Description: The method of interception has been modified by filtration of the object's position vector.
Direct input of the object position.
The simulation shows the difference after modifying the method.
Precision dependent on the filtration method.
Current method:
- Calculation of target data using derivation of equations of motion
- Prediction of target data using Dead Reckoning
New method:
- Filtering target position vector
- Calculation of target data using derivation of equations of motion
- Prediction of target data using Dead Reckoning

Description: The method of interception has been modified by filtration of the object's acceleration vector.
Acceleration of the object calculated from a derivative.
The simulation shows the difference after modifying the method.
Precision dependent on the filtration method.
Current method:
- Calculation of target data using derivation of equations of motion
- Prediction of target data using Dead Reckoning
New method:
- Calculation of target data using derivation of equations of motion
- Filtering target acceleration vector
- Prediction of target data using Dead Reckoning

Description: A comparison of two methods that determine current target data such as position, speed and acceleration.
The observable effect of these methods on pursuers during a standard test.
First method:
- Direct input
Second method:
- Calculation of target data using derivation of equations of motion
- Prediction of target data using Dead Reckoning

Description: Angular version of the Cosine rule method.
The method calculates the optimal time and recommended direction of the acceleration axis to intercept the target direction.
This method calculates the optimal time using a 4th degree polynomial.
Near-optimal "angular intercept" case.
The allocation of the random orientation and angular velocity for all tested objects during initialization.
First test :
Interception of the destination vector by one of the axis of the object.
Quaternion form of RK2/RK4 integration used for modify orientation, angular velocity and angular acceleration of the tested objects.

Description: Orientation matching using Cosine Rule test.
Near-optimal "orientation matching" case.
The allocation of the random orientation and angular velocity for all tested objects during initialization.
First test :
Adjusting the direction of one of the object's axes to the direction of the target vector.
Second test :
The equalization of the orientation of the source object to the orientation of the target object.
Quaternion form of RK2/RK4 integration used for modify orientation, angular velocity and angular acceleration of the tested objects.

Description: Angular version of the LOS guidance.
Suboptimal "angular intercept" case.
The allocation of the random orientation and angular velocity for all tested objects during initialization.
First test :
Interception of the destination vector by one of the axis of the object.
Second test :
Interception of all axes of the target object by the axes of the source object.
Quaternion form of RK2/RK4 integration used for modify orientation, angular velocity and angular acceleration of the tested objects.

Description: Self-tuning Dahlin PID Controller test.
Multiple self-tuning controller tests using variable additive white gaussian noise on a sinusoidal signal.
Only for this test :
Set Point = Generated input signal
Process variable = Controller output
Signal generation : 60 Hz
K factor : 1.0-0.0
Three other factors : 0.1

Description: Optimized augmented proportional navigation test.
The method uses additional information about pursuer acceleration.
Near-optimal "interception" case.
Two moving pursuers and evader.
Acceleration of pursuers 1/3 greather than acceleartion
of evader.
The interception of the target takes place as quickly as possible.

Description: The method calculates the optimal time and recommended direction of the acceleration vector to the closest approach to the target.
The equation of this method can be obtained from the first derivative of another equation used for continuous collision detection.
This method can be considered as "higher-order" proportional navigation.
Near-optimal "closest approach" case.
Two moving pursuers and evader.
Acceleration of pursuers 1/3 greather than acceleartion
of evader.
The interception of the target takes place as quickly as possible.

Description: The method calculates the optimal time and recommended direction of the acceleration vector to intercept the target.
This method calculates the optimal time using a 4th degree polynomial.
Near-optimal "intercept" case.
Two moving pursuers and evader.
Acceleration of pursuers tripple greather than acceleartion
of evader.
The interception of the target takes place as quickly as possible.

Description: Kalman filter test.
Multiple filter tests using variable additive white gaussian noise on a sinusoidal signal.
6-level Kalman filter adapted to motion tracking, smoothing and prediction.
A defined 6x6 Transition matrix to achieve the next state (Taking into account Position-Velocity-Acceleration-Jerk-Jounce-Crackle).
The control vector and the input matrix were not used in this case.
The value of measurement noise depends on the variance.
Implementation with a reduced amount of matrix in order to:
-Eliminate redundant multiplications by 0 or 1
-Acceleration of the process of calculating optimal gains
-Observe one state (matrix inversion reduced to 1/value)
-Optimal gains are recalculated only when the value of key
variables changes.
Signal generation : 60 Hz
Process noise variable : 1.0-0.0

Description: The method calculates the optimal time and recommended direction of the acceleration vector to join the formation.
Near-optimal "joint the formation" case.
Two moving wingmen and leader.
Acceleration of wingmen tripple greather than acceleartion
of leader.
Formation is always created as soon as possible.

