Satellite technology in the maritime world : applications and implications

This dissertation is a study of the widespread utilization of satellite technology in the maritime world, with particular reference to the applications and implications of communication, navigation and remote sensing satellites to shipping and other maritime related fields. A brief look is taken at the general background of satellite technology, including the growth and development and basic techniques as well as categorization of applications. This is followed by a more detailed view of the three different areas of the applications of satellite technology, namely maritime communications, marine navigation and maritime meteorology and oceanography. The two well-known satellite-based systems, the INMARSAT System and the Global Positioning System, are both widely used by the maritime community and thus emphasized in this study. Apart from a brief description of those two systems, their applications are examined, their impact on the marine industry is discussed and their future development is investigated in order to obtain an appreciation of their role in the present maritime world. The applications of remote sensing satellites in marine meteorology and oceanography are also discussed. In addition to the above, the impact of the increasing use of satellite technology on maritime education and training is investigated. The concluding chapter is a summary of the main points discussed in the core chapters, which reflect the author’s viewpoin

Lane Discovery in Traffic Video

Video sensing has become very important in Intelligent Transportation Systems (ITS) due to its relative low cost and non-invasive deployment. An effective ITS requires detailed traffic information, including vehicle volume counts for each lane in surveillance video of a highway or an intersection. The multiple-target, vehicle-tracking and counting problem is most reliably solved in a reduced space defined by the constraints of the vehicles driving within lanes. This requires lanes to be pre-specified. An off-line pre-processing method is presented which automatically discovers traffic lanes from vehicle motion in uncalibrated video from a stationary camera. A moving vehicle density map is constructed, then multiple lane curves are fitted. Traffic lanes are found without relying on possibly noisy tracked vehicle trajectories

A Hybrid Adaptive Compressive Sensing Model for Visual Tracking in Wireless Visual Sensor Networks

The employ of Wireless Visual Sensor Networks (WVSNs) has grown enormously in the last few years and have emerged in distinctive applications. WVSNs-based Surveillance applications are one of the important applications that requires high detection reliability and robust tracking, while minimizing the usage of energy to maximize the lifetime of sensor nodes as visual sensor nodes can be left for months without any human interaction. The constraints of WVSNs such as resource constraints due to limited battery power, memory space and communication bandwidth have brought new WVSNs implementation challenges. Hence, the aim of this paper is to investigate the impact of adaptive Compressive Sensing (CS) in designing efficient target detection and tracking techniques, to reduce the size of transmitted data without compromising the tracking performance as well as space and energy constraints. In this paper, a new hybrid adaptive compressive sensing scheme is introduced to dynamically achieve higher compression rates, as different datasets have different sparsity nature that affects the compression. Afterwards, a modified quantized clipped Least Mean square (LMS) adaptive filter is proposed for the tracking model. Experimental results showed that adaptive CS achieved high compression rates reaching 70%, while preserving the detection and tracking accuracy which is measured in terms of mean squared error, peak-signal-to-noise-ratio and tracking trajectory

Innovative Solutions for Navigation and Mission Management of Unmanned Aircraft Systems

