extended_kalman_filter_python

An Extended Kalman Filter (that uses a constant velocity model) in Python. This EKF fuses LIDAR and RADAR sensor readings to estimate location (x,y) and velocity (vx, vy).

Source layout

1. main.py - Can run the tracker.
2. tracker.py - Instance that tracks and uses EKF to predict and update state.
3. efk.py - EKF implementation lives here.
4. utils.py - Helper methods.

Package requirements

1. pandas
2. numpy

Data log input file format

#L(for laser) meas_px meas_py timestamp gt_px gt_py gt_vx gt_vy
#R(for radar) meas_rho meas_phi meas_rho_dot timestamp gt_px gt_py gt_vx gt_vy

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

To run the filter

python main.py

Started Tracker for sample-laser-radar-measurement-data-1.txt
estimations count:  1224
measurements count:  1224
RMSE:  [[ 0.06516487  0.06053792  0.53321165  0.5441927 ]]
Metric:  [0.08, 0.08, 0.6, 0.6]
RMSE PASSED metric


Started Tracker for sample-laser-radar-measurement-data-2.txt
estimations count:  200
measurements count:  200
RMSE:  [[ 0.18549633  0.19030227  0.47675529  0.80446808]]
Metric:  [0.2, 0.2, 0.5, 0.85]