Development of a New Fuzzy Logic Based Algorithm for Tracking Targets
Radio-electronic means, including transmission, radio-localization, broadcasting and navigation equipment, allow the execution of various research missions and the management of combat forces. Determining target coordinates and guiding arms towards them, capturing and analysing enemy data, ensuring navigation of ships, aircraft and outer atmospheric equipment, sending orders, decisions, reports and other necessary information to the armed forces; these are just some of the possibilities of radio-electronic technologies. Fuzzy logic makes it possible to explain the laws of order, function and control of a system linguistically. It can also be found when dealing with complex and nonlinear systems that, as their complexity increases, there is a decrease in the importance of the information in explaining the system’s global behaviour. Even if such an approach can seem insufficient, it is often superior to a rigorous mathematical approach and less laborious. In favour of fuzzy set theory, the main point is to succeed in working with imprecise, ambiguous notions. This article demonstrates the superiority of a fuzzy tracking system in the conditions of uneven accelerations and sudden change of direction of the targets, as well as in the event of failure to observe the target during successive scans, over the traditional Kalman filter tracking system. To overcome the velocity uncertainty and decrease the measurement error in real-time radar processing, a cascading Kalman filtering algorithm was used. Cascade filters are extended Kalman operated gain filters that use fuzzy logic to detect targets under difficult tracking conditions using radar equipment. The key directions for further study are as follows: applying fuzzy logic under complicated conditions in a real target tracking programme. A classical alpha-beta method can be updated as an initial approach, in order to calculate alpha and beta coefficients by fuzzy logic. An automated structure can be developed based on the fuzzy gain filter provided in this article, which provides the tracking system with modified values for alpha and beta coefficients in real time.
Maria Simona Raboaca
National R&D Institute for Cryogenic and Isotopic Technologies, 240050 Rm. Valcea, Romania and Faculty of Electrical Engineering and Computer Science, “Stefan cel Mare” University of Suceava, 720229 Suceava, Romania and Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.
“Polytechnic” University of Bucharest, 060042 Bucharest, Romania.
National R&D Institute for Cryogenic and Isotopic Technologies, 240050 Rm. Valcea, Romania and “Polytechnic” University of Bucharest, 060042 Bucharest, Romania.
View Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/341