Automatic Identification System Transponders Research Articles

Procjena moguće pogrešne identifi kacije AIS transpondera unutar AIS podataka zbog inverzije MMSI bitova

For almost two decades now, the automatic identification system (AIS) has contributed to increasing the safety of navigation at sea. In addition to that, the system can provide valuable data for surveillance of maritime traffic on a global scale. For reliable tracking the movement of vessels with AIS, it is important to correctly identify the craft. Nowadays, such identification is based entirely on the maritime mobile service identity (MMSI). Despite extensive research being done on the subject of anomalies detectable within the AIS, with emphasis on spotting and correcting erratic vessel movements, a possibility of accidental falsification of MMSI leading to incorrect track reconstruction of a vessel has so far been neglected. This study focuses on analysing hypothetical situations when the MMSI of an AIS transponder on board a vessel is altered within the transponder software and a valid AIS data message is sent with a false MMSI which accidentally belongs to a completely different vessel. Since such MMSI transformation leaves no traces, an assessment of a speed-based plausibility is additionally proposed to figure out how many MMSI based injections of false AIS track points can be detected.

Ship Detection Based on Faster R-CNN Using Range-Compressed Airborne Radar Data

Near real-time ship monitoring is crucial for ensuring safety and security at sea. Established ship monitoring systems are the automatic identification system (AIS) and marine radars. However, not all ships are committed to carry an AIS transponder and the marine radars suffer from limited visibility. For these reasons, airborne radars can be used as an additional and supportive sensor for ship monitoring, especially on the open sea. State-of-the-art algorithms for ship detection in radar imagery are based on constant false alarm rate (CFAR). Such algorithms are pixel-based and therefore it can be challenging in practice to achieve near real-time detection. This letter presents two object-oriented ship detectors based on the faster region-based convolutional neural network (R-CNN). The first detector operates in time domain and the second detector operates in Doppler domain of airborne Range-Compressed (RC) radar data patches. The Faster R-CNN models are trained on thousands of real X-band airborne RC radar data patches containing several ship signals. The robustness of the proposed object-oriented ship detectors is tested on multiple scenarios, showing high recall performance of the models even in very dense multitarget scenarios in the complex inshore environment of the North Sea.

Hot spots of unseen fishing vessels

Illegal, unreported, and unregulated (IUU) fishing incurs an annual cost of up to US$25 billion in economic losses, results in substantial losses of aquatic life, and has been linked to human rights violations. Vessel tracking data from the automatic identification system (AIS) are powerful tools for combating IUU, yet AIS transponders can be disabled, reducing its efficacy as a surveillance tool. We present a global dataset of AIS disabling in commercial fisheries, which obscures up to 6% (>4.9 M hours) of vessel activity. Disabling hot spots were located near the exclusive economic zones (EEZs) of Argentina and West African nations and in the Northwest Pacific, all regions of IUU concern. Disabling was highest near transshipment hot spots and near EEZ boundaries, particularly contested ones. We also found links between disabling and location hiding from competitors and pirates. These inferences on where and why activities are obscured provide valuable information to improve fisheries management.

Extended linear regression model for vessel trajectory prediction with a-priori AIS information

ABSTRACT As maritime activities increase globally, there is a greater dependency on technology in monitoring, control, and surveillance of vessel activity. One of the most prominent systems for monitoring vessel activity is the Automatic Identification System (AIS). An increase in both vessels fitted with AIS transponders and satellite and terrestrial AIS receivers has resulted in a significant increase in AIS messages received globally. This resultant rich spatial and temporal data source related to vessel activity provides analysts with the ability to perform enhanced vessel movement analytics, of which a pertinent example is the improvement of vessel location predictions. In this paper, we propose a novel strategy for predicting future locations of vessels making use of historic AIS data. The proposed method uses a Linear Regression Model (LRM) and utilizes historic AIS movement data in the form of a-priori generated spatial maps of the course over ground (LRMAC). The LRMAC is an accurate low complexity first-order method that is easy to implement operationally and shows promising results in areas where there is a consistency in the directionality of historic vessel movement. In areas where the historic directionality of vessel movement is diverse, such as areas close to harbors and ports, the LRMAC defaults to the LRM. The proposed LRMAC method is compared to the Single-Point Neighbor Search (SPNS), which is also a first-order method and has a similar level of computational complexity, and for the use case of predicting tanker and cargo vessel trajectories up to 8 hours into the future, the LRMAC showed improved results both in terms of prediction accuracy and execution time.