The last decades have witnessed a significant increase in Unmanned Aircraft Systems (UAS) of all shapes and sizes. UAS are finding many new applications in supporting several human activities, offering solutions to many dirty, dull, and dangerous missions, carried out by military and civilian users. However, limited access to the airspace is the principal barrier to the realization of the full potential that can be derived from UAS capabilities. The aim of this thesis is to support the safe integration of UAS operations, taking into account both the user's requirements and flight regulations. The main technical and operational issues, considered among the principal inhibitors to the integration and wide-spread acceptance of UAS, are identified and two solutions for safe UAS operations are proposed: A. Improving navigation performance of UAS by exploiting low-cost sensors. To enhance the performance of the low-cost and light-weight integrated navigation system based on Global Navigation Satellite System (GNSS) and Micro Electro-Mechanical Systems (MEMS) inertial sensors, an efficient calibration method for MEMS inertial sensors is required. Two solutions are proposed: 1) The innovative Thermal Compensated Zero Velocity Update (TCZUPT) filter, which embeds the compensation of thermal effect on bias in the filter itself and uses Back-Propagation Neural Networks to build the calibration function. Experimental results show that the TCZUPT filter is faster than the traditional ZUPT filter in mapping significant bias variations and presents better performance in the overall testing period. Moreover, no calibration pre-processing stage is required to keep measurement drift under control, improving the accuracy, reliability, and maintainability of the processing software; 2) A redundant configuration of consumer grade inertial sensors to obtain a self-calibration of typical inertial sensors biases. The result is a significant reduction of uncertainty in attitude determination. In conclusion, both methods improve dead-reckoning performance for handling intermittent GNSS coverage. B. Proposing novel solutions for mission management to support the Unmanned Traffic Management (UTM) system in monitoring and coordinating the operations of a large number of UAS. Two solutions are proposed: 1) A trajectory prediction tool for small UAS, based on Learning Vector Quantization (LVQ) Neural Networks. By exploiting flight data collected when the UAS executes a pre-assigned flight path, the tool is able to predict the time taken to fly generic trajectory elements. Moreover, being self-adaptive in constructing a mathematical model, LVQ Neural Networks allow creating different models for the different UAS types in several environmental conditions; 2) A software tool aimed at supporting standardized procedures for decision-making process to identify UAS/payload configurations suitable for any type of mission that can be authorized standing flight regulations. The proposed methods improve the management and safe operation of large-scale UAS missions, speeding up the flight authorization process by the UTM system and supporting the increasing level of autonomy in UAS operations

Investigation of advanced navigation and guidance system concepts for all-weather rotorcraft operations

Results are presented of a survey conducted of active helicopter operators to determine the extent to which they wish to operate in IMC conditions, the visibility limits under which they would operate, the revenue benefits to be gained, and the percent of aircraft cost they would pay for such increased capability. Candidate systems were examined for capability to meet the requirements of a mission model constructed to represent the modes of flight normally encountered in low visibility conditions. Recommendations are made for development of high resolution radar, simulation of the control display system for steep approaches, and for development of an obstacle sensing system for detecting wires. A cost feasibility analysis is included

Bounding Box-Free Instance Segmentation Using Semi-Supervised Learning for Generating a City-Scale Vehicle Dataset

Vehicle classification is a hot computer vision topic, with studies ranging from ground-view up to top-view imagery. In remote sensing, the usage of top-view images allows for understanding city patterns, vehicle concentration, traffic management, and others. However, there are some difficulties when aiming for pixel-wise classification: (a) most vehicle classification studies use object detection methods, and most publicly available datasets are designed for this task, (b) creating instance segmentation datasets is laborious, and (c) traditional instance segmentation methods underperform on this task since the objects are small. Thus, the present research objectives are: (1) propose a novel semi-supervised iterative learning approach using GIS software, (2) propose a box-free instance segmentation approach, and (3) provide a city-scale vehicle dataset. The iterative learning procedure considered: (1) label a small number of vehicles, (2) train on those samples, (3) use the model to classify the entire image, (4) convert the image prediction into a polygon shapefile, (5) correct some areas with errors and include them in the training data, and (6) repeat until results are satisfactory. To separate instances, we considered vehicle interior and vehicle borders, and the DL model was the U-net with the Efficient-net-B7 backbone. When removing the borders, the vehicle interior becomes isolated, allowing for unique object identification. To recover the deleted 1-pixel borders, we proposed a simple method to expand each prediction. The results show better pixel-wise metrics when compared to the Mask-RCNN (82% against 67% in IoU). On per-object analysis, the overall accuracy, precision, and recall were greater than 90%. This pipeline applies to any remote sensing target, being very efficient for segmentation and generating datasets.Comment: 38 pages, 10 figures, submitted to journa