Statistical Analysis of the Detection Probability of the TianTuo-3 Space-based AIS

2017 ◽  
Vol 71 (2) ◽  
pp. 467-481 ◽  
Author(s):  
Shiyou Li ◽  
Lihu Chen ◽  
Xiaoqian Chen ◽  
Yong Zhao ◽  
Lei Yang
Keyword(s):  
Statistical Analysis ◽  
Detection Probability ◽  
High Probability ◽  
Distribution Density ◽  
Automatic Identification ◽  
Identification System ◽  
Receiving Antenna ◽  
Defense Technology ◽  
Vessel Detection ◽  
National University

The Micro-Nano satellite TianTuo-3 (TT-3) developed by the National University of Defense Technology (NUDT) was successfully launched on 20 September 2015. The space-based Automatic Identification System (AIS) on board TT-3 works well and stably receives AIS signals from global vessels. In this work, we perform statistical analysis on the detection probability of the vessels in the concerned areas by using the TT-3 AIS data. The results suggest that the detection probability of vessels decreases as the distribution density increases, especially in the offshore areas of dense traffic and the TT3-AIS vessel detection probability in the oceans can be higher than 40%, indicating that the TT-3 AIS has achieved a high probability of coverage of vessels for a single receiving antenna. The analysis results will present helpful references both in evaluating the potential application of satellite-based AIS and for designing the next generation space-based AIS which might greatly improve the detection probability of ocean-going vessels.

scholarly journals Semi-Continuous Spatial Statistical Analysis Using AIS Data for Vessel Traffic Flow Characteristics in Fairway

2021 ◽  
Vol 9 (4) ◽  
pp. 378
Author(s):  
Jong Kwan Kim
Keyword(s):  
Statistical Analysis ◽  
Traffic Flow ◽  
Flow Characteristics ◽  
Automatic Identification ◽  
Identification System ◽  
Spatial Statistical Analysis ◽  
Traffic Characteristics ◽  
Traffic Distribution ◽  
Traffic Flow Characteristics ◽  
Marine Accidents

As high vessel traffic in fairways is likely to cause frequent marine accidents, understanding vessel traffic flow characteristics is necessary to prevent marine accidents in fairways. Therefore, this study conducted semi-continuous spatial statistical analysis tests (the normal distribution test, kurtosis test and skewness test) to understand vessel traffic flow characteristics. First, a vessel traffic survey was conducted in a designated area (Busan North Port) for seven days. The data were collected using an automatic identification system and subsequently converted using semi-continuous processing methods. Thereafter, the converted data were used to conduct three methods of spatial statistical analysis. The analysis results revealed the vessel traffic distribution and its characteristics, such as the degree of use and lateral positioning on the fairway based on the size of the vessel. In addition, the generalization of the results of this study along with that of further studies will aid in deriving the traffic characteristics of vessels on the fairway. Moreover, these characteristics will reduce maritime accidents on the fairway, in addition to establishing the foundation for research on autonomous ships.

A Beam Scanning Method based on the Helical Antenna for Space-based AIS

2014 ◽  
Vol 68 (1) ◽  
pp. 52-70 ◽  
Author(s):  
Yun Cheng ◽  
Lihu Chen ◽  
Xiaoqian Chen
Keyword(s):  
Detection Probability ◽  
Automatic Identification ◽  
Identification System ◽  
Helical Antenna ◽  
Beam Scanning ◽  
Scanning Method ◽  
Ship Detection ◽  
Low Earth Orbits ◽  
Earth Orbits ◽  
Scanning Antenna

We investigate a strategy to address the problem of low ship detection probability of space-based Automatic Identification System (AIS). A directional AIS antenna and an innovative beam scanning method are proposed, which scan the antenna across a wide swath to provide complete coverage and maintain the advantage of a narrow footprint to reduce signal collision. Aiming at the mission requirement of global ship detection by the year 2016, the appropriate swath, the scanning range and the scanning rate were studied and designed in detail. Theoretical analysis and simulations showed that this scanning antenna can greatly improve ship detection probability and hold the detection probability at an average reporting interval from six to 15 seconds for most oceans when compared with the traditional fixed wide beam antenna. Furthermore, the detection capacity of this scanning antenna was little affected by the heights of different Low Earth Orbits. The results of this work show that the design of the helical antenna along with the beam scanning method can be considered as a building block of future space-based AIS.

scholarly journals DEEP LEARNING FOR VESSEL DETECTION AND IDENTIFICATION FROM SPACEBORNE OPTICAL IMAGERY

2021 ◽  
Vol V-3-2021 ◽  
pp. 303-310
Author(s):  
G. Matasci ◽  
J. Plante ◽  
K. Kasa ◽  
P. Mousavi ◽  
A. Stewart ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Input Image ◽  
Automatic Identification ◽  
Identification System ◽  
Optical Remote Sensing ◽  
Detection Model ◽  
Model Based ◽  
Optical Images ◽  
Vessel Detection

Abstract. We present a deep learning-based vessel detection and (re-)identification approach from spaceborne optical images. We introduce these two components as part of a maritime surveillance from space pipeline and present experimental results on challenging real-world maritime datasets derived from WorldView imagery. First, we developed a vessel detection model based on RetinaNet achieving a performance of 0.795 F1-score on a challenging multi-scale dataset. We then collected a large-scale dataset for vessel identification by applying the detection model on 200+ optical images, detecting the vessels therein and assigning them an identity via an Automatic Identification System association framework. A vessel re-identification model based on Twin neural networks has then been trained on this dataset featuring 2500+ unique vessels with multiple repeated occurrences across different acquisitions. The model allows to naturally establish similarities between vessel images. It returns a relevant ranking of candidate vessels from a database when provided an input image for a specific vessel the user might be interested in, with top-1 and top-10 accuracies of 38.7% and 76.5%, respectively. This study demonstrates the potential offered by the latest advances in deep learning and computer vision when applied to optical remote sensing imagery in a maritime context, opening new opportunities for automated vessel monitoring and tracking capabilities from space.

Assessment of the Influence of Offshore Wind Farms on Ship Traffic Flow Based on AIS Data

2019 ◽  
Vol 73 (1) ◽  
pp. 131-148 ◽  
Author(s):  
Qing Yu ◽  
Kezhong Liu ◽  
A.P. Teixeira ◽  
C. Guedes Soares
Keyword(s):  
Statistical Analysis ◽  
Traffic Flow ◽  
Wind Farms ◽  
Offshore Wind ◽  
Automatic Identification ◽  
Identification System ◽  
Offshore Wind Farms ◽  
Ship Traffic ◽  
Maritime Traffic ◽  
Before And After

This paper proposes a framework to assess the influence of Offshore Wind Farms (OWFs) on maritime traffic flow based on raw Automatic Identification System (AIS) data collected before and after the installation of the offshore wind turbines. The framework includes modules for data acquisition, data filtering and statistical analysis. The statistical analysis characterises the influence of an OWF on maritime traffic in terms of minimum passing distances and lateral distribution of the ship trajectories near the OWF. The framework is applied to a specific route for which AIS data is available before and after an OWF installation. The impacts of the OWF on marine traffic are diverse and depend on the ship type categories. This paper quantitatively characterises an OWF's influence on a specific route that is probabilistically modelled, which is important for further studies on OWF site selection and maritime traffic risk assessment and management.

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