Description: An example of avoiding a possible collision in the future.
Custom initialization of test objects only for the purpose of this presentation (nonstandard case).
Reaction on detection of possible collision when the time to collision is less than the braking time.

Description: 2nd-degree continuous collision detection between the pursuer and the obstacle, analyzing their position, speed and acceleration.
Reaction to collision by changing the color of the obstacle.
Custom initialization of test objects only for the purpose of this presentation (nonstandard case).

Description: Pure pursuit mixed with LOS-guidance test (+- 300 second)
Suboptimal "intercept target" case.
Two moving pursuers against moving evader.
Acceleration of pursuers tripple greather than acceleartion
of evader.
Target intercepted by one pursuer in less than 50 second.
Four interceptions in 300 seconds.

Description: Memory Fading Polynomial fliter test.
4th degree filter test using variable additive white gaussian noise on a sinusoidal signal.
Precalculated suboptimal a-b-c-d gains.
Visible signal estimation at low noise levels.
Signal generation : 100 Hz
Theta factor : 1.0-0.0

Description: Modified seek steering behaviour test (+- 300 second)
Suboptimal "intercept target" case.
Two moving pursuers against moving evader.
Acceleration of pursuers tripple greather than acceleartion
of evader.
Target intercepted by two pursuers in less than 130 second

Description: Modified seek steering behaviour test (+- 300 second)
Suboptimal "joint the formation" case.
Two moving wingmen and leader.
Acceleration of wingmen tripple greather than acceleartion
of leader.
Formation created in less than 180 seconds.

Description: Alpha-Beta-Gamma filter test.
Multiple filter tests using variable additive white gaussian noise on a sinusoidal signal.
Calculated in real-time fast and approximate measurement noise and optimal a-b-g-gains.
Visible signal estimation at low noise levels.
Value of the a-b-g gains depends on time interval
and noise variance
Signal generation : 100 Hz
Process noise variable : 2.0-0.0

Description: Line of Sight Guidance test (+- 300 seconds)
Two moving pursuers against moving evader.
Acceleration of pursuers tripple greather than acceleartion
of evader.

Description: Adaptive Exponential Smoothing test.
Two filter tests using large and small additive white gaussian noise on a sinusoidal signal.
Signal generation : 50 Hz
Beta factor : 0.0-1.0

Description: Low-pass filter test.
Two filter tests using large and small additive white gaussian noise on a sinusoidal signal.
Value of an alpha factor depends on time interval.
Signal generation : 100 Hz
Filter cutoff frequency : 0-100 Hz

Description: Automatic Flight Controller Simulation - (C++/OpenGL)
Current features:
- Test and analysis of methods and algorithms used
by the automatic flight controller
- Three tested objects inside the test space
in the pursuer-evader mode (two pursuer and one evader)
- The tested objects have their own automatic flight controller
- Comparison of the methods used by the automatic flight
controller of the pursuers.
- Variable density and radius of the test space
- Test objects can not get beyond the boundaries
of the test space due to the use of additional forces
- Ability to use many methods and techniques of pursuit
and evasion in many modes
- Possibility of adding a random number of static
and dynamic obstacles in the test space
- Variable thrust value for the tested objects
- RK2/RK4 Integration used for modify position, velocity,
acceleration of the tested objects
- Variable virtual camera orientation and three points of view
(Global, Pursuer perspective, Evader perspective)
- Possibility to record and draw the path traveled by both
tested objects during the test
- Variable length and frequency of the recording path
traveled by the test objects
- Displaying the distance between the tested objects,
their speed, the number of collisions between them
and the number of collisions with random obstacles
- Visible position, velocity vector and acceleration vector of the
tested objects
- Optional saving of location data of objects to
an external file

Description: Simple Digital Signal Analyzer ( C++/OpenGL)
Current features:
-Real-time processing and rendering of a discrete time data
such as input signal or time series. (value by value)
-Rendering the original signal and processed signal on
the screen in the area of the moving window.
-Processing many types of input signals and time series
generated procedurally. (sine, rectangle)
-Real-time processing and resizing of loaded input signals
from external files.
-Ability to add noise to the procedural generated signal.
(standard additive white gaussian noise)
-Set of digital filters, estamers and controllers for
real time processing of input signal.
-Real-time noise percentage scale detector.
-Real-time signal to noise ratio detector.
-Calculation and display of current error, aggregate error
and percentage error between the original signal
and the processed signal.
-Ability to control the speed of signal reading
or signal generation.
-Adjustable range of moving window.
-Adjustable Scale of Additive Noise for procedural
generated signal.
-Variable frequency and amplitude for procedural
generated signal.
-Ability to save the processed input signal to a file.