{0.0, 0.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0},
};
float IntermediateSumMatrix[4][4] = {
{0.0, 0.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0},
};
float PerrorCovarianceEstimate[4][4] = {
{0.0, 0.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0},
};
Matrix.Transpose((float*) Amatrix, 4 ,4, (float *) AmatrixTranspose);
Matrix.Multiply((float*) Amatrix, (float*) PerrorCovariance, 4,4,4, (float*) IntermediateProductMatrix);
Matrix.Multiply((float*) IntermediateProductMatrix, (float*) AmatrixTranspose, 4,4,4, (float*) IntermediateSumMatrix);
Matrix.Add((float*) IntermediateSumMatrix, (float*) QcovarianceMatrix, 4, 4, (float*) PerrorCovarianceEstimate);
for (int j = 0; j < 4; j++){
for (int k = 0; k < 4; k++){
PerrorCovariance[j][k] = PerrorCovarianceEstimate[j][k];
}
}
GPS_data[0][0] = Xstate[0][0];
GPS_data[0][1] = Xstate[1][0];
time = millis();
return GPS_data[0];
}
float KalmanFilter::Smooth(int data, float filterVal, float smoothedVal){
if (filterVal > 1){ // check to make sure param's are within range
filterVal = .99;
}
else if (filterVal <= 0){
filterVal = 0;
}
smoothedVal = (data * (1 - filterVal)) + (smoothedVal * filterVal);
return smoothedVal;
}