Collision and Detection Performance with Three Overlap Signal Collisions in Space-Based AIS Reception

Yang, Menghui; Zou, Yongzhong; Fang, Li

doi:10.1109/trustcom.2012.109

Cited by 8 publications

(2 citation statements)

References 2 publications

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“…Applying traditional data processing techniques can lead to processing times of several days, if applied to global data sets of considerable size. In addition to this, conventional algorithms are proving incapable of dealing with the uncertainty and partial truth present in such datasets [6], [7]. As such, AIS datasets present some unique characteristics and difficulties.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Spatial Method for Modeling Maritime Routes

et al. 2020

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In this work we propose a novel spatial knowledge discovery pipeline capable of automatically unravelling the ''roads of the sea'' and maritime traffic patterns by analysing voluminous vessel tracking data, as collected through the Automatic Identification System (AIS). We present a computationally efficient and highly accurate solution, based on a MapReduce approach and unsupervised learning methods, capable of identifying the spatiotemporal dynamics of ship routes and most crucially their characteristics, thus deriving maritime ''patterns of life'' at a global scale, without the reliance on any additional information sources or a priori expert knowledge. Experimental results confirm high accuracy of results and superior performance in comparison to other methods, with the entire processing duration completing in less than 3 hours for more than a terabyte of non-uniform spatial data. Finally, to clearly demonstrate the applicability and impact of our proposed method, we evaluate its ability to detect real world ''anomalies'', such as maritime incidents reported in the European Marine Casualty Information Platform. Numerical results show the advantages of our scheme in terms of accuracy, with an achieved anomaly detection accuracy of higher than 93%, by detecting 313 out of 335 relevant maritime incidents. INDEX TERMS AIS, anomaly detection, data driven maritime traffic, patterns of life, routes.

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Section: Introductionmentioning

confidence: 99%

A Distributed Spatial Method for Modeling Maritime Routes

et al. 2020

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“…Due to these two problems, it mainly affects the organizational mechanism of S-AIS signals. It results a collision data, as illustrated in the Figure 1, issued by vessels located in different AIS cells but received at the antenna of the same satellite [8], [9]. For this reason, we present new approaches to address this problem where the Doppler effect and the propagation delay are also taken into consideration.…”

Section: Introductionmentioning

confidence: 99%

Blind separation of complex-valued satellite-AIS data for marine surveillance: a spatial quadratic time-frequency domain approach

Cherrak

Ghennioui

Thirion-Moreau

et al. 2019

IJECE

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<p>In this paper, the problem of the blind separation of complex-valued Satellite-AIS data for marine surveillance is addressed. Due to the specific properties of the sources under consideration: they are cyclo-stationary signals with two close cyclic frequencies, we opt for spatial quadratic time-frequency domain methods. The use of an additional diversity, the time delay, is aimed at making it possible to undo the mixing of signals at the multi-sensor receiver. The suggested method involves three main stages. First, the spatial generalized mean Ambiguity function of the observations across the array is constructed. Second, in the Ambiguity plane, Delay-Doppler regions of high magnitude are determined and Delay-Doppler points of peaky values are selected. Third, the mixing matrix is estimated from these Delay-Doppler regions using our proposed non-unitary joint zero-(block) diagonalization algorithms as to perform separation.</p>

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