At 17:22 UTC on 7th March 2014 Malaysian Airlines flight MH370 carrying 239 passengers and crew from Kuala Lumpur to Beijing lost contact with Air Traffic Control and was subsequently reported missing. Over the following days an extensive air and sea search was made around the last reported location of the aircraft in the Gulf of Thailand without success. Subsequent analysis of signals transmitted by the aircraft's satellite communications terminal to Inmarsat's 3F1 Indian Ocean Region satellite indicated that the aircraft continued to fly for several hours after loss of contact, resulting in the search moving to the southern Indian Ocean. This paper presents an analysis of the satellite signals that resulted in the change of search area.

Urban positioning using the Global Positioning System (GPS) is challenging because of multipath. Urban canyons limit open sky visibility, and cause signal reflection and diffraction, resulting in significant satellite range measurement errors. The investigations reported here have been carried out in a French project called Inturb (an acronym derived from integrity and urban positioning). So far, the project has had two phases: first, a simple Three-Dimensional (3D) geometrical city modelling, called “Urban Trench”, has been developed and engineered manually from data sets collected in different cities. Positioning improvement in terms of accuracy was quantified where the model could be applied. Second, this modelling has been automated, based on the standard national BD Topo ® map database for France, with promising results. This geometrical modelling makes it possible to distinguish between line-of-sight satellite signals and those from non-line-of-sight. The latter, apparently bona fide, signals are caused by strong reflections, usually from buildings with a lot of steel and glass in their construction. A correction of the pseudo-range measurements of the latter is also computed and applied in the position estimator. Positioning accuracy is improved, whilst availability is kept at its maximum. In the study both manual and automatic 3D models are used in extensive experimental campaigns. Results are: first, the possibility to cover entirely any urban area in the country; second, the reduction of the median error in 3D by more than 50% on data collected in Nantes, Paris and Toulouse for a total duration of nearly ten hours; third, the compliance with standards used in most embedded maps and geographical information systems, including an assessment of the trade-off between the model simplicity and the positioning improvement.

The Decca Navigator System was very unusual among radio-navigation systems in that the entire system was championed and marketed by a commercial company. How it came about was also unusual – it was neither the product of market research nor a military specification. It was a solution to one of the major problems of the 1930s facing those who attempted to use radio methods for the location of vehicles, that of measuring extremely small time intervals. The problem was eventually solved by pulse methods (radar), which were kept very secret at the time, but Decca offered an alternative particularly suited to marine navigation because of its use of low frequencies. No matter how good a technical solution, many systems fail if they are not actively supported by proper marketing and in this Decca Navigator was fortunate to have the backing of a major British company, the Decca Record Company. This paper describes the inception, wartime trials and eventual acceptance into general marine use of Decca Navigator. Some previously unpublished historical material from the company archives is included.

In various applications of land vehicle navigation and automatic guidance systems, Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU) positioning performance crucially depends on the attitude determination accuracy affected by gyro and accelerometer bias instabilities. Traditional bias estimation approaches based on the Kalman filter suffer from implementation complexity and require non-intuitive tuning procedures. In this paper we propose, as an alternative, a simple observer that estimates inertial sensor biases exclusively in terms of quantities with obvious geometrical meaning. By this, any multidimensional vector-matrix operations are avoided and observer tuning is substantially simplified. The observer has been successfully tested in a farming vehicle navigation system.

Autonomous navigation has become a key technology for deep space exploration missions. Phobos and Deimos, the two natural moons of Mars, are important optical navigation information sources available for Mars missions. However, during the phase of the probe orbiting close to Mars, the ephemeris bias and the difference between the barycentre and the centre of brightness of a Martian moon will result in low navigation accuracy. On the other hand, Satellite-to-Satellite Tracking (SST) can achieve convenient and high accuracy observation for autonomous navigation. However, this cannot apply for a Mars mission during the Mars orbit phase only by SST data because of a rank defect problem of the Jacobian matrix. To improve the autonomous navigation accuracy of Mars probes, this paper presents a new autonomous navigation method that combines SST radio data provided by two probes and optical measurement by viewing the natural Martian moons. Two sequential orbit determination algorithms, an Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are compared. Simulation results show this method can obtain high autonomous navigation accuracy during the probe's Mars Orbit phase.

