Recursive parameter identification for estimating and displaying maneuvering vessel path

Abstract

Real-time recursive parameter identification is applied to surface vessel modeling for maneuvering path prediction. An end-to end system is developed to simulate vessel motion, identify vessel parameters and estimate future path. Path prediction improves bridge team situational awareness by providing a real-time depiction of future motion over the ground 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 tunes 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 reducing sea room requirements. Suitable 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 Interface (NAVSSI) electronic charting system. Future research should emphasize an underway demonstration with real-time data acquisition.