Frequency Offset Estimation of Satellite-Based AIS Signals Based on Interpolated FFT

In order to verify the feasibility of atmospheric duct inversion method using automatic identification system signal in the actual sea area, a sea experiment was carried out. The signal acquisition system of portable high sensitivity civil automatic identification system is developed, and the receiving and processing process of automatic identification system signal is analysed. The sea experiment scheme and main experiment equipment are given, and the collected experiment data are processed and analysed. Finally, the method of atmospheric duct inversion using automatic identification system signal is verified by the measured data. Atmospheric duct inversion is an inverse problem, which requires the use of various optimisation algorithms to optimise the duct parameters in order to obtain the characteristic parameter information of the duct. In this paper, the Lévy flight quantum‐behaved particle swarm optimisation algorithm and the deep learning algorithm are respectively used to invert the atmospheric duct parameters. The results show that there is little difference between the inverted duct parameters and the true duct parameters, which verifies the feasibility of using the automatic identification system signal to invert the atmospheric duct.

With the rapid development of global industry, AIS (automatic identification system) has become an important tool for ship management and safety monitoring. In order to better display the frequency distribution of AIS signals, this paper graphically displays the data in the AIS signals, allowing users to fully understand the data in the AIS signals, including ship type distribution, speed range, and signal frequencies in different areas. It uses Kafka and Flink technology to receive, clean and analyze the sent data, and finally stores it in MySQL and Redis data. It reads the data through Spring Boot + MyBatis, and uses Vue + Echarts chart tool and AutoNavi Map API for display and analysis. To gain an in-depth understanding of its importance and application prospects in the field of epidemics.

Global Navigation Satellite Systems (GNSS), today the primary mean for Position, Navigation and Timing (PNT) information on-board of a vessel, are highly vulnerable to unintentional and intentional interference like jamming or spoofing. Therefore, maritime community seeks for an alternative system to compensate this thread and follow the vision of the eNavigation strategy of International Maritime Organization (IMO), a safe navigation of vessels from berth to berth. The only alternative terrestrial backup for GNSS is the enhanced LOng-RAnge Navigation (eLORAN). However, that system will probably never reach global coverage because a substantial part of LORAN stations was shutdown in the years 2010 to 2015. There are only certain regions where it is still usable as well as Chayka, the Russian system. Today, in case of a temporal loss of GNSS no system can provide absolute positioning in areas with increased necessity for reliable navigation data provision on a global scale. One candidate for a terrestrial backup system is R(anging)-Mode which utilizes existing maritime radio infrastructure for the broadcast of modified signals, which allow distance estimation to the transmitter. First feasibility studies were done in the North Sea, in China and in South Korea. The Baltic Sea is due to the high number of vessels along the shipping routes and its sensitive marine ecosystem a suitable region for the implementation and testing of R-Mode firstly as a terrestrial backup system to GNSS. It will supplement navigation information in order to reduce the risk of collisions and groundings. New marine applications that require a continuous provision of position information will be made possible at sea, in coastal areas and in harbours. A consortium of 12 research institutions, administrations and industry of Germany, Poland, Sweden and Norway is currently engaged to setup the first R-Mode testbed, which utilizes IALA (International Association of Marine Aids to Navigation and Lighthouse Authorities) beacons and Automatic Identification System (AIS) land stations for the broadcast of R-Mode enabled signals. This will be organized in the southern Part of the Baltic Sea until 2020 funded by EU within the scope of R-Mode Baltic Project. Beside this main goal of the Project (back-up to GNSS) further research and development activities will be conducted. This comprises the selection of most suitable way of R-Mode service implementation on IALA beacon and AIS signals without disruption of the legacy service. Further the selection of best algorithms for positioning either with only one type of R-Mode signals or with both is part of the research activities. In addition a concept development and test installation for the time synchronization of a transnational network of R-Mode stations which is currently maintained by independent national maritime administrations as well as the development of first prototypes of R-Mode receiver capable to cope with modified IALA beacons and/or AIS signals as well as R-Mode base station hardware for R-Mode signal generation is planned. Finally, with R-Mode enabled PNT data processing and Pilot Plug Unit the first two R-Mode applications will be made available. All this activities will be done in close contact with the maritime administrations, IALA, and maritime users. It is anticipated, that R-Mode will be a terrestrial backup system that can fulfil their need for robust and reliable PNT data for safe navigation at sea. Beside a general description of the project first results of a common coverage prediction for the IALA beacon and AIS communication channels for the Baltic Sea region will be estimated and presented. Latter shows that especially the relevant Baltic Sea region with its narrow passages of coastlines in the North (Denmark, Norway and Sweden) and in the South (Germany, Poland) is already well equipped with a dense network of IALA beacons and AIS stations in the vicinity of vessel routes. In case of positive Project results, upgrading all of them to R-Mode service level these would allow positioning in the entire region. Furthermore, the output of the requirement phase study on the R-Mode system and its components is presented. This was done starting from relevant documents of IMO and IALA requirements for a terrestrial backup system were extracted and next further broken down for specific components of the R-Mode system. This shall give guidance throughout the project when selecting appropriate algorithms, methods and technical solutions.

