In recent years, both the offshore industry and the navies of the world have become increasingly interested in the potential operational usage of unmanned underwater vehicles. This paper provides a comprehensive review of a number of modern control approaches and artificial intelligence techniques which have been applied to the autopilot design problem for such craft.

In this paper, we present a high-precision Mars entry integrated navigation algorithm under large uncertainties via a desensitised extended Kalman filter (DEKF). Firstly, a new six degree-of-freedom Mars entry dynamics model is derived based on the angular velocity outputs of a gyro, which is free of modelling errors in the aerodynamic and control torques. Secondly, both the accelerometer outputs and radio measurements between orbiters and entry vehicle are used as the observations embedded in a navigation filter to perform state estimation and suppress the measurement noise. Finally, a desensitised extended Kalman filter, exhibiting the desirable property of efficiently reducing the sensitivity of state variables with respect to model and parameter uncertainties, is adopted in order to overcome the adverse effects of initial state errors and uncertainties during Mars atmospheric entry and further improve entry navigation accuracy. The numerical simulation results show that the DEKF-based integrated navigation algorithm developed in this paper can achieve a better navigation performance with higher accuracy when compared with the standard extended Kalman filter (EKF)-based integrated navigation algorithm in the presence of larger state errors and parameter uncertainties.

Multipath is still one of the major error sources that degrades the accuracy of GPS positioning. The amount of multipath is highly dependent on the antenna's environment, which makes it difficult to isolate. Usually there is at least one in-view satellite which is more susceptible to multipath, particularly the one with the lowest elevation angle. To increase the positioning the best satellites must be selected (i.e. by least square or multipath mitigation) for computing a position. In this paper we propose an algorithm which picks up the best satellites (when there are more than four satellites in view) based on wavelet analysis for calculating a position. In this experiment, code and carrier measurements were collected in 15-minute segments by exploiting a single frequency (L1), stationary, navigation-grade receiver in a high-multipath environment. The magnitudes of these pseudoranges were often inflated by multipath error. We then post-processed the received data by applying wavelet filtering to the residuals (code minus carrier) to approximate the multipath values, and compute the receiver's position based on the selected satellites. Satellites were selected based on the residual values. To compare the results with the raw measurements, statistical elements were computed. The results showed significant improvement in variance of the estimated positions and, most importantly, a normalization of the data scatter-distribution was observed.

The BeiDou system has been providing a regional navigation service since 27 December 2012. The Global Navigation Satellite System (GNSS) user community will benefit from combined Global Positioning System (GPS)/BeiDou positioning due to improved positioning accuracy, reliability and availability. But to achieve the best positioning solutions, precise weights of the GPS and BeiDou observations are important since this involves the processing of measurements from two different satellite systems with different quality. Currently, a priori variances are typically used to determine the weights of different types of observations. However, such an approach may not be precise since many un-modelled errors are not accounted for. The Helmert variance component estimation method is more appropriate in this case to determine the weights of GPS and BeiDou observations. This requires high redundant observations in order to obtain reliable solutions, which will be a concern in the case of insufficient numbers of visible satellites. To address this issue, a weighting approach is proposed by a combination of the Helmert method and a moving-window average filter. In this approach, the filter is applied to combine all epoch-by-epoch weight estimates within a time window. As a result, more precise and reliable weights for GPS and BeiDou observations can be obtained at every epoch. Both static and kinematic tests in open sky and under tree environments are conducted to assess the performance of the new weighting approach. The results indicate significantly improved positioning accuracy.

