Start Date
24-4-2025 12:00 AM
Description
In this project, we offer application to our previously constructed information filter. The information filter is an algorithm used to estimate the information of a process corrupted in some way. The information filter is mathematically similar to the Kalman filter, widely used in navigation. Unlike the Kalman filter, the information filter propagates backwards in time and is more effective in smoothing. Here, our corrupted system is in terms of conformable derivative introduced by Khalil et al. in 2014. This time-weighted derivative shares many of the same properties as the classical derivative but lacks the usual semigroup property associated with the exponential. Here we offer two models for larger systems. The first model represents an aircraft in midflight tracked by radar. The second model tracks selected economic indicators over 2015-2024.
Recommended Citation
Hungerford, Sophia; Smith, Joseph E.; and Wintz, Nick, "Applications for the Conformable Information Filter" (2025). 2025 Student Academic Showcase. 25.
https://digitalcommons.lindenwood.edu/src_2025/Posters/Posters/25
Included in
Applications for the Conformable Information Filter
In this project, we offer application to our previously constructed information filter. The information filter is an algorithm used to estimate the information of a process corrupted in some way. The information filter is mathematically similar to the Kalman filter, widely used in navigation. Unlike the Kalman filter, the information filter propagates backwards in time and is more effective in smoothing. Here, our corrupted system is in terms of conformable derivative introduced by Khalil et al. in 2014. This time-weighted derivative shares many of the same properties as the classical derivative but lacks the usual semigroup property associated with the exponential. Here we offer two models for larger systems. The first model represents an aircraft in midflight tracked by radar. The second model tracks selected economic indicators over 2015-2024.