As Gaussian minimum shift keying (GMSK) modulation scheme is adopted for wireless body area network (WBAN) applications, simple and noise-robust synchronisation algorithms for GMSK are desired to meet the limited resources of power, storage and size in WBAN. However, the current synchronisation algorithms for GMSK are either too complicated or have unsatisfactory performance for low signal-to-noise ratio (SNR), especially for coded GMSK systems. The authors will propose the synchronisation algorithms for general GMSK systems with three unknown synchronisation parameters, namely, timing, frequency and phase offsets estimation. By incorporating noise/interference-reduction methods, the authors propose low-complexity synchronisation algorithms for GMSK that can achieve satisfactory performance in low SNR region. Furthermore, their new feedforward timing and frequency recovery algorithms can provide much better performance than those in the literature by using reliability-measuring mechanism for timing estimation and two-phase frequency estimation. The data-aided Cramer–Rao bounds (CRBs) for the joint estimation of GMSK synchronisation parameters are also derived. The numerical results show that the performance of their proposed synchronisation algorithms is close to the CRBs. The overall bit error rate performance for both uncoded and Bose-Chaudhuri-Hocquenhem BCH-coded GMSK systems are examined. The performance loss owing to synchronisation errors can be reduced to about 1 dB with their proposed algorithms.

The vessel monitoring system at the sea which mainly uses Automatic Identification System (AIS) technology will be more effective if it is combined with the satellite technology. This id due to the satellite can cover reach thousands of kilometers when orbiting in the space. Unfortunately, the process of receiving AIS signals from the transmitter has the problem of very low received power that reaches up to −102.07dBm. Therefore the simulation design of CubeSat AIS receiver is required for analyzing received signal prior the deployment. This paper present the signal analysis of CubeSat AIS receiver constructed based on Gaussian Minimum Shift Keying (GMSK) modulation. The AIS receiver as a payload of CubeSat is intended to operate at frequency of 162.025 MHz. It is designed to receive AIS signals in the orbit at an altitude of 650km. The GMSK demodulation of AIS receiver is simulated using an electronic circuit simulation software. The simulation result shows that the CubeSat AIS receiver can receive AIS signals under appropriate conditions satisfying link budget calculation.

This paper addresses the problem of error correction in a multi-user trellis coded system in the presence of bit stuffing. In particular, one considers the situation in which automatic identification system (AIS) signals are received by a satellite. The proposed receiver uses a cyclic redundancy check (CRC) for error correction. A Viterbi algorithm based on a so-called extended trellis is developed. This trellis is defined by extended states composed of a trellis-code state and a CRC state. Moreover, special conditional transitions are defined in order to take into account the possible presence of bit stuffing. The proposed algorithm was first developed in a single-user context. It is generalized in this paper to a multi-user scenario by designing an interference mitigation method. This method allows one to derive a demodulation algorithm whose complexity is almost identical to that obtained in the single-user context. Some performance results are presented in the context of AIS and compared with results provided by existing techniques.