Monitoring Kapal Menggunakan Automatic Identification System(AIS) Dengan RTL-SDR dan Low Noise Amplifier (LNA)

Automatic Identification System (AIS) is a ship transponder that uses MMSI data, speed, position, destination, ship type, and size to locate, track, and monitor ships. Only Vessel Traffic Services (VTS) and a few other agencies can be supervised presently. This is one of the issues that must be resolved. To resolve this issue, hardware that can receive AIS signals at 161.975 MHz and 162.025 MHz and convert them into information signals is required. RTL-SDR is hardware capable of receiving signals in the frequency range of 25-1700 MHz. Its antenna is used to achieve maximum signal reception by establishing a direct line of sight to the AIS data source. Yagi antennas can only receive signals from a single direction, the front. Low Noise Amplifier (LNA) is also used in order to optimize the signal received by the antenna. The signal can be processed and decoded using SDR-Sharp and AISMon to provide data that can be plotted on OpenCPN. The performance of the AIS data decoding process is controlled by the strength and weakness of the signal that the RTL-SDR receiver can receive. Therefore, the antenna and receiver must be placed in a clear line of sight (LOS) with the ship's AIS transponder emitting source. It is envisaged that this monitoring system would make it easier to monitor ships in real-time

Availability of Automatic Identification System (AIS) Based on Spectral Analysis of Mean Time to Repair (MTTR) Determined from Dynamic Data Age

Good marine practice and proper operation of navigation, radio navigation, and radio communication systems are nowadays of key importance for marine navigation safety. This applies to merchant vessels and navy ships and effectively monitoring unmanned vehicles along a set route. Technological progress has contributed to developing equipment for ships and unmanned vehicles, which are fitted out with devices of the automatic identification system (AIS). One of the issues in AIS operation is limited-service availability, which manifests itself in the presence of incomplete data for the navigation parameters sent by radio, compressed in dynamic data messages. This results in the unusability of the system information for ships equipped with an AIS transponder. This paper aims to develop an AIS service availability model based on the mean time of incomplete navigation parameter occurrence in AIS data messages and to present the test results in the time and frequency domain using a mathematical method—Fast Fourier Transform. The study results refer to five basic navigation parameters and are indicative of a high service availability index—over 0.99 for three out of the five navigation parameters tested. Data recorded by a ship’s system receiver were the key source of practical knowledge concerning the limitations of AIS service availability. The experiment revealed interruptions in regular data inflow from navigation devices. In effect, a description was provided of a functional relationship based on a spectral analysis of the frequencies of the times occurring between service repair (time to repair—TTR), and the use of the model to analyze other variables was proposed.

Passive acoustic ship detection performance near the Port of Sept-Îles, Quebec

An acoustic recorder was deployed near the Port of Sept-Îles, Quebec in the fall of 2020 and collected six months of data on a four-channel orthogonal array. The system, a JASCO C-lander, operated on a duty cycle consisting of 340s of data recorded at 32 kHz sampling rate, 1 min of data recorded at 256 kHz sampling rate followed by 500s of sleep. Data were stored on SD memory cards for post-retrieval analysis. Vessels were detected using narrowband tonals produced by their propulsion system and other rotating machinery and the sound pressure level (SPL) for each minute of data in the 40–315 Hz shipping frequency band was then computed. A 10min shoulder period before and after the detection was then searched for the highest 1 min SPL which was identified as closest point of approach (CPA) time for each acoustic contact. Vessel track data from the automatic identification system (AIS) were used to compute CPAs for vessels carrying an AIS transponder during the deployment period. A comparison of the two results was used to identify missed and false detections, and to assess the algorithm’s performance. Recommendations for implementing an improved detection approach will be discussed.

