Zero velocity update (ZUPT) is widely discussed for error restriction in land vehicle Inertial Navigation Systems (INSs) and wearable pedestrian INSs to overcome the problems of Global Positioning System (GPS) unavailability in urban canyons or indoor scenarios. In this paper, an online smoothing method for ZUPT-aided INSs is presented. By introducing the Rauch–Tung–Striebel (RTS) smoothing method into the ZUPT-aided INS, position errors can be effectively restrained not only at stop points but during the whole trajectory. By integrating reverse navigation with a ZUPT smoother, the method realises forward and real-time processing. Compared with existing approaches, it can improve the position accuracy in real time without any other sensors, which is well suited for applications on high-accuracy navigation in GPS-challenging environments. Accuracy test results with different Inertial Measurement Units (IMUs) show that the developed method can significantly decrease position errors from hundreds or thousands of metres to below ten metres. During the whole trajectory, the online smoothing method ensures the maximum position errors at non-stop points can reach the same level of accuracy at stop points. A delay test result proves that the delay of the reverse online smoothing method proposed in this paper is much shorter than existing online smoothing methods.

The traditional Kalman filtering-based transfer alignment methods largely depend on prior information for initialisation. However, the initialisation process is hard to fulfil on a moving base. In this paper, a type of inertial measurement vector integration matching for optimisation-based transfer alignment and calibration is proposed to estimate the misalignment between the Master Inertial Navigation System (MINS) and Slave Inertial Navigation System (SINS), and main inertial sensor error parameters of SINS, including bias and scale factor error. In contrast to filter techniques, the proposed method has the capability of self-initialisation and provides a new idea to solve the alignment and calibration problem. No prior information is needed. Moreover, the integration time interval for the matching inertial measurement vector is selected by considering both the observation degree of inertial sensor error parameters and the noise effect. Simulation results demonstrate that the proposed method has faster convergence and is more accurate than Kalman filter techniques.

Ship surveillance is important in maritime management. Space-borne Synthetic Aperture Radar (SAR), High Frequency Surface Wave Radar (HFSWR) and the Automatic Identification System (AIS) are three main sensors for the ship surveillance of large maritime areas. Fusion of these sensors' measurements can produce an accurate ship image distribution in a surveillance area. Data association is fundamental to data fusion. A Maximum Likelihood (ML) association algorithm with multi-feature improvements is proposed to increase detection accuracy and reduce false alarms. The tested features are position, size, heading and velocity. First, the ship measurement model is established. Then, the problem of data association for SAR, HFSWR and AIS is formulated as a multi-dimensional assignment problem. In the data assignment process, Jonker-Volgenant-Castanon (JVC) and Lagrangian relaxation algorithms are applied. Simulation results show that the algorithm proposed here can improve the association accuracy compared with the Nearest Neighbour (NN) and the position-only ML algorithms, using the additional features of length and velocity. Real data experiments illustrate that the algorithm can enhance target identification and reduce false alarms.

In a waist-worn Pedestrian Navigation System (PNS) based on Dead-Reckoning (DR), heading drift caused by Micro-Electro-Mechanical System (MEMS) gyro bias is an essential factor affecting DR accuracy. Considering the characteristics of pedestrian navigation and the poor bias repeatability of MEMS gyros, this paper presents a standing calibration method for MEMS gyro bias. The current gyro biases for each boot can be calibrated accurately in the initial stage before walking. Since the attitude angles calculated by the output data from magnetic sensor and accelerometers do not drift, this paper applies the reverse algorithm of attitude updating to calculate the angular velocities of human motion. Then the gyro biases at each moment can be acquired by subtraction operation between the measured angular velocities from gyros and the calculated angular velocities of human motion. Finally, in order to restrain the random error caused by sensor noise, the calculated biases in the initial stage are smoothed, and consequently the optimal estimate of current gyro biases after each boot can be obtained. Still and dynamic turntable experiments and a walking experiment are performed to compare and analyse the proposed method and the Zero Angular Rate Update (ZARU) method. Experimental results show that the proposed method can also calibrate the gyro bias accurately in the case of body swaying.