Global Positioning System (GPS) has been used as a primary source of navigation in land and airborne applications. However, challenging environments cause GPS signal blockage or degradation, and prevent reliable and seamless positioning and navigation using GPS only. Therefore, multi-sensor based navigation systems have been developed to overcome the limitations of GPS by adding some forms of augmentation. The next step towards assured robust navigation is to combine information from multiple ground-users, to further improve the chance of obtaining reliable navigation and positioning information. Collaborative (or cooperative) navigation can improve the individual navigation solution in terms of both accuracy and coverage, and may reduce the system's design cost, as equipping all users with high performance multi-sensor positioning systems is not cost effective. Generally, ‘Collaborative Navigation’ uses inter-nodal range measurements between platforms (users) to strengthen the navigation solution. In the collaborative navigation approach, the inter-nodal distance vectors from the known or more accurate positions to the unknown locations can be established. Therefore, the collaborative navigation technique has the advantage in that errors at the user's position can be compensated by other known (or more accurate) positions of other platforms, and may result in the improvement of the navigation solutions for the entire group of users. In this paper, three statistical network-based collaborative navigation algorithms, the Restricted Least-Squares Solution (RLESS), the Stochastic Constrained Least-Squares Solution (SCLESS) and the Best Linear Minimum Partial Bias Estimation (BLIMPBE) are proposed and compared to the Kalman filter. The proposed statistical collaborative navigation algorithms for network solution show better performance than the Kalman filter.

In this paper the author introduces a new method of collision avoidance for multi-target encounters. The method combines the qualities of a sequence of necessary manoeuvres with some optimisation elements and supports any given ship domain. First the problem of determining the optimal course alteration manoeuvre for given ship domain model and multi-target encounter situation is solved. The solution consists of analytical formulas and numerical algorithms combined to achieve low computational complexity. Then this solution is further used to construct a method of planning ship trajectories, which are safe, economic and intuitive for navigators. Example scenarios and the resulting trajectories are also provided. All of the algorithms, formulas and their derivations in the text are presented explicitly, so that they could be directly applied in any collision avoidance system.

This paper presents a collision risk model and operational concepts for longitudinal separated aircraft in the North Atlantic Region air traffic control track system, and indicates how it might be used to reduce separation minima safely, and hence deliver cost savings. It is an event-based model: it is easy to see what is being assumed, to understand the role of the main parameters, and to incorporate collision detection and hazard analysis. A potential future operation, with a 7-minute separation and a strategic lateral offset system, is sketched using the model.

Alarm fatigue is a critical safety issue, as it can increase workload and impair operators' situational awareness. This paper proposes a design methodology to enhance the interaction between alarm systems and operators. Through input from VTS personnel as the fundamental design requirements, a user requirement-driven design framework is proposed. It integrates quality function deployment, the theory of inventive problem solving, and software quality characteristics into three design phases. In Phase I, user requirements are obtained from the analysis of current working processes. Phase II investigates the specific non-functional design requirements of vessel alarm systems and the contradictions. In Phase III, the innovative principles generated with the contradiction matrix were analysed. A case study was conducted to verify and illustrate this framework, resulting in a conceptualisation design of a smart vessel alarm system.

The concept of Relative Receiver Autonomous Integrity Monitoring (RRAIM) using time differential carrier phase measurements is investigated in this paper. The precision of carrier phase measurements allows for mitigation of integrity hazards by implementing RRAIM monitors with tight thresholds without significantly affecting continuity. In order to avoid the need for cycle ambiguity resolution, time differences in carrier phase measurements are used as the basis for detection. In this work, we examine RRAIM within the context of the GNSS Evolutionary Architecture Study (GEAS), which explores potential architectures for aircraft navigation utilizing the satellite signals available in the mid-term future with GPS III. The objectives of the GEAS are focused on system implementations providing worldwide coverage to satisfy LPV-200 operations, and potentially beyond. In this work, we study two different GEAS implementations of RRAIM. General formulas are derived for positioning, fault detection, and protection level generation to meet a given set of integrity and continuity requirements.

