Signal analysis of GMSK modulation-based CubeSat automatic identification system receiver

Assessing the fishing footprint using data integrated from different tracking devices: Issues and opportunities

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.

Aimed at addressing the problem of carrier synchronization for satellite-based Automatic Identification System (AIS) signals, a novel data-aided carrier frequency offset estimation method in the intermediate frequency is proposed in this paper. AIS signals use Gaussian Minimum Shift Keying (GMSK) for transmission, and a nonlinearity of the GMSK signal is shown to be a sine wave with frequency related to the carrier frequency offset. Based on that, this method constructs an auxiliary function with the available symbols to avoid the influence of the modulated phase information, and it estimates the frequency offset due to Doppler shift with a new interpolated FFT method. Moreover, this method performs better when the signals are filtered by band pass filter even through the bandwidth is as low as the 1/3 nominal bandwidth. Computer simulations are used to assess the synchronizer performance on AWGN channels.

This technical service has been carried out in response to a request from OSPAR concerning the improvement of automatic identification system (AIS) and vessel monitoring system (VMS) data for underwater noise assessment purposes across the OSPAR region.ICES conducted a technical service through the Workshop on Integrating Marine Spatial Data for Noise Assessments (WKIMSDNA), held online on 12 June 2025. The workshop brought together experts in AIS and VMS data, underwater noise monitoring, and data management to examine current data availability, evaluate quality and processing requirements, explore integration opportunities, and develop recommendations for improving spatial data collection and coordination across the OSPAR region.The workshop addressed four questions: (1) what vessel tracking data currently exist and how they can be accessed, (2) what quality issues affect these data and how they are processed, (3) whether different data sources can be effectively integrated, and (4) what improvements are needed for noise assessment applications.Analysis revealed that while substantial data resources exist across the region, they are fragmented across multiple national providers without comprehensive regional coordination. The need for reliable AIS data extends beyond underwater noise to multiple MSFD indicators including greenhouse gas emissions and maritime traffic intensity, with the six-yearly assessment cycle requiring consistent, comparable shipping information across regions. Currently there is no coordinated, sustainably funded mechanism for acquiring these datasets, representing a structural challenge affecting multiple descriptors simultaneously. Coverage by AIS extends from 2012 onwards, but significant gaps exist in some coastal areas. Use of VMS data collected by ICES remains restricted to specific advice purposes, with notable gaps in coverage affecting Norwegian, Portuguese, and Faroese regions. Comparative analysis across three marine regions demonstrated that different data sources capture complementary aspects of vessel activity, suggesting integration could improve coverage. However, technical challenges around data quality, format standardization, and avoiding double-counting currently limit this potential. The workshop also identified complementary non-AIS detection technologies (synthetic aperture radar (SAR), optical and multispectral imagery, radio frequency (RF) detection, and coastal radar) that could supplement conventional monitoring in areas with coverage limitations. Additionally, platforms such as Global Fishing Watch offer free research access to processed AIS datasets and may provide alternative pathways for data acquisition.Priority recommendations include extending terrestrial receiver coverage in areas with known gaps; investigating appropriate institutional hosting arrangements and governance structures for integrated data infrastructure; developing standardized data formats and processing methodologies applicable across providers; establishing protocols for effective integration while avoiding double-counting vessels across AIS, VMS, and emerging data sources; establishing harmonised legal frameworks to support cross-border data sharing whilst respecting national sovereignty and commercial interests; developing sustainable funding models for ongoing data collection, processing, and integration activities; and preparing for forthcoming EU regulations that require tracking of all fishing vessels, which will significantly expand data availability but necessitate enhanced processing capabilities. The workshop also recommended developing specific metrics for measuring integration success and establishing regular evaluation cycles to assess progress.

Maritime traffic monitoring using a space-based AIS receiver

The present paper deals with design and development of a generalized parametrizable modulator (GPM) that can perform Gaussian minimum shift keying (GMSK) modulation and Quadrature Phase Shift Keying (QPSK) modulation in a reconfigurable baseband modulator for software defined radio (SDR) architecture. GMSK is the underlying modulation scheme for the Global System for mobile (GSM) standard, while QPSK technique is the basic modulation scheme for Code Division Multiple Access (CDMA) standard. Although GMSK is an inherently non-linear modulation technique, the present work uses a linearly approximated GMSK technique to take the advantage of the simplicity of the filter structure. The normalised error in amplitude as a function of modulation index and bandwidth time product has been simulated. The proposed GPM is capable of working at a maximum data rate of 13.75 Mbps, thus satisfying the requirements of almost all 2G and 3G air interface standards. The GPM can perform QPSK using four pulse shaping filters, two Raised Cosine (RC) filters and two Root Raised Cosine (RRC) filters. The proposed GPM architecture is implemented on a Spartan 3E starter kit.