A robust state estimation technique based on the Huber-based Cubature Kalman Filter (HCKF) is proposed for Global Positioning System (GPS) navigation processing. The Cubature Kalman Filter (CKF) employs a third-degree spherical-radial cubature rule to compute the Gaussian weighted integration, such that the numerical instability induced by round-off errors can be avoided. In GPS navigation, the filter-based estimation of the position and velocity states can be severely degraded due to contaminated measurements caused by outliers or deviation from a Gaussian distribution assumption. For the signals contaminated with non-Gaussian noise or outliers, a robust scheme combining the Huber M-estimation methodology and the CKF framework is beneficial where the Huber M-estimation methodology is used to reformulate the measurement information of the CKF. GPS navigation processing using the HCKF algorithm has been carried out and the performance has been compared to those based on the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and CKF approaches. Simulation and experimental results presented in this paper confirm the effectiveness of the method.

Nowadays, the most widely used method for estimating location of autonomous vehicles in real time is the use of Global Navigation Satellite Systems (GNSS). However, positioning in urban environments using GNSS is hampered by poor satellite geometry due to signal obstruction created by both man-made and natural features of the urban environment. The presence of obstacles is the reason for the decreased number of observed satellites as well as uncertainty of GNSS positioning. It is possible that in some sections of the vehicle route there might not be enough satellites necessary to fix position. It is common to use software for static GNSS measurement campaign planning, but it is often only able to predict satellite visibility at one point. This article presents a proposal for dynamic GNSS mission planning using a Digital Terrain Model (DTM) and dead reckoning. The methodology and sample results of numerical experiments are also described. They clearly show that proper dynamic GNSS mission planning is necessary in order to complete a task by an autonomous vehicle in an obstructed environment.

The Unscented Kalman Filter (UKF) is a well-known nonlinear state estimation method. It shows superior performance at nonlinear estimation compared to the Extended Kalman Filter (EKF). This paper is devoted to an investigation between UKF and EKF with different feedback control modes in vehicle navigation. Theoretical formulation, simulation and field tests have been carried out to compare the performance of UKF and EKF. The simulation and test results demonstrate that the estimated state of a UKF relies on the measurements and is less sensitive to historical model information. The results also indicate that UKF has benefits for prototype model design due to avoidance of calculation of a Jacobian matrix. EKF, however, is more computationally efficient and more stable.

This critical review addresses 26 studies of Wayfinding conducted in a real-world setting (large-scale), and with subjects using a map or an aerial photograph in their navigation. The purpose of the review is to summarise how previous surveys have been conducted and to provide some methodological advice for future research. Research design has consequently been the main priority, emphasising methods, measures, participants, context and types of map. Both qualitative and quantitative studies should be initiated, but the lack of research is significant within quantitative studies, the link between laboratory-based and real-world studies, studies where the subjects have been children with map experience and adult novices, and studies conducted in complex settings. Type of map appears to be an important determinant of performance and should be varied according to age and level of skills. Alignment of a map could be a possible source of error.

This study examines maritime routes between ports along the Atlantic coast of the US, utilising Automated Identification System (AIS) data for the years 2010 through 2012. The delineation of vessel routes conducted in this study was motivated by development planned for offshore Wind Energy Areas (WEAs) along the Atlantic coast of the US and the need to evaluate the effect of these development areas on commercial shipping. To this end, available AIS data were processed to generate commercial vessel tracks for individual vessels, though cargo vessels are the focus in this study. The individual vessel tracks were sampled at transects placed along the Atlantic coast. The transect samples were analysed and partitioned by voyages between Atlantic ports to facilitate computation of vessel routes between ports. The route boundary analysis utilised a definition from UK guidance in which routes' boundaries encompassed 95% of the vessel traffic between ports. In addition to delineating route boundaries, we found multi-modal transverse distributions of vessels for well-travelled routes, which indicated preference for lanes of travel within the delineated routes.

Container shipping is one of the most important transport modes in international trade. For a large-scale container shipping network, an optimisation model is proposed to minimise the total shipping cost of container flow. Considering the directional property of the container shipping process, a steering vector search pattern is incorporated in a Max-Min Ant System (MMAS) solving algorithm, and an orthogonal array method is adopted in parameter settings. In numerical experiments, a traditional ant colony algorithm and an MMAS algorithm based on a steering vector search pattern are used respectively to optimise the network of five node scales. The results show that a steering vector search pattern can speed up the computation process and improve the optimisation effect.

