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FusionEKF.cpp
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FusionEKF.cpp
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#include "FusionEKF.h"
#include <iostream>
#include "Eigen/Dense"
#include "tools.h"
using Eigen::MatrixXd;
using Eigen::VectorXd;
using std::cout;
using std::endl;
using std::vector;
/**
* Constructor.
*/
FusionEKF::FusionEKF() {
is_initialized_ = false;
previous_timestamp_ = 0;
// initializing matrices
R_laser_ = MatrixXd(2, 2);
R_radar_ = MatrixXd(3, 3);
H_laser_ = MatrixXd(2, 4);
Hj_ = MatrixXd(3, 4);
//measurement covariance matrix - laser
R_laser_ << 0.0225, 0,
0, 0.0225;
//measurement covariance matrix - radar
R_radar_ << 0.09, 0, 0,
0, 0.0009, 0,
0, 0, 0.09;
//Measurement matrix - LIDAR
H_laser_ << 1, 0, 0, 0,
0, 1, 0, 0;
//Measurement matrix - RADAR
Hj_ << 1, 1, 0, 0,
1, 1, 0, 0,
1, 1, 1, 1;
/**
* TODO: Finish initializing the FusionEKF.
* TODO: Set the process and measurement noises
*/
// create a 4D state vector, we don't know yet the values of the x state
ekf_.x_ = VectorXd(4);
// state covariance matrix P
ekf_.P_ = MatrixXd(4, 4);
ekf_.P_ << 1, 0, 0, 0,
0, 1, 0, 0,
0, 0, 1000, 0,
0, 0, 0, 1000;
// the initial transition matrix F_
ekf_.F_ = MatrixXd(4, 4);
ekf_.F_ << 1, 0, 1, 0,
0, 1, 0, 1,
0, 0, 1, 0,
0, 0, 0, 1;
// set the acceleration noise components
noise_ax = 9;
noise_ay = 9;
}
/**
* Destructor.
*/
FusionEKF::~FusionEKF() {}
void FusionEKF::ProcessMeasurement(const MeasurementPackage &measurement_pack) {
/**
* Initialization
*/
if (!is_initialized_) {
/**
* TODO: Initialize the state ekf_.x_ with the first measurement.
* TODO: Create the covariance matrix.
* You'll need to convert radar from polar to cartesian coordinates.
*/
// first measurement
cout << "EKF: " << endl;
ekf_.x_ = VectorXd(4);
ekf_.x_ << 1, 1, 1, 1;
if (measurement_pack.sensor_type_ == MeasurementPackage::RADAR) {
// TODO: Convert radar from polar to cartesian coordinates
// and initialize state.
float ro = measurement_pack.raw_measurements_(0);
float phi = measurement_pack.raw_measurements_(1);
float roDot = measurement_pack.raw_measurements_(2);
ekf_.x_(0) = ro * cos(phi);
ekf_.x_(1) = ro * sin(phi);
ekf_.x_(2) = roDot * cos(phi);
ekf_.x_(3) = roDot * sin(phi);
}
else if (measurement_pack.sensor_type_ == MeasurementPackage::LASER) {
// TODO: Initialize state.
// set the state with the initial location and zero velocity
ekf_.x_ << measurement_pack.raw_measurements_[0],
measurement_pack.raw_measurements_[1],
0,
0;
}
previous_timestamp_ = measurement_pack.timestamp_;
// done initializing, no need to predict or update
is_initialized_ = true;
return;
}
/**
* Prediction
*/
/**
* TODO: Update the state transition matrix F according to the new elapsed time.
* Time is measured in seconds.
* TODO: Update the process noise covariance matrix.
* Use noise_ax = 9 and noise_ay = 9 for your Q matrix.
*/
// compute the time elapsed between the current and previous measurements
// dt - expressed in seconds
float dt = (measurement_pack.timestamp_ - previous_timestamp_) / 1000000.0;
previous_timestamp_ = measurement_pack.timestamp_;
// TODO: YOUR CODE HERE
float dt_2 = dt * dt;
float dt_3 = dt_2 * dt;
float dt_4 = dt_3 * dt;
// Modify the F matrix so that the time is integrated
ekf_.F_(0, 2) = dt;
ekf_.F_(1, 3) = dt;
// set the process covariance matrix Q
ekf_.Q_ = MatrixXd(4, 4);
ekf_.Q_ << dt_4 / 4 * noise_ax, 0, dt_3 / 2 * noise_ax, 0,
0, dt_4 / 4 * noise_ay, 0, dt_3 / 2 * noise_ay,
dt_3 / 2 * noise_ax, 0, dt_2*noise_ax, 0,
0, dt_3 / 2 * noise_ay, 0, dt_2*noise_ay;
ekf_.Predict();
/**
* Update
*/
/**
* TODO:
* - Use the sensor type to perform the update step.
* - Update the state and covariance matrices.
*/
if (measurement_pack.sensor_type_ == MeasurementPackage::RADAR) {
// TODO: Radar updates
// measurement update
Tools tools;
Hj_ = tools.CalculateJacobian(ekf_.x_);
ekf_.H_ = Hj_;
ekf_.R_ = R_radar_;
ekf_.UpdateEKF(measurement_pack.raw_measurements_);
}
else {
// TODO: Laser updates
// measurement update
ekf_.H_ = H_laser_;
ekf_.R_ = R_laser_;
ekf_.Update(measurement_pack.raw_measurements_);
}
// print the output
std::cout << "x_ = " << ekf_.x_ << std::endl;
std::cout << "P_ = " << ekf_.P_ << std::endl;
}