Year: 2025
Type: conference-paper
Abstract: Currently, the development of discrete signal filtering methods that are used in computerized biometric identification systems is an urgent task. In the case of linear filtering, one of the most popular methods is the Kalman filter. A modified Kalman filtering method was proposed to improve the efficiency of digital filtering of a discrete signal. This method provides automation of parameter value determination and improves the speed and accuracy of Kalman filtering by using fewer parameters and identifying them based on immune metaheuristic methods. The proposed metaheuristic methods reduce the probability of convergence to a local extremum by using the Cauchy distribution and make parametric identification more accurate. Algorithms of immune metaheuristic methods for identifying Kalman filter parameters have been developed, which are designed for software implementation on the GPU using CUDA technology, which increases the accuracy of Kalman filtering. Further prospects of the study are to utilize the proposed immune metaheuristic methods for various general and special purpose intelligent systems.
Institution: Cherkasy State Technological University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7166707147