Aircraft Trajectory Tracking Using Radar Equipment with Fuzzy Logic Algorithm

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.

[1]  Gang Wang,et al.  Two Algorithms for the Detection and Tracking of Moving Vehicle Targets in Aerial Infrared Image Sequences , 2015, Remote. Sens..

[2]  Kazuo Tanaka,et al.  Robust stabilization of a class of uncertain nonlinear systems via fuzzy control: quadratic stabilizability, H∞ control theory, and linear matrix inequalities , 1996, IEEE Trans. Fuzzy Syst..

[3]  Fabian de Ponte Muller Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles. , 2017 .

[4]  Nicu Bizon,et al.  Performance analysis of the tracking of the global extreme on multimodal patterns using the Asymptotic Perturbed Extremum Seeking Control scheme , 2017 .

[5]  Nicu Bizon,et al.  Global maximum power point tracking based on new extremum seeking control scheme , 2016 .

[6]  Jonas Aust,et al.  Bowtie Methodology for Risk Analysis of Visual Borescope Inspection during Aircraft Engine Maintenance , 2019 .

[7]  Christian Heite,et al.  Analog CMOS realization of fuzzy logic membership functions , 1993 .

[8]  Joris De Schutter,et al.  Kalman ® ltering for radar 2 D tracking q , 2000 .

[9]  Gang Feng,et al.  Analysis and design of fuzzy control systems using dynamic fuzzy global models , 1995, Fuzzy Sets Syst..

[10]  Juan Luis Castro,et al.  Fuzzy logic controllers are universal approximators , 1995, IEEE Trans. Syst. Man Cybern..

[11]  Faisal Jamil,et al.  Improving Accuracy of the Alpha–Beta Filter Algorithm Using an ANN-Based Learning Mechanism in Indoor Navigation System , 2019, Sensors.

[12]  Karim Abed-Meraim,et al.  An improved fuzzy alpha-beta filter for tracking a highly maneuvering target , 2016 .

[13]  Takayuki Osugi,et al.  Onset of background dynamic noise attenuates preview benefit in inefficient visual search , 2015, Vision Research.

[14]  Henrik Zsiborács,et al.  Changes of Photovoltaic Performance as a Function of Positioning Relative to the Focus Points of a Concentrator PV Module: Case Study , 2019, Applied Sciences.

[15]  Zong-xiang Liu,et al.  Fuzzy logic approach to visual multi-object tracking , 2018, Neurocomputing.

[16]  Baoqing Li,et al.  A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance , 2018, Sensors.

[17]  Yang Li,et al.  Three-Dimensional Impact Time and Angle Control Guidance Based on MPSP , 2019, International Journal of Aerospace Engineering.

[18]  Huajun Liu,et al.  Maneuvering Target Tracking Using Simultaneous Optimization and Feedback Learning Algorithm Based on Elman Neural Network , 2019, Sensors.

[19]  Ali Karimpour,et al.  An interacting Fuzzy-Fading-Memory-based Augmented Kalman Filtering method for maneuvering target tracking , 2013, Digit. Signal Process..

[20]  Gang Feng,et al.  Analysis and design for a class of complex control systems Part I: Fuzzy modelling and identification , 1997, Autom..

[21]  Masao Masugi,et al.  Performance Analysis and Design Strategy for a Second-Order, Fixed-Gain, Position-Velocity-Measured (α-β-η-θ) Tracking Filter , 2017 .

[22]  O. Landolt,et al.  Efficient analog CMOS implementation of fuzzy rules by direct synthesis of multidimensional fuzzy subspaces , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[23]  Tor Arne Johansen,et al.  Fuzzy model based control: stability, robustness, and performance issues , 1994, IEEE Trans. Fuzzy Syst..

[24]  Hui Zhao,et al.  Adaptive Unscented Kalman Filter for Target Tracking in the Presence of Nonlinear Systems Involving Model Mismatches , 2017, Remote. Sens..

[25]  Wenhao Bi,et al.  A novel strong tracking cubature Kalman filter and its application in maneuvering target tracking , 2019, Chinese Journal of Aeronautics.

[26]  Phatiphat Thounthong,et al.  Designing and modelling of the asymptotic perturbed extremum seeking control scheme for tracking the global extreme , 2017 .

[27]  Takanori Shibata,et al.  Correct Stability Condition and Fundamental Performance Analysis of the α-β-γ-δ Filter , 2018, Applied Sciences.

[28]  Kevin M. Passino,et al.  A fuzzy dynamic model based state estimator , 2001, Fuzzy Sets Syst..

[29]  Maria Lindén,et al.  Signal Quality Improvement Algorithms for MEMS Gyroscope-Based Human Motion Analysis Systems: A Systematic Review , 2018, Sensors.

[30]  Gang Feng,et al.  H∞ control of nonlinear continuous-time systems based on dynamical fuzzy models , 1996, Int. J. Syst. Sci..