We study the whole process of using Low Earth Orbit (LEO) satellite constellation to localize Automatic Identification System (AIS) emitter, especially the methodologies of intelligently improving and modifying LEO satellite constellation positioning algorithm based on AIS data. In this vein, we design a simulation system for cooperative localization of vessels based on the AIS signals they emit, with a LEO satellite constellation. We implement key modules such as vessel motion simulation, AIS signal simulation, satellite-to-ground link simulation, localization algorithm module and system visualization. The simulation results of single satellite and multi-satellites localization systems verify the effectiveness of the proposed system.

In order to use the existing automatic identification system (AIS) to provide additional navigation and positioning services, a complete pseudorange measurements solution is presented in this paper. Through the mathematical analysis of the AIS signal, the bit-0-phases in the digital sequences were determined as the timestamps. Monte Carlo simulation was carried out to compare the accuracy of the zero-crossing and differential peak, which are two timestamp detection methods in the additive white Gaussian noise (AWGN) channel. Considering the low-speed and low-dynamic motion characteristics of ships, an optimal estimation method based on the minimum mean square error is proposed to improve detection accuracy. Furthermore, the α difference filter algorithm was used to achieve the fusion of the optimal estimation results of the two detection methods. The results show that the algorithm can greatly improve the accuracy of pseudorange estimation under low signal-to-noise ratio (SNR) conditions. In order to verify the effectiveness of the scheme, prototypes containing the measurement scheme were developed and field tests in Xinghai Bay of Dalian (China) were performed. The test results show that the pseudorange measurement accuracy was better than 28 m (σ) without any modification of the existing AIS system.

We consider joint estimation of carrier frequency offset and channel impulse response (CIR) for orthogonal frequency division multiplexing (OFDM) systems with pilot symbols and virtual subcarriers (VCs). We derive the receive-signal correlation structure due to the pilots and VCs, give the evidence of joint multivariate Gaussian distribution of the received samples, and derive an approximate maximum likelihood (ML) frequency offset estimator. We also derive the asymptotic mean-square error (MSE) and an approximate Cramer-Rao bound (CRB) and establish the asymptotic unbiasedness. Without pilots, in high signal-to-noise ratio, our estimator is equivalent to Liu and Tureli's estimator with Nv virtual carriers. When the pilot number (Np) is greater than the channel length L, our estimator acts as a subspace-based estimator with Nv + Np - L virtual carriers. A decision-directed joint ML estimator is derived to iteratively update the estimates of frequency offset, data symbols and CIR. The optimal pilot and virtual carrier placement strategies are also discussed. The resulting decision-directed joint estimator performs within 0.8 dB of the ideal case even when the frequency offset is as large as 20%

In this letter, we propose and experimentally investigate a training-aided iterative frequency offset estimation (IFOE) method, which can track dynamic frequency offset (FO) drift in coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems. In contrast to existing FO estimation algorithms that compute specific FO values, in IFOE, only the sign value of FO is judged and recorded without any phase calculation. The estimated FO is updated once for every data block based on FO signs obtained from multiple previous blocks, which helps combat noise and allows the shortening of the employed training symbol. We experimentally investigate the performance of IFOE in a 160-km CO-OFDM system. The results show that IFOE achieves superior bit error rate performance to a one-time FO estimator (without FO tracking) and approaches the performance of multiple separate FO estimators that use longer training symbols.

We evaluate the performance improvement in orthogonal frequency-division multiplex access (OFDMA) achievable with cooperative relaying in the presence of frequency offsets. The relays can operate in either the amplify-and-forward (AF) or decode-and-forward (DF) mode. In each transmission, node S has M relays when transmitting its own signal, and it also helps the other M relays transmit their signals. The total power used to transmit each symbol, including the power consumed in S and the relays, is kept constant. The outage information rates of the proposed cooperative transmission scheme are first derived. The performance improvement in the frequency offset estimation by using cooperative relay is then analyzed. Channel estimation in a cooperative OFDMA network in the presence of frequency offsets is also evaluated, and the pairwise error probability (PEP) of orthogonal space-time coding in cooperative OFDMA due to both the frequency offset and channel estimation errors is derived. Numerical results illustrate the superior performance of the proposed scheme over the conventional transmission with regard to outage information rate, frequency offset estimation error and the PEP.