Many navigation services, such as car navigation services, provide users with praxic navigational instructions (such as “turn left after 200 metres, then turn right after 150 metres”), however people usually associate directions with visual cues (e.g. “turn right at the square”) when giving navigational instructions in their daily conversations. Landmarks can play an equally important role in navigation and routing services. Landmarks are unique and easy-to-recognise and remember features; therefore, in order to remember when exploring an unfamiliar environment, they would be assets. In addition, Landmarks can be found both indoors and outdoors and their locations are usually fixed. Any positioning techniques which use landmarks as reference points can potentially provide seamless (indoor and outdoor) positioning solutions. For example, users can be localised with respect to landmarks if they can take a photograph of a registered landmark and use an application for image processing and feature extraction to identify the landmark and its location. Landmarks can also be used in pedestrian-specific path finding services. Landmarks can be considered as an important parameter in a path finding algorithm to calculate a route passing more landmarks (to make the user visit a more tourist-focussed area, pass along an easier-to-follow route, etc.). Landmarks can also be used as a part of the navigational instructions provided to users; a landmark-based navigation service makes users sure that they are on the correct route, as the user is reassured by seeing the landmark whose information/picture has just been provided as a part of navigational instruction. This paper shows how landmarks can help improve positioning and praxic navigational instructions in all these ways.

This paper describes a statistical method for learning and estimating the risk posed by other craft in the vicinity of a vessel and an overview of its possible spatial application, simulating how professional mariners perceive and assess such risk and using navigational data obtained from a standard integrated bridge. We propose a non-linear model for risk estimation which attempts to capture mariners' judgement. Questionnaire data has been collected that captures and quantifies mariners’ judgements of risk for craft in the vicinity, where each craft is described by measurements that can be obtained easily from the data already present in the ship's navigational equipment. The dataset has then been used for analysis, training and validating Ordered Probit models in order to obtain a computationally efficient data driven model for estimating the risk probability vector posed by other craft. Finally, we discuss how this risk model can be incorporated into decision making and path finding algorithms.

This paper presents a reliable in-motion alignment algorithm for a low cost Strapdown Inertial Navigation System/Global Positioning System (SINS/GPS) combination under random misalignment angles, which transforms attitude alignment into an attitude estimation problem. Based on Rodrigues parameters, an alignment model with a linear state-space equation and a second order nonlinear measurement equation is established. Furthermore, by employing a Taylor expansion on the nonlinear measurement equation, we implement a second order Extended Kalman Filter (EKF2). The proposed method uses a single filter that can not only determine the initial attitude, but also estimate the sensor errors. In addition, a scheme is given for avoiding singularity, which makes the algorithm more widely suitable for random misalignment angles. Experimental ground tests are performed with a low-cost Micro-Electromechanical System (MEMS) SINS, which validates the efficacy of the proposed method. The performance compared to the traditional alignment algorithm is also given.

Communicating location-specific information to pedestrians is a challenging task which can be aided by user-friendly digital technologies. In this paper, landmark visibility analysis, as a means for developing more usable pedestrian navigation systems, is discussed. Using an algorithmic framework for image-based 3D analysis, this method integrates a 3D city model with identified landmarks and produces raster visibility layers for each one. This output enables an Android phone prototype application to indicate the visibility of landmarks from the user's actual position. Tested in the field, the method achieves sufficient accuracy for the context of use and improves navigation efficiency and effectiveness.

International discussions on the concept of e-navigation have identified a robust position-fixing system as one of the essential components. Global Navigation Satellite Systems (GNSS) are known to have vulnerabilities and onboard alternatives such as inertial systems have limitations. This paper considers the case for an enhanced version of the terrestrial radio-navigation system Loran to provide an alternative with performance comparable to GNSS. The paper reviews recent studies of inertial navigation systems and concludes that they do not present a fully capable backup to GNSS at present. Trials of enhanced Loran carried out in the UK by the General Lighthouse Authorities have shown that eLoran does have the potential to provide equivalent performance to GNSS over long periods and is a fully complementary system. The steps needed to provide eLoran on at least a regional basis, covering critical waterways, are considered. The international process for the specification and standardisation of eLoran is already underway and some projections are made about the timescale for full implementation, in the context of the introduction of e-Navigation. A version of this paper was first presented at NAV 07 held in Church House, London from 30th October – 1st November 2007.

