Nowadays, the Internet of Things (IoT) can be considered an important technological revolution related to smart cities, smart homes, smart factories and smart ports implementations As the presence of smart sensing systems in ports becomes a reality, different operation areas are working today in automatic mode Examples of challenging projects related to smart ports in the IoT era can be found from Europe to Asia, to Australia, and to North America; in all of these new architecture implementations, sensing technologies play a key role This paper highlights the main requirements and the key ideas for each ports, sensing solution and also the challenges related to the calibration and testing of distributed sensing systems associated with the main equipment that compose the world largest ports, such as quayside cranes, automated guided vehicles for container handling and yard cranes Details of the architecture and operations and sensing systems for smart ports are described Communication standards for smart ports are discussed, and smart ports implementation examples regarding structural health monitoring are considered Conclusions and future research opportunities in the IoT era are addressed in the final section of the paper

Internet of things for smart ports: Technologies and challenges

Future driver assistance systems will rely on accurate, reliable and continuous knowledge on the position of other road participants, including pedestrians, bicycles and other vehicles. The usual approach to tackle this requirement is to use on-board ranging sensors inside the vehicle. Radar, laser scanners or vision-based systems are able to detect objects in their line-of-sight. In contrast to these non-cooperative ranging sensors, cooperative approaches follow a strategy in which other road participants actively support the estimation of the relative position. The limitations of on-board ranging sensors regarding their detection range and angle of view and the facility of blockage can be approached by using a cooperative approach based on vehicle-to-vehicle communication. The fusion of both, cooperative and non-cooperative strategies, seems to offer the largest benefits regarding accuracy, availability and robustness. This survey offers the reader a comprehensive review on different techniques for vehicle relative positioning. The reader will learn the important performance indicators when it comes to relative positioning of vehicles, the different technologies that are both commercially available and currently under research, their expected performance and their intrinsic limitations. Moreover, the latest research in the area of vision-based systems for vehicle detection, as well as the latest work on GNSS-based vehicle localization and vehicular communication for relative positioning of vehicles, are reviewed. The survey also includes the research work on the fusion of cooperative and non-cooperative approaches to increase the reliability and the availability.

https://www.mdpi.com/1424-8220/17/2/271/pdf

Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles.

To improve the seamless navigation ability of an integrated Global Positioning System (GPS)/inertial navigation system in GPS-denied environments, a hybrid navigation strategy called the self-learning square-root- cubature Kalman filter (SL-SRCKF) is proposed in this article. The SL-SRCKF process contains two innovative steps: 1) it provides the traditional SRCKF with a self-learning ability, which means that navigation system observations can be provided continuously, even during long-term GPS outages; and 2) the relationship between the current Kalman filter gains and the optimal estimation error is established, which means that the optimal estimation accuracy can be improved by error compensation. The superiority of the proposed SL-SRCKF strategy is verified via experimental results and prominent advantages of this approach include: 1) the SL-SRCKF comprises two cycle filtering systems that work in a tightly coupled mode, and this allows more accurate error correction results to be obtained during GPS outages; 2) the system's error prediction ability is effectively improved by introducing a long short-term memory, which provides much better performance than other neural networks, such as random forest regression or the recursive neural network; and 3) under different (30, 60, and 100 s) GPS outage conditions, the long-term stability of SL-SRCKF is much better than that of other error correction approaches.

Seamless GPS/Inertial Navigation System Based on Self-Learning Square-Root Cubature Kalman Filter

Industry 4.0 in the port and maritime industry: A literature review

The performance of an inertial navigation system (INS) and global positioning system (GPS) integrated navigation system is reduced during GPS outages. To bridge GPS outages, a fusion methodology to provide pseudo GPS position information is proposed. The methodology consists of two parts, empirical mode decomposition threshold filtering (EMDTF) and a long short-term memory (LSTM) neural network. The EMDTF eliminates the noise in inertial sensors and provides more accurate data for subsequent calculations. The LSTM uses the current specific forces and angular rates to predict the pseudo GPS position during GPS outages. To evaluate the effectiveness of the proposed methodology, numerical simulations and real field tests are employed. Compared with the traditional artificial neural networks, the results illustrate the proposed methodology can significantly improve the navigation accuracy during GPS outages and the model is simpler.

/pdf/a-fusion-methodology-to-bridge-gps-outages-for-ins-gps-2yolgtgprn.pdf

A Fusion Methodology to Bridge GPS Outages for INS/GPS Integrated Navigation System

How to achieve reliable and accurate positioning performance using low-cost sensors is one of the main challenges for land vehicles. This paper proposes a novel fusion positioning strategy for land vehicles in GPS-denied environments, which enhances the positioning performance simultaneously from the sensor and methodology levels. It integrates multiple complementary low-cost sensors not only incorporating GPS and microelectromechanical-based inertial measurement unit, but also a “virtual” sensor, i.e., a sliding-mode observer (SMO). The SMO is first synthesized based on nonlinear vehicle dynamics model to estimate vehicle state information robustly. Then, a federated Kalman filter (FKF) is designed to fuse all sensor information, which can easily isolate and accommodate such sensor failures as GPS ones due to its decentralized filtering architecture. Further, a hybrid global estimator (HGE) is constructed by augmenting the FKF with a grey predictor, which has the advantages of dealing with the systems with uncertain or insufficient information. The HGE works in the update mode when there is no GPS failure, whereas it switches to the prediction mode in case of GPS outage to realize accurate and reliable positioning. The experimental results validate the effectiveness and reliability of the proposed strategy.

Qimin Xu

Papers

A Reliable Fusion Positioning Strategy for Land Vehicles in GPS-Denied Environments Based on Low-Cost Sensors