The acquisition and tracking strategies of the BeiDou navigation satellite signals are affected by the modulation of Neumann-Hoffman code (NH code), which increases the complexity of receiver baseband signal processing. Based on the analysis of probability statistics of the NH code, a special sequence of incoming signals is proposed to evade the bit transitions caused by the NH code, and an NH Code Evasion and Stripping method (NCES) based on the NH-pre-modulated code is proposed. The NCES can be applied in both 20-bit NH code and 10-bit NH code. The fine acquisition eliminates the impact of NH code on the traditional tracking loop. These methods were verified with a BeiDou PC-based software-defined receiver using the actual sampled signals. Compared with other acquisition schemes which try to determine or ignore the NH code phase, the NCES needs fewer incoming signals and the actual runtime is greatly reduced without sacrificing much time to search in the secondary code dimension, and the success rate of acquisition is effectively improved. An extension of Fast Fourier Transform (FFT)-based parallel code-phase search acquisition gives the NCES an advantage in engineering applications.

Vessel traffic in the Arctic is expanding in volume both within and transiting the region, yet the infrastructure necessary to support modern ship navigation is lacking. This includes aids to navigation such as buoys and beacons that can be difficult to place and maintain in this hostile environment that stretches across vast distances. The results of research are described which determine whether virtual electronic Aids to Navigation (eAtoN) existing entirely as digital information objects can overcome the practical limitations of physical aids to navigation (AtoN) and Automatic Identification System (AIS) radio eAtoN. Capabilities unique to virtual eAtoN that are not available using either physical or AIS radio technologies are also examined including dynamic and real time properties and immunity to Global Navigation Satellite System (GNSS) and AIS spoofing, aliasing, denial of service attacks and service outages. Conclusions are provided describing potential methods of deployment based upon similar concepts already in use.

Accurate navigation systems are required for future pinpoint Mars landing missions. A radio ranging augmented Inertial Measurement Unit (IMU) integrated navigation system concept is considered for the Mars entry navigation. The uncertain system parameters associated with the Three Degree-Of-Freedom (3-DOF) dynamic model, and the measurement systematic errors are considered. In order to improve entry navigation accuracy, this paper presents the Multiple Model Adaptive Rank Estimation (MMARE) filter of radio beacons/IMU integrated navigation system. 3-DOF simulation results show that the performances of the proposed navigation filter method, 70·39 m estimated altitude error and 15·74 m/s estimated velocity error, fulfill the need of future pinpoint Mars landing missions.

In this paper, we propose an algorithm for tuning both the kinematic and measurement noise Variance–Covariance (VCV) matrices to produce a more robust and adaptive Kalman filter. The proposed algorithm simultaneously considers both observation outliers and abrupt changes. This algorithm may be divided into two basic parts: 1. Robust estimation, from which the position components of the filtering estimates and the equivalent weight factor matrix can be obtained; and 2. Adaptive estimation, from which the adaptive kinematic noise VCV tuning matrix is calculated. To demonstrate the efficiency of our algorithm, we process a set of kinematic Global Positioning System (GPS) data received from a rover mounted on an aeroplane. The processing results are found to be very satisfactory, with observation outliers and abrupt changes detected and dealt with accordingly. The detailed calculation procedure for the adaptive VCV tuning matrix is also described.

A strapdown inertial navigation system and celestial navigation system integrated autonomous navigation scheme is proposed in this paper, using the navigation information obtained from Earth sensors and star sensors. To eliminate the adverse effect caused by the asymmetry of Earth infrared radiance, the relationship between Earth infrared radiance brightness and effective horizon height is found. According to the relationship as well as the measuring principle of the Earth sensor, this paper derives a function to correct the measurement of the Earth sensor. Then, the angle-distance of stars can be calculated, and using this information, we can estimate the navigation information of a ballistic missile by least square estimation. The simulation results show that the error of Earth infrared radiance has a great effect on the navigation precision, and by using the correction scheme, this adverse effect can be greatly mitigated. This correction scheme is available and effective.

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.

The volume of maritime vessel data, such as available from the Automatic Identification System (AIS), places considerable burden on systems designed and developed to manage data pertaining to maritime traffic. A properly designed and implemented data management infrastructure can provide benefits to the maritime domain awareness research community by supporting data volumes, diverse user needs, and product management. Such an infrastructure has been constructed on a modest budget by utilising open-source technologies. This paper describes the Maritime Situational Awareness Research Infrastructure (MSARI), and the design of the underlying database to meet data volume and user analysis needs. The resulting infrastructure currently handles input rates of approximately two billion vessel reports per month. This work is of potential benefit to those in the navigational community interested in the long-term storage and usage of global vessel data such as that available from AIS.