Abstract
Radio-electronic means, including equipment for transmissions, radio-location, broadcasting, and navigation, allow the execution of various research missions and combat forces management. Determining the target coordinates and directing the armament towards them, obtaining and processing data about enemies, ensuring the navigation of ships, planes and outer atmospheric means, transmitting orders, decisions, reports and other necessary information for the armed forces; these are only some of the possibilities of radio-electronic technology. Fuzzy logic allows the linguistic description of the laws of command, operation and control of a system. When working with complex and nonlinear systems, it can often be observed that, as their complexity increases, there is a decrease in the significance of the details in describing the global behavior of the system. Even though such an approach may seem inadequate, it is often superior and less laborious than a rigorous mathematical approach. The main argument in favor of fuzzy set theory is to excel in operating with imprecise, vague notions. This article demonstrates the superiority of a fuzzy tracking system over the standard Kalman filter tracking system under the conditions of uneven accelerations and sudden change of direction of the targets, as well as in the case of failure to observe the target during successive scans. A cascading Kalman filtering algorithm was used to solve the speed ambiguity and to reduce the measurement error in real-time radar processing. The cascade filters are extended Kalman filters with controlled gain using fuzzy logic for tracking targets using radar equipment under difficult tracking conditions.
Highlights
Tracking of targets represents the forecast of the possible trajectories of the target as a function of its previous positions
We propose a fuzzy gain filter which implements fuzzy logic for target tracking in difficult conditions and we will show its superiority against a traditional Kalman filter
Kalman filter tracking system were assessed taking into account the number of tracking losses of reliability and robustness of theand tracking system)ofand averagesystem) of the prediction
Summary
Tracking of targets represents the forecast of the possible trajectories of the target as a function of its previous positions. The values measured can enhance multiple variable features and may be constrained by different initial conditions depending on the flight angle [2] and on the position of the target towards the radar [3]. In [4], Zhou et al proposed an alternative to the standard Kalman filter for target tracking in the case when the radar has a faulty functioning with model mismatching. Their solution was to use an adaptive unscented Kalman filter in which the parameters (innovation vector, covariance matrix) can Mathematics 2020, 8, 207; doi:10.3390/math8020207 www.mdpi.com/journal/mathematics
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