In this project, you are going to implement the unscented Kalman filter in C++. You will be tracking the bicycle's position and velocity and predicting the positon of the object.
This project is based on the same structure as the extended Kalman filter. It uses a main file that calls a function called ProcessMeasurement. Anything important happens in this function. The function is part of the class ukf.
There is a simulator provided by Udacity (Term 2 Simulator Release) which can generate noisy LIDAR and RADAR measurements. And you will be using those measurements to predict your object position.
Here is the link to the orginal repository provided by Udaciy.

• cmake: 3.5
  • All OSes: click here for installation instructions
• make: 4.1 (Linux and Mac), 3.81 (Windows)
  • Linux: make is installed by default on most Linux distros
  • Mac: install Xcode command line tools to get make
  • Windows: Click here for installation instructions
• gcc/g++: 5.4
  • Linux: gcc / g++ is installed by default on most Linux distros
  • Mac: same deal as make - install Xcode command line tools
  • Windows: recommend using MinGW

1. Meet the Prerequisites/Dependencies
2. Intall uWebSocketIO on your system
   2.1 Windows Installation
   2.1.1 Use latest version of Ubuntu Bash 16.04 on Windows 10, here is the step-by-step guide for setting up the utility.
   2.1.2 (Optional) Check your version of Ubuntu Bash here.
3. Open Ubuntu Bash and clone the project repository
4. On the command line execute ./install-ubuntu.sh
5. Build and run your code.

• main.cpp:Reads in data, calls a function to run the Unscented Kalman filter, calls a function to calculate RMSE
• ukf.cpp: Initializes the Unscented Kalman filter, calls the predict and update function, defines the predict and update functions
• tools.cpp: Function to calculate RMSE
• README.md: Writeup for this project, including setup, running instructions and project rubric addressing.
• CMakeLists.txt: CMakeLists.txt file that will be used when compiling your code (you do not need to change this file)

• Clone this respository
• At the top level of the project repository, create a build directory: mkdir build && cd build
• In /build directory, compile yoru code with cmake .. && make
• Launch the simulator from Windows
• Execute the run command for the project ./UnscentedKF (Make sure you also run the simulator on the Windows host machine) If you see * * this message, it is working Listening to port 4567 Connected!!!

There are several criteria that must be fulfilled to pass the project.

• The overall processing chain (prediction, laser update or radar update depending on measurement type) must be correct.
• The student is not allowed to use values from the future to reason about the current state.
• It must be possible to run the project in three different modes: considering laser only, with considering radar only, or with using both sensors.
• For every mode, the overall RMSE (2d position only) may not be more than 10% increased to what the original solution is able to reach (this number depends on the individual measurement sequence)
• The RMSE of laser AND radar must be lower than radar only or laser only
• The NIS of radar measurements must be between 0.35 and 7.81 in at least 80% of all radar update steps.

• I recommend a hall of fame for the lowest overall RMSE using laser AND radar.
• I recommend to ask students to improve the initialization procedure and evaluate the RMSE during the first 20 steps.

Compiled successfully.

Meet.

My Kalman Filter implementation is completed at ukf.cpp

The first measurement is handled at ukf.cpp Line128-194.

My Kalman Filter predict function will be called at ukf.cpp Line195-207
My Kalman Filter update functions will be called based on different measurements after calling prediction function at ukf.cpp Line209-223.

My Kalman Filter update functions will be called based on different measurements after calling prediction function. For laser measurement, the update function implementation can be found at ukf.cpp Line401-555. For radar measurement, the update function implementation can be found at ukf.cpp Line557-710.

Yes.

Please (do your best to) stick to Google's C++ style guide.

Video recordings for success case.
Success case for running on Dataset 1.

Success case for running on Dataset 2.