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.

Switching Multisensor Improved Current Statistical Model for Maneuvering Target Tracking

2013 ◽  
Vol 385-386 ◽  
pp. 585-588
Author(s):  
Qi Fang He ◽  
Yan Bin Li ◽  
Hang Lv ◽  
Guang Jun He
Keyword(s):  
Statistical Model ◽  
Target Tracking ◽  
Tracking System ◽  
Sensor Management ◽  
Maneuvering Target Tracking ◽  
System Parameters ◽  
Maneuvering Target ◽  
Tracking Model ◽  
Transient Error ◽  
Random Change

For an actual maneuvering target tracking system, the random change of system parameters and structure can often occur. So the whole system is dynamic, and there is uncertainty in system parameters and structure. In the paper, theory of system with random changing structures and technology of sensor management is introduced to build a tracking model with random changing structures. For the considering of random changing, the transient error brought by structure random changing is overcome.

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.

Maneuvering Target Tracking Algorithm Based on Improved "Self-Adaptive Turning Rate" Model

2013 ◽  
Vol 278-280 ◽  
pp. 1682-1686
Author(s):  
Xiu Ling He ◽  
Jing Song Yang
Keyword(s):  
Target Tracking ◽  
Rate Model ◽  
Simulation Test ◽  
Maneuvering Target Tracking ◽  
Maneuvering Target ◽  
Modeling Methods ◽  
Actual Application ◽  
Model Set ◽  
Tracking Model ◽  
Self Adaptive

Multi-model (interactive multi-model )tracking is an effective method for maneuvering target tracking, and turning model is widely studied and applied in multi-model tracking modeling and model set establishment. A detailed study is carried out on the self-adaptive turning model modeling method for maneuvering target tracking. Also a summary is made about the method of establishing tracking model by calculating the turning rate. The shortcomings of literature [4、6]'s self-adaptive turning model in actual application is also pointed out, with two modeling methods adopting average turning rate presented accordingly. The simulation test proves the necessity of improvement and the validity of the new model.

scholarly journals Using a New Circular Prediction Algorithm to Design an IMM Filter for Low Update Rate Radar System

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5035
Author(s):  
Yung-Lung Lee
Keyword(s):  
Linear Prediction ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Prediction Algorithm ◽  
Hybrid Filter ◽  
Maneuvering Target Tracking ◽  
Linear Filters ◽  
Maneuvering Target ◽  
Update Rate ◽  
Stable Estimation

For radar systems with low update rates; such as track-while-scan (TWS) systems using rotating phased array antennas; reducing the prediction error is a very important issue. A good interacting multiple models (IMM) hybrid filter combined with circular and linear filters that are defined in relation to three measurements has been proposed in the literature. However; the algorithm requires three previous measurements; and too much prior information will result in a reduced ability to predict the future position of a highly maneuvering target. A new circular prediction algorithm for maneuvering target tracking is proposed as a non-linear prediction filter in this paper. Based on this new predictor; we also proposed a new type of IMM filter that has good estimation performance for high maneuvering targets. The proposed hybrid filter is entirely defined in relation to two measurements in a three-dimensional space to obtain a better maneuver following capability than the three measurements hybrid filter. Two target profiles are included for a comparison of the performance of our proposed scheme with that of the conventional circular; linear and IMM filters. The simulation results show that under low update rates; the proposed filter has a faster and more stable estimation response than other filters

scholarly journals Adaptive Sample-Size Unscented Particle Filter with Partitioned Sampling for Three-Dimensional High-Maneuvering Target Tracking

2019 ◽  
Vol 9 (20) ◽  
pp. 4278 ◽  
Author(s):  
Qi Deng ◽  
Gang Chen ◽  
Huaxiang Lu
Keyword(s):  
Particle Filter ◽  
Sample Size ◽  
Target Tracking ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Maneuvering Target Tracking ◽  
Time Step ◽  
Maneuvering Target ◽  
Unscented Particle Filter ◽  
Partitioned Sampling

High-maneuvering target tracking is a focused application area in radar positioning and military defense systems, especially in three-dimensional space. However, using a traditional motion model and techniques expanded from general two-dimensional maneuvering target tracking may be inaccurate and impractical in some mission-critical systems. This paper proposes an adaptive sample-size unscented particle filter with partitioned sampling (PS-AUPF), which is used to track a three-dimensional, high-maneuvering target, combined with the CS-jerk model. In PS-AUPF, the partitioned sampling is introduced to improve the resampling and predicting process by decomposing motion space. At the same time, the adaptive sample size strategy is used to adjust the sample size adaptively in the tracking process, according to the initial parameters and the estimated state variance of each time step. Finally, the effectiveness of this method is validated by simulations, in which the sample size of each algorithm is set to the minimum required for the optimal accuracy, thus ensuring the reliability of the tracking results. The results have shown that the proposed PS-AUPF, with higher accuracy and lower computational complexity, performs better than other existing tracking methods in three-dimensional high-maneuvering target tracking scenarios.

Curve Model of Adaptive Interaction Model Algorithm Tracking Method

2015 ◽  
Vol 738-739 ◽  
pp. 344-349
Author(s):  
Xiu Ling He ◽  
Jing Song Yang
Keyword(s):  
Target Tracking ◽  
Estimation Method ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Adaptive Kalman Filter ◽  
Maneuvering Target Tracking ◽  
Maneuvering Target ◽  
Curve Model ◽  
Tracking Model ◽  
Model Algorithm

Firstly, through the principle analysis and simulation experiment, the maneuvering target tracking algorithm of curve model interacting multiple model tracking algorithm was given. Because the algorithm is simple structure and high cost efficiency, it becomes generally applicable algorithm for curve tracking model. But, the target mobility is very high in practice, Single target tracking model is no longer applicable curve tracking model. To improve the accuracy of tracking, the adaptive grid interacting multiple model (AGIMM) algorithm was given. The algorithm has two fatal weaknesses in the practical application. First, the process of maneuvering target tracking, when the model changes and gradient, the tracking precision is not high ;Second, because the changing model structure is very large model sets, the algorithm is complexity and system processing speed is very slow, which cannot be widely used. In order to improve the algorithm and its scope of application, The paper proposed the adaptive Kalman filter adaptive interacting multiple model algorithm (AKFAIMM).The algorithm introduced the parameter in the adaptive Kalman filter, and adjusted parameter in maneuvering target tracking, the parameter was adjusted Continuously in the curve motion model, it could greatly improve the tracking precision and the application of the model. Second, to improve the algorithm complexity. The paper improved on turning curve. The angular velocity estimation method replaced centripetal acceleration estimation method. The estimation method reduced the number of model set and reduced greatly of computation.

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