THE AUTOMATIC CONTROL OF LARGE SHIPS IN CONFINED WATERS

The design and evaluation of a control system, which can be utilised for the automatic guidance of large ships in confined or restricted waters, is investigated. The vessel is assumed to be a multivariable system and it is demonstrated that a non-linear, time-varying mathematical model most accurately describes the motion of the hull, particularly in tight manoeuvres. A discrete optimal controller has been designed to control simultaneously track, heading and forward velocity. The system is most effective whilst operating under a dual-mode policy. It is shown that feedback matrix adaption is necessary to deal with changes in forward velocity and a form of gain scheduling is proposed. Active disturbance control is employed to counteract effects of wind and tide. An inertial navigation system, together with an optimal controller and filter, is installed on-board a car ferry model. Free-sailing tests show that the performance characteristics of the system are in accordance with theoretical predictions. The feasibility of implementation on a full-size vessel is considered.University College, Londo

AN ADAPTABLE MATHEMATICAL MODEL FOR INTEGRATED NAVIGATION SYSTEMS

The project has been directed towards improving the accuracy and safety of marine navigation and ship handling, whilst contributing to reduced manning and improved fuel costs. Thus, the aim of the work was to investigate, design and develop an adaptable mathematical model that could be used in an integrated navigation system (INS) and an automatic collision avoidance system (ACAS) for use in marine vehicles. A general overview of automatic navigation is undertaken and consideration is given to the use of microprocessors on the bridge. Many of these systems now require the use of mathematical models to predict the vessels' manoeuvring characteristics: The different types and forms of models have been investigated and the derivation of their hydrodynamic coefficients is discussed in detail. The model required for an ACAS should be both accurate and adaptable, hence, extensive simulations were undertaken to evaluate the suitability of each model type. The modular model was found to have the most adaptable structure. All the modular components of this model were considered in detail to improve its adaptability, the number of non-linear terms in the hull module being reduced. A novel application, using the circulation theory to model the propeller forces and moments, allows the model to be more flexible compared to using traditional B-series four-quadrant propeller design charts. A new formula has been derived for predicting the sway and yaw components due to the propeller paddle wheel effect which gives a good degree of accuracy when comparing simulated and actual ship data, resulting in a mean positional error of less than 7%. As a consequence of this work, it is now possible for an ACAS to incorporate a ship mathematical model which produces realistic manoeuvring characteristics. Thus, the study will help to contribute to safety at sea.Kelvin Hughes Lt

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour

Reliable detection and characterisation of dim target via track-before-detect

Detection of manoeuvring and small objects is a challenging task in radar surveillance applications. Small objects in high noise background induce low signal to noise ratio (SNR) reflections. Conventional methods detect such objects by integrating multiple reflections in the same range-bearing and doppler bins in sampled versions of received signals. When the objects manoeuvre, however, these methods are likely to fail to detect them because the integration is performed without taking into account the possibility of the object movements across resolution bins. Furthermore, slowly manoeuvring objects create detection difficulties in discriminating them from radar clutter. Reflections of such objects contain micro-Doppler shifts generated by their propulsion devices. These shifts can characterise specific types of objects. In this case, estimation of these shifts is a challenging task because the front-end signals at the receiver are low SNR reflections and are the superposition of all reflections from the entire object and the noise background. Conventional estimators for this purpose only use reflections collected in a coherent processing interval (CPI) and produce poor estimate outputs. In order to achieve the desired accuracy, one requires more reflections than those collected in a CPI. This thesis mainly considers the aforementioned two difficulties and aims to develop efficient algorithms, which can detect low SNR and manoeuvring objects by incorporating long-time pulse integration and micro-doppler estimation. Main contributions in this thesis are based on the following two algorithms. The first work considers the detection of manoeuvring and small objects with radars. The radar systems are considered both co-located and separated transmitter/receiver pairs, i.e., monostatic and bistatic configurations, respectively, as well as multistatic settings involving both types. The proposed detection algorithm is capable of coherently integrating reflected signals within a CPI in all these configurations and continuing integration for an arbitrarily long time across consecutive CPIs. This approach estimates the complex value of the reflection coefficients for the integration while simultaneously estimating the object trajectory. Compounded with this simultaneous tracking and reflection coefficient estimation is the estimation of the unknown time reference shift of the separated transmitters necessary for coherent processing. The detection is made by using the resulting integration value in a Neyman-Pearson test against a constant false alarm rate threshold. The second work focuses on micro-Doppler signature estimation of manoeuvring and small rotor based unmanned aerial vehicle (UAV) systems with a monostatic radar. The micro-Doppler signature is considered rotation frequencies generated by rotating rotor blades of the UAVs. This estimation uses a maximum likelihood (ML) approach that finds rotation frequencies to maximise a likelihood function conditioned on an object trajectory, complex reflection coefficients, and rotation frequencies. In particular, the proposed algorithm uses an expectation-maximisation (EM) approach such that the expectation of the likelihood mentioned above is approximated by using the state distributions generated from Bayesian recursive filtering for the trajectory estimation. The reflection coefficients and the rotation frequencies are estimated by maximising this approximated expectation. As a result, this algorithm is capable of simultaneously tracking the trajectory and estimating the reflection coefficients and the rotation frequencies of the UAVs before the decision on the object presence is made

