Synthesis of an Algorithm for Estimating Parameters of a Multiposition Surveillance System and Research of Filtering Divergence

The accuracy of aircraft position measurements directly affects flight safety and is one of the most important tactical characteristics. The introduction of new advanced surveillance tools, such as multi-position surveillance systems (MPSS), can significantly increase the level of flight safety, as well as improve the efficiency of airspace use. The authors consider the task of improving the quality of MPSS functioning and increasing the accuracy of estimating the aircraft coordinates. The accuracy of position-fixing is determined by the error in measuring the time of signal arrival under the influence of noise and interference. Random disturbances must be taken into account to ensure high-quality MPSS operation. This is achieved by applying the methods of Kalman filtration theory. Therefore, to solve the problem of estimating the MPSS state variables, it is proposed to use a Kalman filter (KF). The effectiveness of using the Kalman filter depends on the adequacy of mathematical models and real processes. Model inaccuracies associated with the functioning of navigation systems lead to KF divergence. The paper presents the results of theoretical studies and simulating the MPSS functioning processes based on the implementation of the KF algorithm. 

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