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A nonlinear maneuver-tracking algorithm based on modified current statistical model

HUANG Wei-ping1,XU Yu2,WANG Jie3(1.Group of Graduate Management,Air Force Radar Academy,Wuhan Hubei 430019,China;2.Department for Scientific Research,Air Force Radar Academy,Wuhan Hubei 430019,China;3.Unit 95174 of People’s Liberation Army,Wuhan Hubei 430019,China)  
The ‘current’statistical model depends on both the marginal value of the target acceleration and the fre-quency of the maneuver;this leads to a poor performance in tracking targets of low maneuverability or higher maneuver-ability.To obtain an improved model,we introduce to the existing ‘current’statistical(CS) model an activate function with the trace of the residual error variance as parameter for modifying the error covariance between the acceleration and the frequency.This modified model is then combined with an unscented Kalman filter(UKF) to form the modified current statistic model for the nonlinear maneuver tracking algorithm.Simulation results indicate that the proposed algorithms not only keep equal performance level as the CS model in tracking general maneuvers,but provide excellent performance in tracking targets with low maneuverability;thus,extending the range of maneuverability in tracking targets.
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