Design of robust slow-speed ships for sustainable operation

Phd ThesisMulti-objective optimisation that considers the energy efficiency and economic success is an important aspect of ship design and operation. Both the hydrodynamic and economic performance characteristics need to be addressed in the early stages of the design, and secured during the life span of a ship. Because of the conflicting nature of these two objectives, there are various trade-offs at stake in the goal for making ships more efficient and greener to comply with IMO regulations while reducing the building and operating costs and increasing the profitability at the same time for all stakeholders especially owners and operators. In attempt to reduce the amount of greenhouse gas emissions from ships, and hence to achieve a lower EEDI value, this research approaches the problem of improving the energy efficiency of ships. That is achieved by optimising the hull design over a speed range through parametric modification to reduce resistance and required power, and also through adopting slow steaming concept. Moreover, the research aims to determine the best practice to reduce the annual cost of running a ship and to increase the annual revenue as well as to make the ship a more profitable investment over her life span. The profit per tonne.mile and the net present value NPV are estimated in the economic analysis to be used as indicators to compare alternative designs for different routes and market conditions scenarios. To achieve this aim, the main operational and economic aspects such as the fluctuations in the fright rates and fuel prices in the shipping market are covered in the economic analysis. In addition, the acquiring price and salvage value are included in order to obtain solid comparisons. An optimisation framework using a VBA macro code has been developed based on the concept of Pareto optimality to assess decision making, and to determine robust designs as well as operational profiles based on results from the hydrodynamic model, environmental impact model, and the economic model. The optimisation process is carried out for a Panamax tanker case study using 5 parameters and a set of constraints for the hull parameters and speed. The outcome from the optimisation framework is a set of Pareto optimal solutions where weight factors are appointed to give the flexibility when addressing the importance of each individual function. The solutions are presented graphically to form what is known as Pareto front which determines the design space and the trade-offs between the different competing objective ii functions. This optimisation framework could assist decision making where it is possible to choose a robust design or designs that offer a near-optimum performance regardless any fluctuations in the market and or the operation profile, and eliminate any significant sub-optimal design

Selected Papers from the 2018 IEEE International Workshop on Metrology for the Sea

This Special Issue is devoted to recent developments in instrumentation and measurement techniques applied to the marine field. ¶The sea is the medium that has allowed people to travel from one continent to another using vessels, even today despite the use of aircraft. It has also been acting as a great reservoir and source of food for all living beings. However, for many generations, it served as a landfill for depositing conventional and nuclear wastes, especially in its deep seabeds, and we are assisting in a race to exploit minerals and resources, different from foods, encompassed in it. Its health is a great challenge for the survival of all humanity since it is one of the most important environmental components targeted by global warming. ¶ As everyone may know, measuring is a step that generates substantial knowledge about a phenomenon or an asset, which is the basis for proposing correct solutions and making proper decisions. However, measurements in the sea environment pose unique difficulties and opportunities, which is made clear from the research results presented in this Special Issue

