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carnd-extended-kalman-filter's Introduction

This project implements an extended Kalman filter in C++.

Input data consisting of laser measurements (given directly as x and y positions, with some known uncertainty) and radar measurements (given as radius, angle, and radial velocity relative to some fixed measurement site, with some known uncertainty) are combined with a motion model to track a vehicle with much better accuracy than the individual measurements alone allow.

The code presented here is designed to work with the Udacity term 2 simulation executable, and so cannot be run standalone. However, here's some example output.

Tracking car with EKF

Red circles are lidar measurements.

Blue circles are radar measurements (position markers inferred from radius and angle; the also-supplied radial velocity measurements are not shown).

Green markers are the car's position as estimated by the Kalman filter. It's clear that the Kalman filter does a good job of tracking the car's position with significantly reduced noise.

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carnd-extended-kalman-filter's Issues

missing rho_dot in intialization scope

in FusionEKF.cpp file

<

VectorXd x(4);
if( measurement_pack.sensor_type_ == MeasurementPackage::RADAR ) 
{
  // Convert radar from polar to cartesian coordinates and initialize state.
  float rho = measurement_pack.raw_measurements_[0];
  float phi = measurement_pack.raw_measurements_[1];

 x << rho*cos(phi), rho*sin(phi), 0.f, 0.f;

#>

why not git the value of rho_dot for RADAR to be
float rho_dot = measurement_pack.raw_measurements_[2];
then x << rhocos(phi), rhosin(phi), rho_dotcos(phi), rho_dotsin(phi);

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