Generalized technique for optimizing the parameters of tracking estimators of coordinates and motion parameters in air and ground situation monitoring systems

The paper presents the results of research and development of a methodology for optimizing parameters of tracking estimators for object coordinates and motion parameters. The methodology is based on a comprehensive approach to training dataset formation considering various types of object motion and application of specialized optimization algorithms. The developed algorithms implement a complete optimization cycle, including training dataset formation, data preprocessing, parameter optimization, and verification of obtained results. The results of practical application of the methodology for optimizing parameters of non-adaptive Kalman filter and Interacting Multiple Model (IMM) filter under various observation conditions and object motion patterns are demonstrated. Based on simulation modeling, it is shown that the application of the developed methodology significantly improves the accuracy of estimating coordinates and motion parameters compared to traditional approaches to parameter selection. Special attention is paid to studying the stability of obtained solutions to changes in observation conditions and object motion patterns. The obtained results are advisable to use in development and modernization of radar data tracking systems, air traffic control systems, air and ground situation monitoring complexes, as well as in other applications requiring high-precision estimation of object motion parameters under a priori uncertainty.