Analyzing current and anticipated uses of ocean and coastal areas based on scientific understanding and information is quite critical to achieve a successful Marine Spatial Planning (MSP). One of key elements in ocean and coastal use is navigation and thus vessel traffic density should be included as part of MSP. One of emerging techniques to quantify ship traffic density is to analyze Automatic Identification System (AIS) and Vessel Monitoring System (VMS) data for a designated area. This study presents a case study conducted in New England area, USA, where the ship traffic density was estimated by analyzing AIS/VMS data. The results of ship traffic density were used in MSP (e.g., Northeast Ocean Data Portal), which were later used by one of stake holders, U.S. Department of Interior, Bureau of Ocean Energy Management when they issued wind lease blocks in outer continental shelf off Massachusetts.

Fishery closures are widely used to promote the sustainability of fish stocks. Fishery agencies typically have very little data relevant to planning closure enforcement actions and evaluating the effectiveness of closures, due in part to the vast expanse and remote nature of many closures. In some cases the effectiveness of closures can be evaluated using data from GPS based beacons, such as Automatic Identification System (AIS) or Vessel Monitoring Systems (VMS) installed on fishing boats. In fisheries where few boats are equipped with AIS or VMS, the rating of closures relies on other data sources capable of detecting or inferring fishing activity. One such source comes from low light imaging data collected by the NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS), which can detect fishing boats using lights to attract catch. This is a widely used practice in Asia and several other regions. NOAA has developed an automatic system for reporting the locations of VIIRS boat detections with a nominal four hour temporal latency. VIIRS boat detection alerts are running for more than 900 fishery closures in the Philippines, with email and SMS transmission modes. These alerts are being actively used in the Philippines to plan enforcement actions and there is a growing list of apprehensions that occurred based on tip-offs from VIIRS. The VIIRS boat detection archive extends back to April 2012. A VIIRS closure index (VCI) has been developed to rate the effectiveness of closures on monthly increments in terms of a percentage. The VCI analysis was performed on three types of closures: an ad hoc fishery closure associated with a toxic industrial discharge, a seasonal fishery closure and a permanent closure in restricted coastal waters. The VCI results indicate that it is possible to rank the effectiveness of different closure, year-to-year differences in compliance levels, and to identify closure encroachments which may warrant additional enforcement effort.

In the aftermath of the El Faro sinking, one of the NTSB recommendations was for NOAA to explore increasing the reception of weather data from ships in order to improve the weather forecast products that they distribute. Currently NOAA runs the Voluntary Observing Ship (VOS) program, where ships voluntarily submit weather observations to NOAA. However, this data is submitted infrequently, typically four times per day and is a manual process. One method being explored to automate and increase the frequency of data submittal is by using the Automatic identification System (AIS). Most ships (SOLAS-class vessel for example) have a Class A transponder due to mandatory carriage requirements. Many vessels that are not required, also have AIS transponders (either a Class A or B). All of these AIS transponders can be used to transmit AIS message 8 (broadcast binary message) simply by sending the transponder the appropriate NMEA sentence (BBM). The weather data can be embedded in an AIS message 8 and transmitted by the ship automatically. This transmission will be received by terrestrial receivers (when in range) or by satellite AIS receivers. The AIS weather data can then be converted into the appropriate format and forwarded to the NOAA weather database automatically. By leveraging this installed base of AIS transmitters, the volume of weather data being sent to NOAA could be greatly increased. The USACE and MARAD have been exploring the feasibility of this concept. An initial proof-of-concept was tested on the Cape Wrath in Baltimore in October 2018. After this successful demonstration, a prototype was installed on the T/S Kennedy during her training cruise Jan-Feb 2019. More recently, systems were installed on three commercial vessels operating in the Pacific Ocean. This report will discuss the concept, the demonstrations, and the results to date including the efficacy of the various ASM formats.