Global Navigation Satellite System (GNSS) shadow matching is a new positioning technique that determines position by comparing the measured signal availability and strength with predictions made using a three-dimensional (3D) city model. It complements conventional GNSS positioning and can significantly improve cross-street positioning accuracy in dense urban environments. This paper describes how shadow matching has been adapted to work on an Android smartphone and presents the first comprehensive performance assessment of smartphone GNSS shadow matching. Using GPS and GLONASS data recorded at 20 locations within central London, it is shown that shadow matching significantly outperforms conventional GNSS positioning in the cross-street direction. The success rate for obtaining a cross-street position accuracy within 5 m, enabling the correct side of a street to be determined, was 54·50% using shadow matching, compared to 24·77% for the conventional GNSS position. The likely performance of four-constellation shadow matching is predicted, the feasibility of a large-scale implementation of shadow matching is assessed, and some methods for improving performance are proposed. A further contribution is a signal-to-noise ratio analysis of the direct line-of-sight and non-line-of-sight signals received on a smartphone in a dense urban environment.

Exact motion estimation is a major task in autonomous navigation. The integration of Inertial Navigation Systems (INS) and the Global Positioning System (GPS) can provide accurate location estimation, but cannot be used in a GPS denied environment. In this paper, we present a tight approach to integrate a stereo camera and low-cost inertial sensor. This approach takes advantage of the inertial sensor's fast response and visual sensor's slow drift. In contrast to previous approaches, features both near and far from the camera are simultaneously taken into consideration in the visual-inertial approach. The near features are parameterised in three dimensional (3D) Cartesian points which provide range and heading information, whereas the far features are initialised in Inverse Depth (ID) points which provide bearing information. In addition, the inertial sensor biases and a stationary alignment are taken into account. The algorithm employs an Iterative Extended Kalman Filter (IEKF) to estimate the motion of the system, the biases of the inertial sensors and the tracked features over time. An outdoor experiment is presented to validate the proposed algorithm and its accuracy.

The algorithm flow of an inertial-based Pedestrian Navigation System (PNS) can be divided into a trajectory-generation stage and trajectory-calibration stage. The Zero-velocity UPdaTe (ZUPT)-aided Extended Kalman Filter (EKF) algorithm is commonly used to resolve the trajectory of a walking person, but it still suffers from long-term drift. Many methods have been developed to suppress these drifts and thus to calibrate the trajectory generated by the previous stage. However, these methods have certain requirements, such as explicit map information or frequent location revisits, which are hard to satisfy in such situations as Search and Rescue (SAR) operations. A new approach is proposed in this paper that requires no explicit presupposition. This approach is based on a particle filter framework, with the weight of particles being adaptively adjusted according to the a priori knowledge of building structures and human behaviours. The distribution of particle weights is designed with awareness of the regular structures of buildings. The time-varying parameter of the distribution is acquired from a Hidden Markov Model (HMM) based on the foregoing odometry, which has a close relation with human behaviour. HMM is trained offline based on samples acquired in advance. Many real-world experiments under various scenarios were performed, and the results indicate good accuracy and robustness of the proposed approach.

Large container ships can only be berthed in hub ports with deep water, which requires a feeder ship service to transit and transport containers from the hub ports. This paper presents a feeder routing optimisation method for container ships through an intelligent Electronic Chart Display and Information System (ECDIS). ECDIS has been adopted to design routes and calculate the estimated time of arrival in two ports, and a mixed integer programming model is established for container vessel regional transportation where the shortest ship sailing time is designated as the objective function. In this paper, through using heuristic tour-route coding, the solution of the model based on genetic algorithms is presented to select ship capacities and routes simultaneously. Taking the Pearl River in China as an example, for different types of vessel capacity, vessel costs and fuel costs, 100 TEU and 150 TEU ship capacities with six optimal routes are selected to minimise sailing time and operating costs.

