timout/carnd-unscented-kalman-filter

Unscented Kalman Filter

Udacity CarND Term 2, Project 2 - Unscented Kalman Filters

Project Basics

In this project, I used C++ to write a program taking in radar and lidar data to track position using Unscented Kalman Filters, a more advanced and more accurate method than in my previous Extended Kalman Filter project.

The code will make a prediction based on the sensor measurement and then update the expected position. See files in the 'src' folder for the primary C++ files making up this project.

Build instructions

1. Clone this repo.
2. Make a build directory: mkdir build && cd build
3. Compile: cmake .. && make
4. Run it: ./UnscentedKF along with the Term 2 simulator.

Results

Based on the provided data set, the Unscented Kalman Filter produced the below results.

• px - x-position
• py - y-position
• vx - velocity in the x-direction
• vy - velocity in the y-direction
• MSE - Residual error, was calculated by mean squared error (MSE).

Both Radar and Lidar

Input	MSE
px	0.0691
py	0.0803
vx	0.1719
vy	0.2115

Radar only

Input	MSE
px	0.2785
py	0.3176
vx	0.6686
vy	0.6213

Lidar only

Input	MSE
px	0.1627
py	0.1466
vx	0.2561
vy	0.3180