THEORETICAL ASPECTS AND REAL ISSUES IN AN INTEGRATED MULTIRADAR SYSTEM

In the last few years Homeland Security (HS) has gained a considerable interest in the research community. From a scientific point of view, it is a difficult task to provide a definition of this research area and to exactly draw up its boundaries. In fact, when we talk about the security and the surveillance, several problems and aspects must be considered. In particular, the following factors play a crucial role and define the complexity level of the considered application field: the number of potential threats can be high and uncertain; the threat detection and identification can be made more complicated by the use of camouflaging techniques; the monitored area is typically wide and it requires a large and heterogeneous sensor network; the surveillance operation is strongly related to the operational scenario, so that it is not possible to define a unique approach to solve the problem [1]. Information Technology (IT) can provide an important support to HS in preventing, detecting and early warning of threats. Even though the link between IT and HS is relatively recent, sensor integration and collaboration is a widely applied technique aimed to aggregate data from multiple sources, to yield timely information on potential threats and to improve the accuracy in monitoring events [2]. A large number of sensors have already been developed to support surveillance operations. Parallel to this technological effort in developing new powerful and dedicated sensors, interest in integrating a set of stand-alone sensors into an integrated multi-sensor system has been increasing. In fact, rather than to develop new sensors to achieve more accurate tracking and surveillance systems, it is more useful to integrate existing stand-alone sensors into a single system in order to obtain performance improvements In this dissertation, a notional integrated multi-sensor system acting in a maritime border control scenario for HS is considered. In general, a border surveillance system is composed of multiple land based and moving platforms carrying different types of sensors [1]. In a typical scenario, described in [1], the integrated system is composed of a land based platform, located on the coast, and an airborne platform moving in front of the coast line. In this dissertation, we handle two different fundamental aspects. In Part I, we focus on a single sensor in the system, i.e. the airborne radar. We analyze the tracking performance of such a kind of sensor in the presence of two different atmospheric problems: the turbulence (in Chapter 1) and the tropospheric refraction (in Chapter 2). In particular, in Chapter 1, the losses in tracking accuracy of a turbulence-ignorant tracking filter (i.e. a filter that does not take into account the effects of the atmospheric turbulences) acting in a turbulent scenario, is quantified. In Chapter 2, we focus our attention on the tropospheric propagation effects on the radar electromagnetic (em) signals and their correction for airborne radar tracking. It is well known that the troposphere is characterized by a refractive index that varies with the altitude and with the local weather. This variability of the refractive index causes an error in the radar measurements. First, a mathematical model to describe and calculate the em radar signal ray path in the troposphere is discussed. Using this mathematical model, the errors due to the tropospheric propagation are evaluated and the corrupted radar measurements are then numerically generated. Second, a tracking algorithm, based on the Kalman Filter, that is able to mitigate the tropospheric errors during the tracking procedure, is proposed. In Part II, we consider the integrated system in its wholeness to investigate a fundamental prerequisite of any data fusion process: the sensor registration process. The problem of sensor registration (also termed, for naval system, the grid-locking problem) arises when a set of data coming from two or more sensors must be combined. This problem involves a coordinate transformation and the reciprocal alignment among the various sensors: streams of data from different sensors must be converted into a common coordinate system (or frame) and aligned before they could be used in a tracking or surveillance system. If not corrected, registration errors can seriously degrade the global system performance by increasing tracking errors and even introducing ghost tracks. A first basic distinction is usually made between relative grid-locking and absolute grid-locking. The relative grid-locking process aligns remote data to local data under the assumption that the local data are bias free and that all biases reside with the remote sensor. The problem is that, actually, also the local sensor is affected by bias. Chapter 3 of this dissertation is dedicated to the solution of the relative grid-locking problem. Two different estimation algorithms are proposed: a linear Least Squares (LS) algorithm and an Expectation-Maximization-based (EM) algorithm. The linear LS algorithm is a simple and fast algorithm, but numerical results have shown that the LS estimator is not efficient for most of the registration bias errors. Such non-efficiency could be caused by the linearization implied by the linear LS algorithm. Then, in order to obtain a more efficient estimation algorithm, an Expectation-Maximization algorithm is derived. In Chapter 4 we generalize our findings to the absolute grid-locking problem. Part III of this dissertation is devoted to a more theoretical aspect of fundamental importance in a lot of practical applications: the estimate of the disturbance covariance matrix. Due to its relevance, in literature it can be found a huge quantity of works on this topic. Recently, a new geometrical concept has been applied to this estimation problem: the Riemann (or intrinsic) geometry. In Chapter 5, we give an overview on the state of the art of the application of the Riemann geometry for the covariance matrix estimation in radar problems. Particular attention is given for the detection problem in additive clutter. Some covariance matrix estimators and a new decision rule based on the Riemann geometry are analyzed and their performance are compared with the classical ones. [1] Sofia Giompapa, “Analysis, modeling, and simulation of an integrated multi-sensor system for maritime border control”, PhD dissertation, University of Pisa, April 2008. [2] H. Chen, F. Y. Wang, and D. Zeng, “Intelligence and security informatics for Homeland Security: information, communication and transportation,” Intelligent Transportation Systems, IEEE Transactions on, vol. 5, no. 4, pp. 329-341, December 2004