To meet the accuracy, integrity, continuity and availability required for many navigation applications the Locata technology can provide an alternative to satellite-based navigation in difficult Global Navigation Satellite System (GNSS) signal environments, especially for applications in port areas and in constricted waterways. Unlike GNSS constellations, a LocataNet – a local constellation of LocataLites – can be designed specifically for different environments to avoid signal blockages, interference or poor geometry. By using Locata technology, the optimal performance within particular areas can always be guaranteed. This paper demonstrates the influence of LocataNet configuration on the reliability and integrity of the Locata positioning system. The performance of the Locata system is investigated using the Receiver Autonomous Integrity Monitoring (RAIM) concept. Fault Detection and Exclusion (FDE) algorithm performance is validated through the computation of the Dilution of Precision (DOP), the Horizontal Protection Level (HPL) and the correlation coefficient between two failure modes that can indicate the quality of fault identification. The experimental analysis shows that a good configuration of LocataLites will enhance the accuracy and reliability of the navigation system.

A prerequisite for a Global Positioning System (GPS) attitude determination is to calculate baselines between antennae with accuracy at the millimetre level simultaneously. However, in order to have a low cost attitude determination system, a set of Commercial-Off-The-Shelf (COTS) receivers with separate clocks are used. In this case, if the receiver clocks are not precisely synchronized, the baseline vector between antennae will be calculated from the GPS signals received at different times. This can be a significant error source for high-kinematic applications. In this paper, two equivalent and effective approaches are developed to compensate this significant bias for baseline estimation and attitude determination. Test results using real airborne GPS data demonstrate that the receiver time misalignment between the two receivers can result in a 5 cm baseline offset for an aircraft with a 50 m/s velocity; the corresponding attitude errors can reach about 0·50° in yaw and 0·10° in pitch respectively for the attitude determination system with a baseline length of 3·79 m. With the proposed methods, these errors can be effectively eliminated.

The paper introduces a new approach to displaying information on targets. The proposed display visualises three types of information: targets' motion parameters (typical for target tracking), combinations of own course and speed which collide with those targets (typical for Collision Threat Parameters Area display by Lenart (1983)) and combinations of own course and speed which are not compliant with COLREGS in this case (based on ships' motion parameters and visibility conditions). A superposition of the last two types of data enables a navigator to quickly choose a collision avoidance manoeuvre which is both sufficient and COLREGS-compliant. Additionally, the displayed data may be filtered based on the remaining Time To Collision (TTC) so that navigators can concentrate on direct threats. The paper includes a description of the proposed visualisation technique as well as examples of visualised data for some encounter situations.

The objective of this paper is to investigate an online method to generate an optimal ascent trajectory for air-breathing hypersonic vehicles. A direct method called the Pseudo-spectral method shows promise for real-time optimal guidance. A significant barrier to this optimisation-based control strategy is computational delay, especially when the solution time of the non-linear programming problem exceeds the sampling time. Therefore, an online guidance algorithm for an air-breathing hypersonic vehicles with process constraints and terminal states constraints is proposed based on the Pseudo-spectral method and sensitivity analysis in this paper, which can reduce online computational costs and improve performance significantly. The proposed ascent optimal guidance method can successively generate online open-loop suboptimal controls without the design procedure of an inner-loop feedback controller. Considering model parameters' uncertainties and external disturbance, a sampling theorem is proposed that indicates the effect of the Lipschitz constant of the dynamics on sampling frequency. The simulation results indicate that the proposed method offers improved performance and has promising ability to generate an optimal ascent trajectory for air-breathing hypersonic vehicles.

Autonomous Unmanned Ground Vehicles (UGVs) require a reliable navigation system that works in all environments. However, indoor navigation remains a challenge because the existing satellite-based navigation systems such as the Global Positioning System (GPS) are mostly unavailable indoors. In this paper, a tightly-coupled integrated navigation system that integrates two dimensional (2D) Light Detection and Ranging (LiDAR), Inertial Navigation System (INS), and odometry is introduced. An efficient LiDAR-based line features detection/tracking algorithm is proposed to estimate the relative changes in orientation and displacement of the vehicle. Furthermore, an error model of INS/odometry system is derived. LiDAR-estimated orientation/position changes are fused by an Extended Kalman Filter (EKF) with those predicted by INS/odometry using the developed error model. Errors estimated by EKF are used to correct the position and orientation of the vehicle and to compensate for sensor errors. The proposed system is verified through simulation and real experiment on an UGV equipped with LiDAR, MEMS-based IMU, and encoder. Both simulation and experimental results showed that sensor errors are accurately estimated and the drifts of INS are significantly reduced leading to navigation performance of sub-metre accuracy.

