Maritime Over the Horizon Sensor Integration: HFSWR Data Fusion Algorithm

Nikolic, Dejan; Stojkovic, Nikola; Popovic, Zdravko; Tošić, Nikola; Lekic, Nikola; Stankovic, Zoran; Dončov, Nebojša

doi:10.3390/rs11070852

Cited by 25 publications

(24 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Combination of estimated satellite link parameters with inserted expected random failure probability of network components gives the network planners a precise estimation of maritime surveillance system reliability, given the time of the satellite link outage that does not seriously affect the surveillance performance. For example, from our experience in the Gulf of Guinea, outage duration of about 9 min can be tolerated without a significant impact on the data fusion process as it is shown in [32]. With the presented laboratory for IoT communication infrastructure environment, network planners can have a good insight into potential weak spots in the overall network and a priori can plan solutions to avoid them.…”

Section: Estimation Of Satellite Link Parametersmentioning

confidence: 90%

“…Given this, the most important parameter is the duration of a network outage, as a defining factor. Figure 10 shows a screenshot of outputs from the multi-sensor data integration algorithms before, during and after data absence for one real network situation [32]. From Figure 10, one can see that the fused track id No.…”

Section: Use Case 1: Hfswr Data Fusionmentioning

confidence: 99%

“…Again, the most common usage of these laboratories is for smart home and smart city solutions [29].The laboratory environment proposed in this paper focuses mainly on the IoT communication infrastructure and its impact on a maritime surveillance network, unlike the other laboratories. The laboratory was extensively used during the design process of multi-sensor data integration algorithms [31,32] in order to provide the designers of the aforementioned algorithms insight into real communication infrastructure behavior. In this way, the algorithms are mostly tuned to the real working Sensors 2020, 20, 1349 4 of 20 environment in the laboratory environment and later integrated into the real maritime surveillance network, thus minimizing the impact of the tuning process on the operational readiness of the maritime surveillance network.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Designing Laboratory for IoT Communication Infrastructure Environment for Remote Maritime Surveillance in Equatorial Areas Based on the Gulf of Guinea Field Experiences

Petrović

Simić

Drajić

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

The steady increase of the world population and economy leads to an increase in both types and amounts of goods transported over seas, which further inevitably leads to an increase of criminal activities in the maritime arena. In order to stifle criminal activities nations are forced to develop sophisticated sensor networks. The backbone of any sensor network is a communication network which connects all sensors with the command centers, most often located hundreds of kilometers away from the sensors. In developing countries, communication networks are very often poorly developed, leaving only satellite links as somewhat reliable means of communication. Henceforth, in this paper, a laboratory for the Internet of Things (IoT) communication infrastructure environment designed to facilitate maritime sensor network design process in areas where communication network is dependent on data transfer over satellite links is presented. In order to successfully describe and develop a laboratory for IoT communication infrastructure environment, necessary data are collected during the design and deployment of a maritime surveillance network in the Gulf of Guinea. The main advantage of the proposed laboratory environment is the inclusion of satellite link simulation in the IoT laboratory environment. This feature provides an opportunity to cover a much broader scope of IoT solutions compared to other IoT laboratories.2 of 20 fishing [5]. More importantly, overly excessive fishing can lead to the complete depletion of biological resources and thus endanger the marine ecosystem to the point of no return [6]. On the other hand, according to [7], maritime piracy is becoming quite common in some regions of the world. Attacks are mostly performed in order to hijack a vessel and request a ransom for crew and vessel itself or to steal valuable cargo such as crude oil. Regardless of the final goal of the pirate attack, it is clear pirate attacks must be stopped. The very same is true for illegal fishing, which is probably even more important, since permanently damaging oceans ecosystems will definitely endanger a human survival on this planet.In order to provide a safer maritime environment, firstly a sensor network for continuous monitoring of maritime activities in territorial sea and EEZ must be established. On the other hand, since EEZs are huge bodies of water that can cover hundreds of thousands of square kilometers, complete monitoring is much easier said than done. To the best of our knowledge, there are only two ways to achieve complete EEZ monitoring. The first approach utilizes optical and microwave sensors on platforms such as satellites and airplanes, thus avoiding the limitations of the sensors, but this introduces limitations in the platform. The most limiting factor is the interrupted data availability, since no airplane is able to stay in the air constantly during the whole year and during all weather conditions. Meanwhile, satellites, which are orbiting around the Earth, are over the zone of interest for a limited tim...

show abstract

Section: Estimation Of Satellite Link Parametersmentioning

confidence: 90%

Section: Use Case 1: Hfswr Data Fusionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Designing Laboratory for IoT Communication Infrastructure Environment for Remote Maritime Surveillance in Equatorial Areas Based on the Gulf of Guinea Field Experiences

Petrović

Simić

Drajić

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since not all ships are AIS data-and VMS (vessel monitoring system)-compliant, they also use supplementary information such as synthetic aperture radar (SAR) in satellite remote sensing (RS) for vessel recognition [31,38]. High-frequency surface-wave radar (HFSWR) for monitoring maritime crimes in the Gulf of Guinea's economic exclusive zone (EEZ) also showed the use of land and satellite ANIS data to compensate for technical shortcomings that increase the need for a low signal [47]. U.S. federal agencies are often required to validate AIS information using other data sources to ensure robust analytics and decision-making capabilities.…”

Section: Affordabilitymentioning

confidence: 99%

The Maturity of Automatic Identification Systems (AIS) and Its Implications for Innovation

