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Real-time identification for ground vehicle trajectory estimation using extended Kalman filter residual analysis

This thesis describes real-time model identification for ground vehicle trajectory estimation by means of the Automatic Dynamic Trajectory Recognition System. A theoretical approach to trajectory model identification for the ADTR system using Kalman filter residual analysis is developed. This approach selects the best trajectory model from an array of candidates by comparing residual vectors generated by a bank of Kalman filter estimators built upon the candidate models. The filter with the lowest RMS residual magnitude value is identified as containing the best trajectory model. The system is implemented in Draper Laboratory's C-Sim simulation environment. The system simulation contains a sensor model as well as a dynamic model of the ground vehicle. The simulation results demonstrate a correlation between the RMS value of the filter residual magnitude and the RMS magnitude of the ground vehicle position prediction error of extrapolating four seconds into the future. The results support the model identification approach. Thesis Advisor: Derek Rowell Professor of Mechanical Engineering Thesis Supervisor: Edward J. Lanzilotta Charles Stark Draper Laboratory