The wave climate along the main transoceanic routes of the North Atlantic sub basin is determined using three different databases: two derived by numerical models in the HIPOCAS and ERA40 databases and one from Voluntary Observing Ships. For each route the distribution of the mean significant wave height along the path is computed as well as the specific scatter diagram. In addition an assessment of the relative wave heading probability is provided. The results highlight a bias in the visual observations especially in the summer and, more in general, for low sea states. The correction of this bias allows better understanding of rough weather avoidance by ships and to determine a storm avoidance correction.

On ne sait pas pourquoi ils s'echouent parfois sur la plage; car on assure qu'ils le font assez souvent, quand la fantaisie leur en prend, et sans aucune cause appréciable.

The bacterial foraging optimisation (BFO) algorithm is a commonly adopted bio-inspired optimisation algorithm. However, BFO is not a proper choice in coping with continuous global path planning in the context of unmanned surface vehicles (USVs). In this paper, a grid partition-based BFO algorithm, named AS-BFO, is proposed to address this issue in which the enhancement is contributed by the involvement of the A* algorithm. The chemotaxis operation is redesigned in AS-BFO. Through repeated simulations, the relative optimal parameter combination of the proposed algorithm is obtained and the most influential parameters are identified by sensitivity analysis. The performance of AS-BFO is evaluated via five size grid maps and the results show that AS-BFO has advantages in USV global path planning.

This paper optimises two-ship collision-avoidance manoeuvres accounting for both collision risk and fuel use. A collision-avoidance manoeuvring optimisation model is developed to minimise fuel consumption while assuring ships' operational safety. The model can optimally determine when to begin collision-avoidance actions, how to change courses, and what rudder angles are needed. A quantitative scenario simulation is developed to illustrate the model application. The methodology can be further developed to guide practical ship collision-avoidance manoeuvring decisions made under more operational scenarios. In particular, this research can contribute to the development of computer-aided collision-avoidance operations to improve the safety and energy efficiency of maritime transportation.

The search for more efficient ship operation has increased over the last decade. One trend towards reducing operational costs is to conceive the wheelhouse as an operational centre for performing both navigational and supervisory tasks, such as monitoring the propulsion plant status and the condition of ship and cargo.

Under contract to the National Foundation for the Coordination of Maritime Research in The Netherlands, a study was conducted on the feasibility of a highly automated wheelhouse for single-handed navigation. In a series of simulation experiments, and by analysis, the effects on efficiency and safety of performance were investigated.

Results show that a careful function allocation can lead to an automated wheelhouse concept suitable for safe navigation in landfall conditions. Questions concerning the effects of monotonous watches on operator's alertness and the effects of the change in task structure on the operator's skill and interest in the job need further attention.

The accuracy and fault tolerance of filters are directly affected by the filter architecture and algorithm, thus influencing navigation performance. The chi square detection used in the conventional reset federated filter is not sensitive to soft faults, and it is easy to cause the health subsystem to be polluted through information sharing. It is a challenge to design an adaptive reset federated filter to improve the performance of the navigation system. Therefore, taking the Strapdown Inertial Navigation System/Global Positioning System/Celestial Navigation System/Synthetic Aperture Radar (SINS/GPS/CNS/SAR) integrated navigation system as an example, an adaptive federated filter architecture for vector-formed information sharing without a fault isolation module is designed in this paper. The proposed method uses the two-state chi square detection algorithm to calculate the parameters corresponding to each state, making the state with higher accuracy obtain a greater information distribution coefficient. In addition, according to the value of vector-formed information sharing, an adaptive coefficient of measurement noise is designed. This improves the adaptability of the navigation system to soft faults. Simulation results show that the accuracy of the proposed algorithm has the same performance compared with the conventional method under normal circumstances. When the sensor has a soft fault, the adaptive federated filter algorithm proposed in this paper can adaptively adjust the distribution coefficients, eliminate the influence of the fault information and improve the precision of the navigation system. The approach described in this paper can be used in multi-sensor integrated navigation. It will have better performance in engineering applications.