A time domain carrier frequency offset estimation method is proposed for OFDM systems. The essence of the proposed method is based on the correlation features of short and long synchronisation symbols, which appear at the beginning of each frame as the training sequence. The noise effect on the estimation is minimised by averaging over several estimations. The time-domain operations and correcting bigger carrier frequency offsets compared to the work by P.H. Moose (see IEEE Trans. Commun., vol.42, no.10, p.2908-14, 1994) are the important features of this study. The proposed method has a lower complexity and better BER performance for higher frequency offset values compared to the referenced studies.

In this paper a frequency offset estimation scheme is presented for coarse frequency offset estimation of multi-band OFDM (MB-OFDM) based ultra-wideband (UWB) systems considering different normalized carrier frequency offsets and different channel frequency responses in three different bands. The Cramer Rao Lower Bound (CRLB) for variance of the estimated frequency offset is derived and compared with the simulated results of the proposed scheme. Performance of the MB-OFDM system in terms of the Bit Error Rate (BER) with suggested frequency offset estimator is reported. The algorithm is tested by computer simulation over 100,000 noisy realizations in each of the UWB channel models (CM), CM1, CM2, CM3, and CM4 as suggested by IEEE 802.15 channel modeling sub-committee.

Automatic Modulation Classification (AMC) is a critical technology in the field of non-cooperative communication. Carrier frequency offset is an inevitable impairment factor in communication systems, and it significantly affects the performance of AMC, especially when the range is large. Therefore, frequency offset estimation and compensation are essential for automatic modulation classification. To address this issue, this paper proposes an innovative cascading network based on deep learning, primarily comprising pre-classification and compensation mechanisms. With the pre-classification mechanism, we categorize the eleven unknown modulation formats into three major classes: PSK, FSK, and QAM. Subsequently, we utilize deep neural networks to estimate the frequency offset of signals within these major classes, followed by performing the corresponding signal compensation. The experimental results demonstrate that the pre-classification mechanism effectively reduces the error in frequency offset estimation, minimizing the error <Formula format="inline"><TexMath><?TeX $\log MSE$ ?></TexMath><File name="a00--inline1" type="gif"/></Formula> (mean-square error) to as low as -3.9. Under various signal-to-noise ratio (SNR) ranges, the classification accuracy achieved through pre-classification and compensation is up to 14% higher than the accuracy without this method. Specifically, at SNR = 10 dB, it can reach 99%. Moreover, the proposed scheme is robust and performs stably across different frequency offsets range, outperforming other existing network classification schemes to some extent.

The growth of applications relying on the Automatic Identification System (AIS) brings to light the need of verification and trustworthiness of AIS data. AIS data are vulnerable to false or missing declarations requiring the implementation of measures increasing the trustworthiness of AIS messages. One of the possible solution is the comparison between the reported vessel position and the position estimated using for example radiolocalization techniques. The usage of AIS signals for radi-olocalization has been already demonstrated, however the main limitation of this technique is the limited coverage. In order to extend the coverage area, data from different networks should be used. In this case an inter-network time bias has to be considered. This offset is due to the lack of standardization about the specific synchronization technique, hence each AIS receiver manufacturer can implement time extraction using different algorithms. If devices from the same manufacturer are employed, the offset between the receivers is negligible, whereas if dissimilar receivers are used, the offsets could prevent the correct outcome of the classical radiolocalization techniques. In order to fill in this gap, a “Multi-Network (MN) Time Difference Of Arrival (TDOA)” algorithm has been designed. The approach extends the traditional TDOA technique, by working out any synchronization problem. The algorithm has been tested in simulated scenarios. From the results, the effects of the inter-network bias have been investigated and the benefits of the inclusion of heterogeneous stations, either non-synchronized or with different accuracies, have been shown. Moreover, the maximum tolerable anachronism between the receivers included in the network has been evaluated.