Object-Tracking-and-State-Prediction-with-Unscented-and-Extended-Kalman-Filters

Radar and Lidar Sensor Fusion using Extended, and Unscented Kalman Filter for Object Tracking and State Prediction.

Understanding the data

[L(for lidar)] [m_x] [m_y] [t] [r_x] [r_y] [r_vx] [r_vy]
[R(for radar)] [m_rho] [m_phi] [m_drho] [t] [r_px] [r_py] [r_vx] [r_vy]

Where:
(m_x, m_y) - measurements by the lidar
(m_rho, m_phi, m_dho) - measurements by the radar in polar coordinates
(t) - timestamp in unix/epoch time the measurements were taken
(r_x, r_y, r_vx, r_vy) - the real ground truth state of the system

Example:
R 8.60363 0.0290616 -2.99903  1477010443399637  8.6 0.25  -3.00029  0
L 8.45  0.25  1477010443349642  8.45  0.25  -3.00027  0 

Extended KF

Since here for fusing Radar and Lidar data, we need to have Extended KF for working with Radar data since the position is obtained by converting the polar co-ordinates using non-linear equations to (x,y) position.
Lidar data can be treated via simple Kalman Filter.

References:

Helper methods and input data file adapted from https://github.com/mithi/Fusion-EKF-Python