Development and Optimization of Motion Cueing for Flight Simulation of Maritime Helicopter Operations

Maritime helicopter operations such as helicopter ship deck landings are highly demanding for both the pilot and the helicopter. In the absent of potential safety risks during an offshore mission a maritime simulation environment offers a benefit for pilot training and development of new systems and procedures. Such a maritime simulation environment requires a specific optimized motion system. In the recent past years new methods have been developed to tune the motion system in an easier, faster and objective way. Unfortunately, these methods are not sufficiently validated because however suitable validation methods are still missing. This master thesis presents a methodology to implement a Classical Washout Algorithm (CWA) in the simulator. To develop suitable motion parameter sets for a helicopter ship deck landing procedure the fitness function is used as a novel optimization method. The validation is conducted with piloted simulator flight test trials that are new developed. Additionally, an unpiloted validation is carried out with the Objective Motion Cueing Test (OMCT). The results of both validation methods are compared to each other

The University Defence Research Collaboration In Signal Processing: 2013-2018

Signal processing is an enabling technology crucial to all areas of defence and security. It is called for whenever humans and autonomous systems are required to interpret data (i.e. the signal) output from sensors. This leads to the production of the intelligence on which military outcomes depend. Signal processing should be timely, accurate and suited to the decisions to be made. When performed well it is critical, battle-winning and probably the most important weapon which you’ve never heard of. With the plethora of sensors and data sources that are emerging in the future network-enabled battlespace, sensing is becoming ubiquitous. This makes signal processing more complicated but also brings great opportunities. The second phase of the University Defence Research Collaboration in Signal Processing was set up to meet these complex problems head-on while taking advantage of the opportunities. Its unique structure combines two multi-disciplinary academic consortia, in which many researchers can approach different aspects of a problem, with baked-in industrial collaboration enabling early commercial exploitation. This phase of the UDRC will have been running for 5 years by the time it completes in March 2018, with remarkable results. This book aims to present those accomplishments and advances in a style accessible to stakeholders, collaborators and exploiters

Deep-Sea Model-Aided Navigation Accuracy for Autonomous Underwater Vehicles Using Online Calibrated Dynamic Models

In this work, the accuracy of inertial-based navigation systems for autonomous underwater vehicles (AUVs) in typical mapping and exploration missions up to 5000m depth is examined. The benefit of using an additional AUV motion model in the navigation is surveyed. Underwater navigation requires acoustic positioning sensors. In this work, so-called Ultra-Short-Baseline (USBL) devices were used allowing the AUV to localize itself relative to an opposite device attached to a (surface) vehicle. Despite their easy use, the devices\u27 absolute positioning accuracy decreases proportional to range. This makes underwater navigation a sophisticated estimation task requiring integration of multiple sensors for inertial, orientation, velocity and position measurements. First, error models for the necessary sensors are derived. The emphasis is on the USBL devices due to their key role in navigation - besides a velocity sensor based on the Doppler effect. The USBL model is based on theoretical considerations and conclusions from experimental data. The error models and the navigation algorithms are evaluated on real-world data collected during field experiments in shallow sea. The results of this evaluation are used to parametrize an AUV motion model. Usually, such a model is used only for model-based motion control and planning. In this work, however, besides serving as a simulation reference model, it is used as a tool to improve navigation accuracy by providing virtual measurements to the navigation algorithm (model-aided navigation). The benefit of model-aided navigation is evaluated through Monte Carlo simulation in a deep-sea exploration mission. The final and main contributions of this work are twofold. First, the basic expected navigation accuracy for a typical deep-sea mission with USBL and an ensemble of high-quality navigation sensors is evaluated. Secondly, the same setting is examined using model-aided navigation. The model-aiding is activated after the AUV gets close to sea-bottom. This reflects the case where the motion model is identified online which is only feasible if the velocity sensor is close to the ground (e.g. 100m or closer). The results indicate that, ideally, deep-sea navigation via USBL can be achieved with an accuracy in range of 3-15m w.r.t. the expected root-mean-square error. This also depends on the reference vehicle\u27s position at the surface. In case the actual estimation certainty is already below a certain threshold (ca. <4m), the simulations reveal that the model-aided scheme can improve the navigation accuracy w.r.t. position by 3-12%