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Collision problem: The AIS signals from two different SO-TDMA cells received to the satellite antenna at the same time.
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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 d...
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... 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. ...
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A cyclostationary-based blind signal separation (BSS) algorithm is proposed in this paper. The proposed separation algorithm is used for simultaneously received multiple spectrum overlapped BPSK signals in single channel electromagnetic surveillance system. The mixed signal is separated by means of blind adaptive frequency shift (BA-FRESH) filter b...
Citations
... where R γk nF M (f ) and C δk nF M (f ) are the Fourier transforms of the first, R γk nF M (τ ), and second, C δk nF M (τ ), cyclic correlation matrices of n FM (t) for the delay τ and the cyclic frequency γ k and δ k , respectively. Using (18), (22) and (23) into (21), we obtain an alternative expression of (21) given by: ...
Widely linear (WL) receivers have been developed in the past for single antenna interference cancellation (SAIC) of one rectilinear (R) or quasi-rectilinear (QR) data-like multi-user interference (MUI) or co-channel interference (CCI) in particular. The SAIC technology has been implemented in global system for mobile communications (GSM) handsets in particular and has been further analyzed for voice services over adaptive multi-user channels on one slot (VAMOS) standard. It remains of great interest for several current and future applications using R or QR signals, such as anti-collisions processing in radio frequency identification (RFID) or in satellite-AIS systems and to densify 5G and Beyond 5G (B5G) networks through one dimensional signaling or over-loaded large MU-MIMO systems. It may be required to cancel the inter-symbol interference (ISI) of control and non-payload communications (CNPC) links of unmanned aerial vehicles (UAV) and the inter-carrier interference (ICI) of filter bank multi-carrier offset quadrature amplitude modulation (FBMC-OQAM), which are now candidate for B5G mobile networks. For these challenging applications, the development of enhanced WL filtering based SAIC or Multiple Antenna Interference Cancellation (MAIC) receivers for R and QR signals may be of great interest. Such a receiver, corresponding to a three-input WL frequency shift (FRESH) receiver, has been introduced recently for QR signals. However this WL receiver is not robust to a data-like MUI having a residual frequency offset (FO), which occurs for most of the previous applications. In this context, the paper first extends, for arbitrary propagation channels, the standards (for R and QR MUI) and the enhanced (for QR MUI) SAIC/MAIC WL receivers to MUI with a non-zero FO. Then, it shows the less efficiency of the two-input WL receiver for QR MUI with a non-zero FO and the performance improvement obtained with the three-input WL receiver. Finally, it analyzes, both analytically and by simulations, for R and QR MUI, the impact of the MUI FO on the performance of the proposed receivers. The results of the paper should allow the development of new powerful WL receivers for UAV CNPC links, anti-collisions AIS systems and for FBMC-OQAM networks in particular.
... A The main objective of the blind source separation (BSS) is to estimate a set of unknown source signals s(t) using only the mixed signals obtained by a series of sensors x(t), also called "observations" Fig. 1 [7]- [8]. ...
This paper presents a comparison between twoblind source separation methods: the first one based on a microcontroller board such as Arduino Due by the RTW (Real-Time Workshop) integrated into Matlab Simulink and the second one based on a DSP board (TMS320c6713). This study compared the space occupied, the execution speed and the separation index between the two platforms Arduino and DSP. The separation was practically simulated on the Arduino Due and TMS320c6713 board by two signal generators and an oscilloscope.
... A The main objective of the blind source separation (BSS) is to estimate a set of unknown source signals s(t) using only the mixed signals obtained by a series of sensors x(t), also called "observations" Fig. 1 [7]- [8]. ...
This paper presents a comparison between twoblind source separation methods: the first one based on a microcontroller board such as Arduino Due by the RTW (Real-Time Workshop) integrated into Matlab Simulink and the second one based on a DSP board (TMS320c6713). This study compared the space occupied, the execution speed and the separation index between the two platforms Arduino and DSP. The separation was practically simulated on the Arduino Due and TMS320c6713 board by two signal generators and an oscilloscope.
The world of maritime transport is a significant part of the global economy. Traffic control relies, among other means, on the Automatic Identification System (AIS) device, which reports dynamic and fixed data. Vessels use advanced cyber capabilities to falsify data that the AIS transmits and impersonate an innocent ship while carrying out illegal activity, especially the vessel’s location data, without control. A significant part of the work done to find false AIS reports looks for location reports. Each AIS device uses a transceiver based on SOTDMA (Self-Organized TDMA) and determines its transmission schedule (slot) based on data link traffic history and an awareness of other stations’ possible actions. The SOTDMA protocol was developed in the late 1990s and does not have built-in security features, which leaves communication networks vulnerable to cyber threats such as eavesdropping, tampering with data, unauthorized access, and cyber-attacks. This Protocol is widely used in wireless communication systems where no central authority manages the communication between nodes, dynamically adjusts to changes in network topology, and nodes can come and go at any time. This article reviews the cybersecurity challenges in the AIS protocol used in vessels. Most of those challenges imply a variety of areas using SOTDMA protocols like Wireless Sensors (WSNs), Mobile (MANETs), Military, Disaster Relief Networks, Healthcare Monitoring Systems, Industrial Automation Systems, Vehicle-to-Vehicle (V2V) Communication Networks, Wireless Metropolitan Area Networks (WMANs), Internet of Things (IoT) Networks, and Machine-to-Machine (M2M) Communication Networks.