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.

This paper presents a shipborne vessel monitoring system, where position reports of the shipborne vessel are transmitted with the use of LoRa technology, and is received by a receiver module, where the position data is parsed and displayed for the end-user's consumption. This work provides an alternative ship-tracking system to the existing Automatic Identification System (AIS), where the packet encapsulation used for AIS devices will also be used for the LoRa-based Vessel Monitoring System.

This is a combination of YOLO (you only look once) target recognition detection algorithm and AIS (Automatic Identification System) automatic ship identification system to assist vessels in inland navigation to carry out vessels and obstacle avoidance. This system proposes a ship-assisted collision avoidance system for ship collision accidents caused by a narrow inland waterway and complex navigation channel. The system can process the image and video information acquired by the high-definition camera mounted on the ship, and frame and mark the ships in the current sea area, and combine the AIS system to realize the visualization of the ship information and improve the global view. The experiment shows that the system can effectively identify and frame the ship and realize the visualization of AIS information, and the transformed image and video information can be used for decision-making by the ship's driver.

The AIS (Automatic Identification System) became a standard under the International Convention for the Safety of Life at Sea (SOLAS) in December 2001. Using the AIS transponder, the vessel will report its position, identity heading and other information to other ship traffic in the same area. The AIS is a significant contribution to the control and safety of shipping. Transport Canada (TC) is a pioneer internationally in using the AIS (R4A airborne AIS transponder) integrated with the remote sensing system (MSS 6000 from Swedish Space Corporation) on board their aircraft, in order to add the information on ship identity and voyage to the information from other sensors. The AIS integration provides a quantum leap in the effectiveness of the surveillance mission. In the Canadian system integration, detailed vessel information obtained through the AIS system is displayed and correlated with information received from other sensors. All information is also presented together with a high-resolution digital map including nautical chart data. The AIS provides the operators with vessel voyage and identity information. It is very useful for vectoring the aircraft to areas where ships have been reported. The advent of AIS simplifies surveillance procedures and makes each hour of surveillance more productive. During last year TC reported that over 9700 vessels were over flown. It is anticipated that over 20,000 AIS targets will be acquired using AIS in 2007–2008. The experience from Canada also indicates an outstanding VHF range. During missions, vessels have been identified as far away as 200 nautical miles (n.m.) from the aircraft. The AIS information together with other sensor images gives a comprehensive image that will dramatically improve the chance to tie a ship to a detected illegal oil spill. Receiving ship identity information will enable aircrews to conduct covert operations and contact the suspect ship from far standoff distances. This will also act as a deterrent to potential polluters. Integrating AIS within the operational requirements of surveillance programs, dedicated to minimizing the adverse effects of shipping on the marine environment, will make a significant contribution towards improving the effectiveness of surveillance missions.

Vessel Monitoring System (VMS) play a major role in safety navigation. In most cases they are based on two data sources, namely Automatic Identification System (AIS) and Automatic Radar Plotting Aids (ARPA). Integration of data obtained from these sources is an important problem, which needs to be solved in order to ensure the correct performance of a given VMS. In this paper basic functions which should be implemented in a tracked objects position data deduplication module are described. An example based on their practical implementation is also presented.

Maritime traffic pattern extraction is a fundamental and crucial factor for maritime surveillance and anomaly detection. Emerging technologies like Automatic Identification System (AIS) provides multi-dimensional data which is used to construct a maritime traffic model. In this paper, we propose a framework of maritime traffic pattern extraction from vessel AIS information, which learns a traffic pattern using an unsupervised technique, and can be applied on historical Automatic Identification System data. AIS data is a kind of spatio-temporal data that contains information of location data, as well as time stamps. In this way, traffic pattern is described by AIS data. Furthermore, we conduct a simulation experiment that extracts traffic pattern from the AIS data through the unsupervised technique. The proposed framework takes advantage of AIS data, which is a type of the spatiotemporal data that consists of vessel motion information, to perform the experiment. The result shows that the unsupervised framework converts useful information from raw AIS data to effective traffic pattern. The proposed method strongly supports the further research on maritime traffic pattern extraction of AIS data. Besides, an overview of the framework and the unsupervised technique for high-level maritime situation awareness is presented.