Study of an Evaluation Model for AIS Receiver Sensitivity Measurements

Hu, Qing; Cao, Jingyun; Gao, Gemengyue; Xu, Linlin; Song, Meng’en

doi:10.1109/tim.2019.2910341

Cited by 7 publications

(5 citation statements)

References 28 publications

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“…As depicted in Figure 9 below, the test setup consists of four sub-systems and are annotated in red color numbers, viz., playback system (1) , command and control (2) , the OSD (3) package and acquisition system (4) . The playback system or data simulator mimics the camera electronics and can restream an already acquired image with appropriate interface definition.…”

Section: Test Setupmentioning

confidence: 99%

“…Finally, an acquisition system, receives the packets as per data-handling protocol, unpacks and analyzes the results for debug, analysis, and reconfigurations purposes. OSD (3) Acquisition System (4) Command & Control (1) Playback System (2) (b)…”

Section: Test Setupmentioning

confidence: 99%

“…AIS [ 2 , 3 , 4 ] transceivers are installed on all the vessels to establish a dynamic network within their proximity. AIS-transceivers broadcast information about themselves at a regular time interval.…”

Section: Introductionmentioning

confidence: 99%

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On-Board Ship Detection for Medium Resolution Optical Sensors

Ghosh

Konugurthi

Singupurapu

et al. 2021

Sensors

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In recent years there has been an increased interest in ocean surveillance. The activity includes control and monitoring of illegal fisheries, manmade ocean pollution and illegal sea traffic surveillance, etc. The key problem is how to identify ships and ship-like objects accurately and in a timely manner. In this context, currently, many solutions have been proposed based on high resolution optical and radar remote sensing systems. Most often, these systems suffer from two major limitations viz., limited swath, thereby requiring multiple satellites to cover the region of interest and huge volumes of data being transmitted to ground, even though effective per-pixel information content is minimal. Another limitation is that the existing systems are either simulated on ground or built using the non-space qualified/Commercial Of-The-Shelf (COTS) components. This paper proposes an efficient on-board ship detection system/package connected with medium resolution wide swath optical camera. The methodology adopted has three major components, viz., onboard data processing for improving the radiometric fidelity, followed by a ship detection using modified Constant False Alarm Rate algorithm (CFAR) and a false alarm suppression module to mask false identifications. Finally, the package outputs only the locations of the ships, which is transmitted to the ground. The proposed system reduces the effective volume of data to be transmitted and processed on ground and also significantly cuts down the turnaround time for achieving the end objective. The system is built on radiation hardened Field Programmable Gate Array (FPGA) devices to meet the various engineering constraints such as real-time performance, limited onboard power, radiation hardness, handling of multiple custom interfaces etc. The system is tested with one of the medium resolution Multispectral Visual and Near Infra-Red (MX-VNIR) sensor having a spatial resolution of around 50 m and swath of around 500 Kms, which would be flown with one of the upcoming satellites. The systems performance is also verified on ground with Indian Remote Sensing (IRS) Satellite’s Resourcesat’s Advanced Wide Field Sensor (AWiFS) data and the results are found to be quite encouraging as well as meeting the mission objectives.

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Section: Test Setupmentioning

confidence: 99%

Section: Test Setupmentioning

confidence: 99%

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On-Board Ship Detection for Medium Resolution Optical Sensors

Ghosh

Konugurthi

Singupurapu

et al. 2021

Sensors

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“…AIS data may contain some outliers, and many studies have been conducted with machine-learning-related models to correct AIS data outliers [6]. Some researchers have proposed an ensemble framework based on a network of continuously operating reference stations that utilizes multiple interpolation methods to address the lack of AIS data [7][8][9]. With the increase in maritime activities, ship pollution reduction has been a subject of significant focus in the maritime community [10].…”

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Ship Pollution Distribution Exploitation and Harbor Environmental Impact Analysis via Large-Scale AIS Data

Chen,

Dou,

Song

et al. 2024

JMSE

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Ship pollution emissions have attracted increasing attention in the maritime field due to the massive growth of maritime traffic activities. It is important to identify the ship emissions (SEs) magnitude and corresponding spatial and temporal distributions for the purposes of developing appropriate strategies to mitigate environment pollution. The aim of this study was to estimate ship pollution emissions with various typical merchant ship types under different sailing conditions. We estimated the emission variation with a ship traffic emission assessment model (STEAM2), and then the ship pollution emission distribution was further visualized using ArcGIS. We collected data from the automatic identification system (AIS) for ships in New York Harbor and further analyzed the spatiotemporal distribution of pollutant emissions from ships. The experimental results demonstrate that the ship pollutant emission volume in the New York Harbor area in 2022 was 3340 t, while the pollution in terms of CO, SO2, CXHX, PM10, NOX, and PM2.5 was 136, 1421, 66, 185, 1384, and 148 t, respectively. The overall SEs from container ships, passenger ships, and tankers account for a large amount of pollution discharge. The pollutant emissions of container ships are significantly greater than that of their counterparts. Moreover, the spatiotemporal distributions of ship pollutant discharge can vary significantly among different ship types and sailing conditions.

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“…In [5], the protocol to reduce collision in VDES was introduced. A receiver sensitivity evaluation model of AIS terminals was proposed [6]. Deep learning architecture in [7] was introduced for vessel tracking prediction based on AIS data.…”

Section: Introductionmentioning

confidence: 99%

MIMO-aided Efficient Communication Resource Scheduling Scheme in VDES

2022

KSII TIIS

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As demands for the maritime communications increase, a variety of functions and information are required to exchange via elements of maritime systems, which leads communication traffic increases in maritime frequency bands, especially in VHF (Very High Frequency) band. Thus, effective resource management is crucial to the future maritime communication systems not only to the typical terrestrial communication systems. VHF data exchange system (VDES) enables to utilize more flexible configuration according to the communication condition. This paper focuses on the VDES communication system among VDES terminals such as shore stations, ship stations and aids to navigation (AtoN) to address efficient resource allocation. We propose a resource management method considering a MIMO (Multiple Input Multiple Output) technique in VDES, which has been widely used for modern terrestrial wireless networks but not for marine environments by scheduling the essential communication resources. We introduce the general channel model in marine environment and give two metrics, spectral and the energy efficiencies to examine our resource scheduling algorithm. Based on the simulation results and analysis, the proposed method provides a possibility to enhance spectral and energy efficiencies. Additionally, we present a trade-off relationship between spectral and energy efficiencies. Furthermore, we examine the resource efficiencies related to the imperfect channel estimation.

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Study of an Evaluation Model for AIS Receiver Sensitivity Measurements

Cited by 7 publications

References 28 publications

On-Board Ship Detection for Medium Resolution Optical Sensors

On-Board Ship Detection for Medium Resolution Optical Sensors

Spatial-Temporal Ship Pollution Distribution Exploitation and Harbor Environmental Impact Analysis via Large-Scale AIS Data

MIMO-aided Efficient Communication Resource Scheduling Scheme in VDES

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