This paper addresses the problem of ship motion estimation using live data from Automatic Identification Systems (AIS). A globally exponentially stable observer for visualization and motion prediction of ships has been designed. Instead of using the extended Kalman filter (EKF) to deal with the kinematic nonlinearities the eXogenous Kalman Filter (XKF) is applied and by this global stability properties are proven.The proposed observer was validated using live AIS data from the Trondheim harbor in Norway and it was demonstrated that the observer tracks ships in real time. It was also demonstrated that the observer can predict the future motion of ships.The motion prediction capabilities are very useful for decision-support systems since this can be used to improve situational awareness e.g. for manned and unmanned autonomous ships that operate in common waters.

This paper addresses the problem of ship motion estimation using live data from Automatic Identification Systems (AIS) and extended Kalman filter (EKF) design. AIS data are transmitted from ships globally and a very-high frequency (VHF) AIS receiver picks up the signals as coded ASCII characters in a format specified by the National Marine Electronics Association (NMEA). Hence, the AIS sentences must be decoded using a parser to obtain real-time ship position, course and speed measurements. The state estimates are intended for collision detection and real-time visualization, which are important features of modern decision-support systems.The EKF is validated using live AIS data from the Trondheim harbor in Norway and it is demonstrated that the estimator can track ships in real time. It is also demonstrated that the EKF can predict the future motion of ships and different evasive maneuvers were analyzed in a collision avoidance scenario.

Small USVs are usually equipped with a low-cost navigation sensor suite consisting of a global navigation satellite system (GNSS) receiver and a magnetic compass. Unfortunately, the magnetic compass is highly susceptible to electromagnetic disturbances. Hence, it should not be used in safety-critical autopilot systems. A gyrocompass, however, is highly reliable, but it is too expensive for most USV systems. It is tempting to compute the heading angle by using two GNSS antennas on the same receiver. Unfortunately, for small USV systems, the distance between the antennas is very small, requiring that an RTK GNSS receiver is used. The drawback of the RTK solution is that it suffers from dropouts due to ionospheric disturbances, multipath, interference, etc. For safety-critical applications, a more robust approach is to estimate the course angle to avoid using the heading angle during path following. The main result of this article is a five-state extended Kalman filter (EKF) aided by GNSS latitude-longitude measurements for estimation of the course over ground (COG), speed over ground (SOG), and course rate. These are the primary signals needed to implement a course autopilot system onboard a USV. The proposed algorithm is computationally efficient and easy to implement since only four EKF covariance parameters must be specified. The parameters need to be calibrated for different GNSS receivers and vehicle types, but they are not sensitive to the working conditions. Another advantage of the EKF is that the autopilot does not need to use the COG and SOG measurements from the GNSS receiver, which have varying quality and reliability. It is also straightforward to add complementary sensors such as a Doppler Velocity Log (DVL) to the EKF to improve the performance further. Finally, the performance of the five-state EKF is demonstrated by experimental testing of two commercial USV systems.

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