Optimizing Trajectory Tracking Algorithms by Evolutionary Procedure on Real Data with Simulated Measurements

The problem of optimizing trajectory tracking algorithms is considered. Based on measurements of a moving object, such algorithms iteratively make estimates of its state. These algorithms contain parameters that affect the quality of their work, for example, the noise variances in a mathematical model of the object’s dynamics. A multicriteria evolutionary optimization algorithm for such parameters is proposed based on genetic procedures. We also elaborate a procedure for using this algorithm on real data in which random measurement errors are simulated along the real trajectory. The system of criteria is proposed that assesses both the total mean square deviation of the trajectory tracking algorithm’s output and the quality of its transition processes after a change of the object’s motion mode. The algorithm was tested on model and real air traffic data.