This paper presents the results of distance measurements performed with an AT86RF233 chip. It uses a combination of time of flight and phase shift measurements to perform ranging. The statistical parameters describing the ranging results are presented and an algorithm to process raw measurement data is proposed. The results show significant improvement in ranging accuracy.

Understanding the impact of technology introduction on users’ attitudes toward and behaviour with technology is increasingly important. This paper suggests several research questions that are important as Wearable, Immersive Augmented Reality (WIAR) systems are introduced in ship navigation: what contributions to navigation decision-making might be made by WIAR technology? And how do these influences relate to safety in marine transportation? This paper begins with an overview of information needs for shipboard navigation and piloting, and then describes the evolution of shipboard decision support systems for navigation, including the emergence of wearable, immersive augmented reality systems. An assessment of piloting and navigation information needs and the capabilities of WIAR are then presented, which suggests a conceptual model for studying the impact of WIAR systems on performance and safety in marine transportation. Conclusions and future work to explore the role and contribution of WIAR technology to the safety of navigation are then presented.

The paper presents the continuation of the author's research on ship track planning by means of Evolutionary Algorithms (EA). The presented method uses EA to search for an optimal set of safe tracks for all ships involved in an encounter. Until now the method assumed good visibility – compliance with standard rules of the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS, 1972). However, in restricted visibility, when Rule 19 applies instead of Rules 11 to 18, the problem is a different one. Therefore this paper introduces the extended method, with a focus on compliance with Rule 19 and its implications. It includes descriptions of detecting, penalizing and eliminating violations of Rule 19. The method has been implemented and the paper contains sample results of computer simulation tests carried out for ship encounters in restricted visibility in both open and restricted waters. They confirm the effectiveness of the chosen approach and suggest that the method could be applied in on board decision support systems.

Multipath arises from the reception of reflected or diffracted signals in addition to the Line-Of-Sight (LOS) signal. By using a block processing high sensitivity receiver scheme, this paper aims to obtain better positioning performance in urban canyon areas. Generally, the peak with the most power is utilised in high sensitivity receivers; however, this approach is not always optimal in multipath environments. Noting that signal correlation peaks may be separated in the Doppler domain by a long coherent integration time, a peak identification scheme is proposed in this work, which yields better positioning performance. It is shown that most of the multipath peaks are removed in the receiver after using the proposed algorithm.

A new Bayesian approach for multiple satellite faults detection and exclusion is proposed by introducing a classification variable to each satellite observation. If we treat this classification variable as random and assume a prior distribution for it, then a rule for satellite fault detection and exclusion based on the posterior probabilities of the classification variables is constructed under the framework of Bayesian hypothesis testing. Secondly, the Gibbs sampler is introduced to compute the posterior probabilities of the classification variables. Then the implementation for a Bayesian Receiver Autonomous Integrity Monitoring (RAIM) algorithm is designed with the Gibbs sampler. Finally, different schemes are designed to evaluate the performance of the new Bayesian RAIM algorithm in the case of multiple faults. We compare the method in this paper with the Range Consensus (RANCO) method. Experiments illustrate that the proposed algorithm in this paper is capable of detecting and eliminating multiple satellite faults, and the probability of correctly detecting faults is high.

Ocean currents impose a negative effect on Autonomous Underwater Vehicle (AUV) underwater target searches, which lengthens the search paths and consumes more energy and team effort. To solve this problem, an integrated algorithm is proposed to realise multi-AUV cooperative search in dynamic underwater environments with ocean currents. The proposed integrated algorithm combines the Biological Inspired Neurodynamics Model (BINM) and Velocity Synthesis (VS) method. Firstly, the BINM guides a team of AUVs to achieve target search in underwater environments; BINM search requires no specimen learning information and is thus easier to apply to practice, but the search path is longer because of the influence of ocean current. Next the VS algorithm offsets the effect of ocean current, and it is applied to optimise the search path for each AUV. Lastly, to demonstrate the effectiveness of the proposed integrated approach, simulation results are given in this paper. It is proved that this integrated algorithm can plan shorter search paths and thus the energy consumption is lower compared with BINM.