Using aerial surveys to fill gaps in AIS vessel traffic data to inform threat assessments, vessel management and planning

AIS (Automatic Identification System) is a widely used source of data to spatially and temporally characterize marine vessel traffic and to subsequently estimate the direct and indirect threats maritime activities pose to marine and coastal environments. However, AIS data do not capture all vessel traffic in an area because not all vessels (e.g., recreational vessels) are required to carry AIS transponders. The goal of this study is to quantify the proportion of vessel traffic not transmitting AIS data by employing dedicated aerial surveys that simultaneously capture AIS data and georeferenced imagery of non-AIS vessels on the Canadian Pacific coast. Results show that 70% of vessel traffic in the surveyed area do not transmit AIS. The majority of these non-AIS vessels are recreational, making up 53% of the total vessels sighted. Analysis of historical AIS data in the survey area shows a steady increase in the voluntary uptake of AIS in the last decade. Results of this study can be used to adjust vessel traffic estimates and associated threats that are exclusively based on AIS data. This information can also benefit managers and policy makers that rely on AIS data when developing vessel traffic management measures to mitigate threats and to inform regional marine spatial plans. Place-specific studies to quantify marine vessel traffic missed by AIS are encouraged in coastal areas with high density of small vessel traffic.

A distributed framework for extracting maritime traffic patterns

ABSTRACT All the modern surveillance systems take advantage of the Automatic Identification System (AIS), a compulsory tracking system for many types of vessels. Ships that carry AIS transponders on board transmit their position and status in order to alert nearby vessels and ground stations, but this information can well be used to identify events of interest and support decision making. The detection of anomalies (i.e. unexpected sailing behavior) in vessels’ trajectories is such an event, which is of utmost importance. Approaches for detecting such anomalies vary from extracting normality models to searching for individual cases, such as AIS switch-off or collision avoidance maneuvers. The current research work follows the former method; it employs sparse historic AIS data and polynomial interpolation in order to extract shipping lanes. It modifies the DB-Scan clustering algorithm in order to achieve more coherent trajectory clusters, which are then composed to create the shipping lanes. The proposed approach implements distributed processing on Apache Spark in order to improve processing speed and scalability and is evaluated using real-world AIS data collected from terrestrial AIS receivers. The evaluation shows that the biggest part (i.e. more than 90%) of any future vessel trajectory falls within the extracted shipping lanes.

Real-time maritime anomaly detection: detecting intentional AIS switch-off

Today, most of the maritime surveillance systems rely on the automatic identification system (AIS), which is compulsory for vessels of specific categories to carry. Anomaly detection typically refers to the problem of finding patterns in data that do not conform to expected behaviour. AIS switch-off is such a pattern that refers to the fact that many vessels turn off their AIS transponder in order to hide their whereabouts when travelling in waters with frequent piracy attacks or potential illegal activity, thus deceiving either the authorities or other piracy vessels. Furthermore, fishing vessels switch off their AIS transponders so as other fishing vessels do not fish in the same area. To the best of our knowledge limited work has focused on AIS switch-off in real-time. We present a system that detects such cases in real-time and can handle high velocity, large volume of streams of AIS messages received from terrestrial base stations. We evaluate the proposed system in a real-world dataset collected from AIS receivers and show the achieved detection accuracy.

Real-time maritime anomaly detection: detecting intentional AIS switch-off

Today, most of the maritime surveillance systems rely on the automatic identification system (AIS), which is compulsory for vessels of specific categories to carry. Anomaly detection typically refers to the problem of finding patterns in data that do not conform to expected behaviour. AIS switch-off is such a pattern that refers to the fact that many vessels turn off their AIS transponder in order to hide their whereabouts when travelling in waters with frequent piracy attacks or potential illegal activity, thus deceiving either the authorities or other piracy vessels. Furthermore, fishing vessels switch off their AIS transponders so as other fishing vessels do not fish in the same area. To the best of our knowledge limited work has focused on AIS switch-off in real-time. We present a system that detects such cases in real-time and can handle high velocity, large volume of streams of AIS messages received from terrestrial base stations. We evaluate the proposed system in a real-world dataset collected from AIS receivers and show the achieved detection accuracy.

