Instruments and Systems: Monitoring, Control, and Diagnostics Annotation << Back Adaptive Processing of Nonlinear Trajectory Data of Maneuvering Objects under Systematic Distortions of Radar Measurements I.F. NAZIROV, G.V. GORBACHEV, E.V. KURNASOV This work is devoted to the search for the most effective methods for reconstructing and processing trajectory data of highly dynamic objects. The objective of this work is to develop and programmatically verify a suite of algorithms robust against impulse noise, systematic biases, and hardware noise inherent in multi-position radar systems. The proposed approach is based on three stages: preliminary synchronization of asynchronous data using cubic spline approximation; nonlinear filtering based on a robust median operator coupled with recursive Kalman smoothing; and the application of hierarchical agglomerative clustering with the Hausdorff metric to identify kinematic patterns. Experimental results, verified using the statistical Wald test, confirm the significant superiority of the proposed architecture over classical linear smoothing methods. The findings can be utilized to improve the accuracy of autonomous navigation and automated target tracking. Keywords: radar tracking; cubic spline; Kalman filter; Wald’s test; agglomerative clustering; median filtering. DOI: 10.25791/pribor.10.2026.1708 Pp. 01-09.