THE STUDY TO APPLY FUZZY WEIGHTED INPUT ESTIMATION FOR THE PREDICTION OF TARGET TRAJECTORY IN A FIRE CONTROL SYSTEM

2017 ◽  
Vol 41 (1) ◽  
pp. 1-21
Author(s):  
Yi-Wei Chen ◽  
Yung-Lung Lee ◽  
Yen-Bin Chen
Keyword(s):  
Kalman Filter ◽  
Control System ◽  
Three Dimensional ◽  
Fire Control ◽  
Input Estimation ◽  
Target State ◽  
Maneuvering Target ◽  
Target Trajectory ◽  
Precise Estimation ◽  
Miss Distance

The fuzzy weighted input estimation (FWIE) is proposed in this paper to solve the problem of noise disturbance and combined with the three-dimensional motion equation of target trajectory to construct the tracking rule of fire control system. FWIE can estimate effectively the input data of maneuvering target acceleration to obtain the precise target state and solve the problems from the traditional Kalman filter which cannot compute the precise estimation of target state because of the input information in the system. Simulation results show that FWIE can estimate the change of target state rapidly and precisely compared with the extended Kalman filter and the proposed tracking rule can improve the fire control system to figure out the target intercepting points with shorter miss distance.

Air-to-Air Attack Based Performance Analysis of Integrated Avionics Systems

1999 ◽  
Author(s):  
Yi Xu ◽  
Zhongliang Jing ◽  
Dekun Jin
Keyword(s):  
Kinematic Model ◽  
Three Dimensional ◽  
Game Problem ◽  
Fire Control ◽  
Maneuvering Target Tracking ◽  
Factors Affecting ◽  
Maneuvering Target ◽  
Kaiman Filter ◽  
Tracking Model ◽  
Manipulation Model

Abstract This paper proposes the advanced techniques used by Integrated Avionics System to track and attack the maneuvering target effectively, develops the parameter sensitivity analysis, and then point out the primary factors and the secondary factors affecting the attack effectiveness. First of all, mathematical models are provided, including the kinematic model, the airborne radar tracking model, the fire control model, the pilot-manipulation model and the fire logic. With the use of self-adaptive Kaiman filter, the algorithm for the maneuvering target tracking is suggested. By means of the forced singular perturbation, the suboptimal strategy for three-dimensional pursuit-evasion differential game problem is developed. Secondly, based on the models, the algorithm and the strategy above, the simulation flow chart is proposed, which is in accordance with the actual air-to-air attack. Thirdly, in the light of the relative sensibility function, the values of system parameter sensibility about several parameters are calculated. Fourthly, appropriate parameter selections for improving and updating the attack capability are suggested. Finally, the imaginary air-to-air scenario is simulated to analyze the performance of Integrated Avionics System. The simulation results show that the system can track the maneuvering target reliably and attack it accurately with the longer locking time, and the burden of numerical calculation is lightened considerably.

Letter to the Editor: Neural Solution to the Correction of Miss Distance in Gun Fire Control System

1997 ◽  
Vol 08 (05n06) ◽  
pp. 485-488
Author(s):  
J. G. Kim ◽  
H. Jeong ◽  
Y. W. Lee
Keyword(s):  
Control System ◽  
Minimum Mean Square Error ◽  
Multilayer Perceptrons ◽  
Mean Square ◽  
Fire Control ◽  
Backpropagation Algorithm ◽  
Neural Controller ◽  
And Performance ◽  
Miss Distance ◽  
Performance Results

Multilayer perceptrons trained with the backpropagation algorithm are tested in gun fire control system for error correction and are compared to optimal algorithms based on minimum mean square error.1,2 The structure of the proposed neural controller is described and performance results are shown.

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