An Asynchronous Trajectory Matching Method Based on Piecewise Space-Time Constraints

Automatic Identification System (AIS) is an automatic tracking system based on reports provided by AIS transponders, which can report the static and dynamic information of the ship, such as name, call sign, position and speed etc. However, AIS can be manually turned off, making it impossible to find the trajectory of the ship when AIS is turned off, which results in difficulties in maritime supervision. This article uses shore-based radar trajectory data to present a new method of asynchronous trajectory matching based on piecewise space-time constraints (PTSCTM), which is used to reconstruct AIS trajectory and discover the abnormal behavior of ships. The Ramer–Douglas–Peucker (RDP) algorithm is used to extract feature points of the trajectory, and the trajectory is segmented according to feature points. The method uses space-time constraints to determine candidate trajectory matching points in each trajectory segment and calculates the optimal matching point from the candidate set through the space and time distance. The method asynchronously calculates the similarity of trajectory segments from multi-source trajectories and calculates the trajectory similarity to achieve multi-source trajectory matching. The measured data show that the model can effectively measure the similarity of multi-source trajectories to find the optimal radar trajectory as a matching result when AIS is turned off, and the calculation accuracy and efficiency of the model is higher than the existing methods. According to the asynchronous trajectory matching method, the monitoring method of staying ships in the Qingdao lancelet reserve area is proposed. The result shows that many ships stayed in the area through radar signals when AIS was turned off. The result can provide auxiliary support for maritime supervisory and legal departments, which helps to strengthen maritime ship supervision, detect maritime illegal crimes and protect the marine environment.

The branching‐course model predictive control algorithm for maritime collision avoidance

AbstractThis article presents a new algorithm for short‐term maritime collision avoidance (COLAV) named the branching‐course model predictive control (BC‐MPC) algorithm. The algorithm is designed to be robust with respect to noise on obstacle estimates, which is a significant source of disturbance when using exteroceptive sensors such as, for example, radars for obstacle detection and tracking. Exteroceptive sensors do not require vessel‐to‐vessel communication, which enables COLAV toward vessels not equipped with, for example, automatic identification system transponders, in addition to increasing the robustness with respect to faulty information which may be provided by other vessels. The BC‐MPC algorithm is compliant with Rules 8, 13, and 17 of the International Regulations for Preventing Collisions at Sea (COLREGs), and favors maneuvers following Rules 14 and 15. Specifically, the algorithm can ignore the specific maneuvering regulations of Rules 14 and 15, which may be required in situations where Rule 17 revokes a stand‐on obligation. The algorithm is experimentally validated in several full‐scale experiments in the Trondheimsfjord in 2017 using a radar‐based system for obstacle detection and tracking. To complement the experimental results, we present simulations where the BC‐MPC algorithm is tested in more complex scenarios involving multiple obstacles and several simultaneously active COLREGs rules. The COLAV experiments and simulations show good performance.

How Satellites Can Support the Information Requirements of the Polar Code

In 2000, the International Maritime Organization (IMO) adopted a new requirement for all international and cargo ships exceeding a certain size, and all passenger ships, to carry Automatic Identification System (AIS) transponders capable of providing information about the ship to other ships and to coastal authorities automatically. The requirement became effective for all ships on 31 December 2004. AIS provides other vessels with information about, for example, a ship’s identity, position, course, speed and destination. The IMO is finalizing implementation of the Polar Code for the safety of vessels, which will apply in both polar waters and will require additional information about the profile of the fleets of ships operating there. However it must be noted that the AIS data is generally only available from legitimate operators (for example, licensed fishers, tourist operators and vessels on government service) and if the AIS transponder is turned off, the vessel becomes virtually invisible. This methodology, therefore, is not a stand-alone system. Norway has currently two satellites in polar orbit capable of receiving AIS signals. AIS is an excellent tool to track tourist vessels and as such create situational awareness and assist in search and rescue operations in the Arctic. The paper presents findings from three regions in the High Arctic: east of the coast of Greenland, north of Svalbard and surrounding the Russian archipelago of Franz Joseph Land, for the years 2010 to 2014 about maritime activities in these regions with a focus on passenger and fishing vessels. It also suggests other satellite-based means for verifying the AIS data.

