Recursive Parameter Identification for Estimating and Displaying Maneuvering Vessel Path

Real-time recursive parameters identification is applied to surface vessel modeling for maneuvering path prediction. An end-to-end system is developed to situational vessel motion identify vessel parameters and estimate future path. Path prediction improves bridge team situational awareness by providing a real-time depiction of future notion over the groin on an electronic chart and display system (ECDIS). The extended least squares (ELS) parameter identification approach allows the system to be installed on most platforms without prior knowledge of system dynamics provided vessel states are available. The system continually times to actual environmental conditions including vessel ballasting current wind and sensor biases. In addition to path prediction the system estimates maximum vessel roll angle during maneuvering. Maximum roll prediction enhances carrier flight deck safety and increases operational effectiveness by redlining sea room retirements. Stateable performance is demonstrated in real-world maneuvering conditions to recommend that maneuvering path prediction be incorporated into the US Navy's AN/SSN-6 Navigation Sensor System Interlace (NAVSSI) electronic charting system. Future research should emphasize an underway demonstration with real-time data acquisition.Naval Postgraduate School Monterey CA is the author of 'Recursive Parameter Identification for Estimating and Displaying Maneuvering Vessel Path', published 2003 under ISBN 9781423513551 and ISBN 142351355X.