Paweł Banyś

Comprehensive Assessment of Automatic Identification System (AIS) Data Application to Anti-collision Manoeuvring

The use of radar information for collision avoidance is common, however it is effective only for constant values of ship motion parameters. As information delays or information errors occur, it is reasonable to supplement the information derived fromradar with another information system. An ideal systemshould operate automatically and continuously.Asystem that appears to be suitable to provide this kind of information is theAutomatic Identification System (AIS),whichmay be classified as a radio communication systemthat uses radiowaves to transmit datawith regard to ship motion parameters. In this paper the topic of integrity and completeness of AIS information is discussed and the research results for the completeness and integrity of dynamic information are presented. In addition, the outcomes ofAIS information correctness fromthe Gulf of Gdansk were compared with studies carried out in the Baltic Sea, east of Bornholm, between Trelleborg and Arkona. The results of research for AIS dynamic information with the highest completeness (Position, Course over Ground and Speed over Ground) are presented. The research outcomes presented in the paper lead to the conclusion that AIS could deliver useful supplementary information in the process of collision avoidance.

ASSESSMENT OF AIS VESSEL POSITION REPORT UNDER THE ASPECT OF DATA RELIABILITY

Abstract Since its introduction the Automatic Identification System (AIS) has played an important part in improving safety at sea, making bridge watchkeeping duties more comfortable and enhancing vessel traffic management ashore. However the analysis of a AIS data set describing the vessel traffic of the Baltic Sea came to conclusion, that specific parameters with relevance to navigation seemed to be defective or implausible. Essentially, it concerned the true heading (THDG) and the rate of turn (ROT) parameters. With the paper we are trying to clarify, which parameters of the AIS position report and to what extent, are affected. The detailed data analysis gives answers on how reliable the AIS data in different traffic areas is.

Improving the Maritime Traffic Situation Assessment for a Single Target in a Multisensor Environment

The key to maritime surveillance is an accurate and real-time update of the current traffic situation. Although the Automatic Identification System (AIS) has greatly improved the traffic situation assessment over the past years, it has shown to be error-prone and vulnerable to spoofing or intentional misuse. To obtain a more reliable picture of a vessel’s motion this work proposes the fusion of AIS and Radar data in a loosely coupled architecture. For that reason, an Interacting Multiple Model (IMM) Multi Sensor Probabilistic Data Association (PDA) filter was designed not only to fuse measurements from different sources, but also to best capture the different dynamic states of a single vessel. In the IMM two Unscented Kalman Filters (UKFs) run in parallel and interact with each other, each implementing a different dynamic model, the CV and CTRV, respectively. Each UKF is conditioned on asynchronous measurements obtained from either AIS or Radar. In case of the latter, measurements of a target candidate may also originate from clutter, not from the target itself. The PDA filtering approach not only associates these measurements to the actual target state but also acts as innovation gate for faulty AIS position updates. A standard blob detection algorithm was implemented and tuned for extracting target candidates in range and bearing from Radar images. To show the benefits of the proposed scheme a dedicated measurement campaign was performed in the Baltic Sea with a stationary ship monitoring a highly manoeuvring target vessel. With the proposed approach not only short term AIS induced errors can be detected and rejected but what may also become observable is suspicious AIS behaviour including anomalous position data or muted transponders.

Validation of Radar Image Tracking Algorithms withSimulated Data

Collision avoidance is one of the high‐level safety objectives and requires a complete and reliable description of the maritime traffic situation. The radar is specified by the IMO as the primary sensor for collision avoidance. In this paper we study the performance of multi‐target tracking based on radar imagery to refine the maritime traffic situation awareness. In order to achieve this we simulate synthetic radar images and evaluate the tracking performance of different Bayesian multi‐target trackers (MTTs), such as particle and JPDA filters. For the simulated tracks, the target state estimates in position, speed and course over ground will be compared to the reference data. The performance of the MTTs will be assessed via the OSPA metric by comparing the estimated multi‐object state vector to the reference. This approach allows a fair performance analysis of different tracking algorithms based on radar images for a simulated maritime scenario.

Motion-based consistency audit of onboard Global Navigation Satellite System reference as reported by static Automatic Identification System data

The Automatic Identification System (AIS) is widely used for reporting vessel movements and broadcasting additional information related to the current voyage or constant parameters like the IMO number or the overall dimension of the hull. Since dynamic AIS data is shared mostly without human interaction, and is not flawless, the static AIS content edited manually is vulnerable to human error. This work introduces a simple vessel motion pattern approach that determines the probable foredeck/afterdeck location of the GNSS reference used by the AIS transponder, and compares it to the hull parameters obtained from the static AIS data, to find observable errors in the static AIS configuration of the mount point of the GNSS reference antenna.

Comparison Of AIS-Based Prediction Of The Distance At The CPA With Factual Separation Between Vessels

Abstract Since its deployment in 2004, the Automatic Identification System (AIS) has been considered a significant improvement of watchkeeping duties at sea. According to current regulations, AIS has not been recognised as an approved anticollision instrument yet. However, it would be difficult to rule out a possibility that AIS, being an essential part of the onboard SOLAS — compliant configuration, is unaidedly used for collision avoidance tasks. Recent research activities of DLRs Department of Nautical Systems have shown that AIS transmissions may contain a lot of incomplete data and the system does not have any dependable information on its data integrity. For that reason, the computation of the closest point of approach (CPA) and the time to the CPA (TCPA) are analysed based on AIS data involving multiple vessels, in order to compare the predictions with factual approaches between vessels and to evaluate the usability of AIS data, in its present form, for the appraisal of the traffic situation around each vessel.