A novel anomaly detection approach to identify intentional AIS on-off switching

The Automatic Identification System (AIS) is a ship reporting system based on messages broadcast by vessels carrying an AIS transponder. The recent increase of terrestrial networks and satellite constellations of receivers is making AIS one of the main sources of information for Maritime Situational Awareness activities. Nevertheless, AIS is subject to reliability and manipulation issues; indeed, the received reports can be unintentionally incorrect, jammed or deliberately spoofed. Moreover, the system can be switched off to cover illicit operations, causing the interruption of AIS reception. This paper addresses the problem of detecting whether a shortage of AIS messages represents an alerting situation or not, by exploiting the Received Signal Strength Indicator available at the AIS Base Stations (BS). In designing such an anomaly detector, the electromagnetic propagation conditions that characterize the channel between ship AIS transponders and BS have to be taken into consideration. The first part of this work is thus focused on the experimental investigation and characterisation of coverage patterns extracted from the real historical AIS data. In addition, the paper proposes an anomaly detection algorithm to identify intentional AIS on-off switching. The presented methodology is then illustrated and assessed on a real-world dataset.

Identifying and Analyzing Safety Critical Maneuvers from High Resolution AIS Data

The authors demonstrate the value in previously disregarded parameters in Automatic Identification System (AIS) data, and present a novel way of quickly identifying and characterizing potentially safety critical situations for vessels with a properly configured AIS transponder. The traditional approach of studying (near) collision situations, is through vessel conflict zones, based on vessel location and speed from low resolution AIS data. Their approach utilizes the rate of turn parameter in the AIS signal, at maximum time resolution. From collision investigation reports it is often seen that prior to or at collision navigators perform frenetic rudder actions in the hope to avoid collision in the last second. These hard maneuverings are easily spotted as non-normal rate of turn signals. An identified potential critical situation may then be further characterized by the occurring centripetal acceleration a vessel is exposed to. The authors demonstrate the novelty of their methodology in a case study of a real ship collision. As the rate of turn parameter is directly linkable to the navigator behavior it provides information about when and to what degree actions were taken. They believe their work will therefore inspire new research on safety and human factors as a risk profiles could be derived based on AIS data.

Using Open Source Data to Quantify the Impact of Supply Chain Disruptions at Niche Ports: Scenario Involving Canada’s Largest Oil Refinery

Niche ports typically are smaller marine ports that specialize in handling specific commodities and can be the interface between import and export of bulk commodities from landside facilities, such as oil refineries. Specialization can make port-dependent industries and their customers particularly vulnerable to disruptions of port operations because the specialized handling facilities may be unique to the port. Industries may not be able to shift their supply to other modes, such as rail. This paper outlines the use of automatic identification system transponder data from oceangoing vessels, in concert with open source data, to quantify first-order impacts of a supply chain disruption at a niche port. Results are presented from a study of a Canadian niche port where a worst-reasonable-case scenario of a 1-week disruption to oil imports and exports would delay delivery of an estimated 246 million L of fuel to New England for at least a week; Boston, Massachusetts, and Portland, Maine, would be most affected. The approach used in this study could be a valuable screening tool for public safety agencies wanting a better understanding of vulnerabilities and interdependencies at niche ports in situations in which detailed simulations and modeling are impractical.

