Parameter Identification via Kalman Filter on a model motor glider
- testEnvironment.slx: Test environment including model of the Vitesse and added sensors (Meyer-Brügel, Modifications by me)
- init_testEnvironment.m: initializes and opens test environment
- Init: contains scripts and data for initialization (Meyer-Brügel)
- main.m: runs filters (basic structure by Jategaonkar, Modifications by me)
- xdot.m: Equations of motion of the Vitesse
- obs.m: Output Equation
- ruku4.m: numerical integration method, Runge-Kutta 4
- ekf.m: algorithm of the EKF (Jategaonkar)
- ukf.m: algorithm of the UKF with noise estimation (Jategaonkar)
- ukf_mod.m: algorithm of the UKF with additive zero-mean noise (Jategaonkar)
- sysmatA.m: linearizes state matrix (for EKF) (Jategaonkar)
- sysmatC.m: linearizes output matrix (for EKF) (Jategaonkar)
- print_par_std.m: prints average estimated parameter, standard deviation and relative standard deviation (Jategaonkar, Modifications by me)
- plot_params.m: plot results of parameter estimation
- plot_states_outputs.m: plot results of state estimation\ output prediction together with measurements
- mDef_check.m: checks if dimensions are set correctly (Jategaonkar)
- mDef.m: Definition of model, flight data, initial values etc. (Jategaonkar, Modifications by me)
- read_logs.m: saves maneuver data for selected flight
- plot_maneuvers.m: plots all maneuver data
- find_std_dev_log.m: computes standard deviations and variances of measured output to use as inital value for optimization of rr and for sensor models in simulink
- optimize_filters.m: inlcudes optimization modes to find suitable maneuvers, system noise covariance Q, scaling parameter alpha and measurement noise cov. R
- logs
- contains log files
- testflight_maneuver_log_data
- contains generated maneuver data from read_logs.m