Lee

Mokashi

Moon

et al. 2019

JMSE

View full text Add to dashboard Cite

The member states of International Maritime Organization (IMO) have been leading in and enforcing the use of automatic identification systems (AIS) in the analysis of ship-to-ship collisions, vessel monitoring, and maritime traffic management offshore. This study will help non-federal stakeholders understand the AIS data and contribute to future research by assessing difficulties and improving access to data and applications. This study introduces the basics of AIS materials, shared channels, and currently developed applications, and discusses areas where they can be incorporated in the future. The literature revealed that using AIS data will be beneficial to the public as well as to business and public agencies.IMO has been leading in and enforcing the use of AIS in ship-to-ship collisions, vessel monitoring, and maritime traffic management offshore. U.S. federal agencies actively access nationwide AIS (NAIS) information to perform a variety of functions, including security, safety, and policy-making.However, access to the data needed by non-federal stakeholders, including in the marine industry and academic fields, is still limited; AIS data are not very well known, and related research and use is at the preliminary stage. However, if the importance and usefulness of AIS information were known, stakeholders could make rapid progress in the use of the data and its related applications in the near future. Therefore, this study aims to help non-federal stakeholders understand the AIS data and support future research by assessing difficulties and improving access to data and its applications.This study introduces the basics of AIS materials, shared channels, and currently developed applications, and discusses areas where they can be incorporated in the future. It examines the existing literature relating to AIS data and its derived products of application. The study is structured as follows: first, this study discusses the background and introduces the status of the data in Section 2. Data scientists and information engineers understand the need for data and determine the right information to collect. Because data consumes memory and storage space, it can be expensive depending on the size of the data. This is discussed in Section 3, on the nature of the data and the policy of collecting, using, and sharing data. Views of data science, information management, and governance are described in Section 4. This section describes the methods and tools for collecting, storing, distributing, and visualizing AIS data. Section 5 explores the evolution and development of applications using AIS. Finally, in the concluding section, the research is summarized, with suggestions to increase the accessibility and utilization of AIS data.

show abstract

“…In ref. [7] target detection at over the horizon (OTH) distances is addressed using a network of high frequency surface-wave-radars (HFSWR). Since a vessel is typically observed by multiple HFSWR, there may be very often situations in which multiple tracks for a vessel are present.…”

mentioning

confidence: 99%

Editorial for the Special Issue “Remote Sensing of Target Detection in Marine Environment”

2019

View full text Add to dashboard Cite

Remote sensing is a powerful tool used to obtain an unprecedented amount of information about the ocean from a distance, usually from satellites or aircrafts. Measurements collected by active and passive remote sensing instruments can be used for both marine and maritime applications. They allow monitoring of vast areas of the Earth that are difficult to access and sample using traditional methods. Within this context, the observation of targets at sea, e.g.; man-made targets (ships or oil/gas rigs/platforms and wind turbines) and natural targets (icebergs, surfactants, etc.) is nowadays a very hot-topic in the field of global monitoring of environment and security.Among the remote sensing tools, Synthetic Aperture Radar (SAR) is one of the most used since it allows all-day and almost all-weather observations with a moderate-to-fine spatial resolution. SAR imagery gives the possibility to overcome the limits of maritime patrol allowing non-cooperative all-day target surveillance, over wide regions and under almost all-weather conditions. An increasing number of SAR satellites have become available since the early 1990s. This unprecedented development in SAR sensors requires the definition of new techniques and algorithms to detect marine targets, as well as in the assessment of existing methods. Hence, although there is a great deal of literature that concerns SAR methods for detecting targets at sea, there is still room to improve both models and methods.Within this context, methods and models are proposed in this Special Issue (SI) to exploit single-and multi-polarization SAR measurements for observation of targets at sea, with special interest for vessels.In ref.[1] the complex coherence between the two channels of the dual-polarized Sentinel-1 SAR imagery is exploited to detect ships in a timely manner using the entire image. The proposed rationale is verified against real SAR imagery that includes a large number of vessels of different sizes. In addition, Automatic Identification System (AIS) data are used for an extensive and large-scale cross-comparison with the SAR-based detections. Experimental results clearly point out the remarkable detection rate of the SAR-based method and its complementarity with respect to AIS information.In ref.[2] fishing vessels detection is addressed with reference to the problem of global overfishing. A method is here developed to deal with the discrimination between fishing and non-fishing vessels and a showcase is presented. The method, which is based on the Random Forest (RF) classifier, takes as input the vessel's length, longitude, and latitude, its distance to the nearest shore, and the time of the measurement (am or pm). The classifier is trained and tested on data from the AIS and Sentinel-1 SAR imagery that refers to the North Sea.In ref.[3] a system to monitor illegal fishing activities is proposed. The approach, which is developed in the framework of a cooperation between the Plymouth Marine Laboratory (PML) and

show abstract

Maritime Over the Horizon Sensor Integration: HFSWR Data Fusion Algorithm

Cited by 25 publications

References 19 publications

Designing Laboratory for IoT Communication Infrastructure Environment for Remote Maritime Surveillance in Equatorial Areas Based on the Gulf of Guinea Field Experiences

Designing Laboratory for IoT Communication Infrastructure Environment for Remote Maritime Surveillance in Equatorial Areas Based on the Gulf of Guinea Field Experiences

The Maturity of Automatic Identification Systems (AIS) and Its Implications for Innovation

Editorial for the Special Issue “Remote Sensing of Target Detection in Marine Environment”

Contact Info

Product

Resources

About