In this project, I utilized an extended kalman filter to estimate the state of a moving object of interest with noisy lidar and radar measurements. I was able to obtain RMSE values that are lower than the outlined tolerance of [.11, .11, 0.52, 0.52].
The programs consists of the following files are:
src/FusionEKF.cpp- This code processes the measurement derived from the sensors.src/FusionEKF.hsrc/kalman_filter.cpp- This code contains the code which does the measurement and prediction steps for the Kalman Filtersrc/kalman_filter.hsrc/tools.cpp- This code contains functions to calculate the RMSE and the Jacobian matrixsrc/tools.hsrc/measurement_package.h- This code stores the measurements and the type of sensor.
The file src\main.cpp comes directly from the project source code and have been used without any changes.
These files work with the Term 2 simulator which can be downloaded here
These were created in accordance to the project rubric