A vessel noise budget for Admiralty Inlet, Puget Sound, Washington (USA)

One calendar year of Automatic Identification System (AIS) ship-traffic data was paired with hydrophone recordings to assess ambient noise in northern Admiralty Inlet, Puget Sound, WA (USA) and to quantify the contribution of vessel traffic. The study region included inland waters of the Salish Sea within a 20 km radius of the hydrophone deployment site. Spectra and hourly, daily, and monthly ambient noise statistics for unweighted broadband (0.02-30 kHz) and marine mammal, or M-weighted, sound pressure levels showed variability driven largely by vessel traffic. Over the calendar year, 1363 unique AIS transmitting vessels were recorded, with at least one AIS transmitting vessel present in the study area 90% of the time. A vessel noise budget was calculated for all vessels equipped with AIS transponders. Cargo ships were the largest contributor to the vessel noise budget, followed by tugs and passenger vessels. A simple model to predict received levels at the site based on an incoherent summation of noise from different vessels resulted in a cumulative probability density function of broadband sound pressure levels that shows good agreement with 85% of the temporal data.

선박자동식별시스템(AIS)과 XML을 이용한 선박위치정보 서비스

AIS(선박자동식별시스템)는 인접한 선박의 정보를 서로 통신하여 해양사고를 예방하거나 대응할 수 있게 해주고, 해상의 다양한 정보를 다른 정보와 융합하여 새로운 정보를 창출할 수 있는 해양 정보시스템의 핵심이라 할 수 있다. 이러한 AIS정보는 많은 잠재적 정보를 담고 있지만, 저장과 관리의 미흡으로 사용하기 쉽지 않고, 또한 정보를 얻기 위해 반드시 AIS 송수신기가 있어야 한다. 본 논문에서는 이러한 AIS와 XML을 이용한 선박위치정보 서비스를 제안한다. 이를 위해 NMEA-0183으로 인코딩된 AIS 정보를 디코딩하여 데이터베이스로 저장하고, 인터넷 상에서 XML을 이용하여 정보를 탐색하는 서비스를 구축하였다. AIS(Automatic Identification System) is a key of maritime information system to prevent maritime accident through communication among any adjacent ship and to establish information base by fusing information collected from ships with other information. The AIS information includes various potential information, but it is not easy to use due to insufficiency storing and management. Furthermore, we need AIS transponders to acquire the information. In this paper, we propose the vessel positional information service using AIS and XML. We decode the AIS information from the NMEA-0183 encoding data and store the AIS information to the database and provide the access service in the internet using XML.

INTEGRATION OF AIRBORNE AIS BRINGS A NEW DIMENSION TO THE DETECTION OF ILLEGAL DISCHARGE OF OIL SPILLS

ABSTRACT The AIS (Automatic Identification System) became a standard under the International Convention for the Safety of Life at Sea (SOLAS) in December 2001. Using the AIS transponder, the vessel will report its position, identity heading and other information to other ship traffic in the same area. The AIS is a significant contribution to the control and safety of shipping. Transport Canada (TC) is a pioneer internationally in using the AIS (R4A airborne AIS transponder) integrated with the remote sensing system (MSS 6000 from Swedish Space Corporation) on board their aircraft, in order to add the information on ship identity and voyage to the information from other sensors. The AIS integration provides a quantum leap in the effectiveness of the surveillance mission. In the Canadian system integration, detailed vessel information obtained through the AIS system is displayed and correlated with information received from other sensors. All information is also presented together with a high-resolution digital map including nautical chart data. The AIS provides the operators with vessel voyage and identity information. It is very useful for vectoring the aircraft to areas where ships have been reported. The advent of AIS simplifies surveillance procedures and makes each hour of surveillance more productive. During last year TC reported that over 9700 vessels were over flown. It is anticipated that over 20,000 AIS targets will be acquired using AIS in 2007–2008. The experience from Canada also indicates an outstanding VHF range. During missions, vessels have been identified as far away as 200 nautical miles (n.m.) from the aircraft. The AIS information together with other sensor images gives a comprehensive image that will dramatically improve the chance to tie a ship to a detected illegal oil spill. Receiving ship identity information will enable aircrews to conduct covert operations and contact the suspect ship from far standoff distances. This will also act as a deterrent to potential polluters. Integrating AIS within the operational requirements of surveillance programs, dedicated to minimizing the adverse effects of shipping on the marine environment, will make a significant contribution towards improving the effectiveness of surveillance missions.