扩展卡尔曼滤波项目C++代码

本资源提供了一个自动驾驶汽车程序启动扩展卡尔曼滤波项目C++代码。所谓扩展卡尔曼滤波器，就是适用于非线性系统的卡尔曼滤波器，可以更广泛的应用在项目中。

#include “FusionEKF.h“
#include 
#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;

  /**
   * TODO: Finish initializing the FusionEKF.
   * TODO: Set the process and measurement noises
   */


}

/**
 * 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.

    }
    else if （measurement_pack.sensor_type_ == MeasurementPackage::LASER） {
      // TODO: Initialize state.

    }

    // 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.
   */

  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

  } else {
    // TODO: Laser updates

  }

  // print the output
  cout << “x_ = “ << ekf_.x_ << endl;
  cout << “P_ = “ << ekf_.P_ << endl;
}

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