04. File Structure

Overview of a Kalman Filter: Initialize, Predict, Update

To review what we learned in the extended Kalman filter lectures, let's discuss the three main steps for programming a Kalman filter:

  • initializing Kalman filter variables
  • predicting where our object is going to be after a time step \Delta{t}
  • updating where our object is based on sensor measurements

Then the prediction and update steps repeat themselves in a loop.

To measure how well our Kalman filter performs, we will then calculate root mean squared error comparing the Kalman filter results with the provided ground truth.

These three steps (initialize, predict, update) plus calculating RMSE encapsulate the entire extended Kalman filter project.

Files in the Github src Folder

The files you need to work with are in the src folder of the github repository.

  • main.cpp - communicates with the Term 2 Simulator receiving data measurements, calls a function to run the Kalman filter, calls a function to calculate RMSE
  • FusionEKF.cpp - initializes the filter, calls the predict function, calls the update function
  • kalman_filter.cpp - defines the predict function, the update function for lidar, and the update function for radar
  • tools.cpp - function to calculate RMSE and the Jacobian matrix

The only files you need to modify are FusionEKF.cpp , kalman_filter.cpp , and tools.cpp .

How the Files Relate to Each Other

Here is a brief overview of what happens when you run the code files:

  1. Main.cpp reads in the data and sends a sensor measurement to FusionEKF.cpp
  2. FusionEKF.cpp takes the sensor data and initializes variables and updates variables. The Kalman filter equations are not in this file. FusionEKF.cpp has a variable called ekf_ , which is an instance of a KalmanFilter class. The ekf_ will hold the matrix and vector values. You will also use the ekf_ instance to call the predict and update equations.
  3. The KalmanFilter class is defined in kalman_filter.cpp and kalman_filter.h . You will only need to modify 'kalman_filter.cpp', which contains functions for the prediction and update steps.

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