Extended Kalman Filter

---

Extended Kalman filter Project

The goals / steps of this project are the following:

• Implement extended Kalman filter to predict the position of an object from LIDAR and RADAR noisy measurements
• Test on simulator that the filter successfully predicts object trajectory
• Summarize the results with a written report

---

Files Submitted

The following files were changed during the implementation:

• tools.cpp contains RMSE and Jacobian matrix calculation
• kalman_filter.cpp contains functionality of predicting and updating tracking values
• FusionEKF.cpp containing a filter working pipeline
• Extended_Kalman_Filter.md summarizing the results

1. Project Basics

In this project the C++ was used to program. The RADAR and LIDAR noisy measurements were taken to track the position of an object. The code were tested using Udacities simulator.

3. Accuracy

According to the project rubric the px, py, vx, and vy RMSE should be less than or equal to the values [.11, .11, 0.52, 0.52].

*Dataset 1 RMSE [0.0973,0.0855,0.4513,0.4399] *Dataset 2 RMSE [0.0726,0.0965,0.4216,0.4932]