Satisfied With The Partial Estimation Algorithm

This paper deals with the bias filtering problem in the target tracking based on the state estimation theory and algorithm. In general, the estimation problem is to obtain the minimum variance constraint of the singal to be estimated. In order to obtain unbias estimation, the order of system model should match the mode of targets dynamics, and the statistic characteristics of the external disturbance signals should be given. However, as far as non-cooperative adversary aircrafts are concerned, the targets sometimes have complicated dynamics except in the period of cruise. In this situation, the dynamics of the targets will show some uncertainties and complex characteristics. High-order models were proposed in some literatures to describe the targets' movements, but this will increase the stochastic error of estimation and cause instability in tracking the relatively lower order dynamics modes. Taking the anti-aircraft weapons systems equiped with following tracking system. For example, the instability of state estimation will lead to instability of antiaircraft artillery tracking and the decreasment of shooting effectiveness. Thus, in engineering, people use as low order system models as possible to assume the target motion, such as CV model which is widely used in fire control systems. However, low order model will cause system error when tracking maneuvering target。Hence, it is an urgent problem to control both accuracy and stability of the estimation systems.Based on the theory of bias estimation in Satisfactory estimation and regression coefficients estimation, this paper proposes a bias estimation strategy to satisfy accuracy and stability simutaneously. This strategy is not to seek the unbias estimation, but to control stochastic error with certain system error, and can obtain the stability of the output data. The whole performance of the system and Requirements of utilization are satisfied based on the reliable tracking of targets.This paper brings the dynamic error coefficients into satisfactory bias estimation strategy. System error can be decreased and the steady of algorithm can be improved by treating the dynamic error coefficients. Finally, the Regional pole indicators, steady-state error variance indicators and dynamic error coefficient indicators are described by certain LMI or BMI. By iterative LMI approach algorithm, BMIs are solved to give satisfactory bias estimation strategy satisfying regional pole, steady-state error variance upper bound indicators simultaneously with the minimum value of dynamic error coefficients.