Global navigation satellite system (GNSS) positioning in dense urban areas remains a challenge due to the signal reflection by buildings, namely multipath and non-line-of-sight (NLOS) reception. These effects degrade the performance of low-cost GNSS receivers such as in those smartphones. An effective three-dimensional (3D) mapping aided GNSS positioning method is proposed to correct the NLOS error. Instead of applying ray-tracing simulation, the signal reflection points are detected based on a skyplot with the surrounding building boundaries. The measurements of the direct and reflected signals can thus be simulated and further used to determine the user's position based on the measurement likelihood between real measurements. Verified with real experiments, the proposed algorithm is able to reduce the computational load greatly while maintaining a positioning accuracy within 10 metres of error in dense urban environments, compared with the conventional method of ray-tracing based NLOS corrected positioning.

With the existing GPS, the replenishment of GLONASS and the launching of Galileo there will be three satellite navigation systems in the future, with a total of more than 80 satellites. So it can be expected that the performance of the global navigation satellite system (GNSS) will be greatly improved, especially in urban environments. This paper studies the potential benefits of GPS/GLONASS/Galileo integration in an urban canyon – Hong Kong. The navigation performances of four choices (GPS alone, GPS+GLONASS, GPS+Galileo and GPS+GLONASS+Galileo) are evaluated in terms of availability, coverage, and continuity based on simulation. The results show that there are significant improvements in availability, coverage and continuity, by using GPS+GLONASS+Galileo compared with the other choices. But the performance is still not good enough for most navigation applications in urban environments.

The absence of a regional, open water vessel collision risk assessment system endangers maritime traffic and hampers safety management. Most recent studies have analysed the risk of collision for a pair of vessels and propose micro-level risk models. This study proposes a new method that combines density complexity and a multi-vessel collision risk operator for assessing regional vessel collision risk. This regional model considers spatial and temporal features of vessel trajectory in an open water area and assesses multi-vessel near-miss collision risk through danger probabilities and possible consequences of collision risks via four types of possible relative striking positions. Finally, the clustering method of multi-vessel encountering risk, based on the proposed model, is used to identify high-risk collision areas, which allow reliable and accurate analysis to aid implementation of safety measures.

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.

Managing the transit vessel traffic in the Strait of Istanbul is a highly complex operation since vessels, weather and water conditions, and a set of regulations affect its operation significantly. At the present time, the Vessel Traffic Services (VTS) operators manage the traffic based on some fundamental rules. After discussions with the VTS, in this report, we present a mathematical formulation of the scheduling process currently in place and validate it by comparing its results with scheduling decisions made by the operators in some days of 2005. The results are highly promising. The fundamental philosophy of the algorithm is to schedule the vessels with highest waiting time first while giving priority to large vessels carrying dangerous cargo. Our goal has been to incorporate the algorithm into a simulation model designed to be used for risk analysis purposes. The proposed algorithm can be slightly altered and used for traffic scheduling in other waterways as well.

The Strait of Istanbul is one of the most congested and risky waterways in the world. Navigation patterns have been investigated using Automatic Indentification System (AIS) data collected over a long period. 1·5 billion AIS messages, gathered over a year from 309,000 moving vessels in the Strait were stored in a Structured Query Language (SQL) database. Grid-based analysis is used to track the time, number, position, type, dimension, heading, speed and course over ground of ships. Local traffic, whose effect on maritime risk has often been neglected, is found to dominate transit traffic by a ratio of eight to one. Vessel distributions indicate that the most common lengths of vessels are 100 m and 170 m. Draught analysis shows a net transfer of goods from north to south. Southbound vessels are more likely to exceed the enforced speed limit due to predominant currents. Courses indicate that the local traffic strongly affects navigation patterns, especially at sectors with sharp turns. All these results help to understand the navigation patterns of ships and give the necessary input to assist in predicting maritime risk.

The errors of an inertial navigation system (INS) in response to gyros' errors can be effectively reduced by the rotation technique, which is a commonly used method to improve an INS's accuracy. A gyro's error consists of a deterministic contribution and a stochastic contribution. The compensation effects of gyros' deterministic errors are clear now, but the compensation effects of gyros' stochastic errors are as yet unknown. However, the compensation effects are always needed in a rotational inertial navigation system's (RINS) error analysis and optimization study. In this paper, the compensation effects of gyros' stochastic errors, which are modelled as a Gaussian white (GW) noise plus a first-order Markov process, are analysed and the specific formulae are derived. During the research, the responses of an INS's and a RINS's position error equations to gyros' stochastic errors are first analysed. Then the compensation effects of gyros' stochastic errors brought by the rotation technique are discussed by comparing the error propagation characteristics in an INS and a RINS. In order to verify the theory, a large number of simulations are carried out. The simulation results show a good consistency with the derived formulae, which can indicate the correctness of the theory.

Underwater terrain matching navigation technology is an important research area for the underwater navigation of Autonomous Underwater Vehicles (AUVs). Terrain matching navigation can realise long-term, subtle, all-weather, and high-precision underwater AUV navigation. In this paper, the research status of the application of AUV underwater terrain matching navigation is reviewed, the system composition, theory and terrain matching methods of underwater terrain matching navigation are summarised and the advantages of a multi-beam bathymetric system in underwater terrain matching navigation are discussed. The current research thoughts are summarised, the key issues are pointed out, and possible future development trends are discussed.

Course keeping for ships is vital for automatic navigation in marine transportation. To improve the control effect and reduce the energy output of the controller, this article proposes an improved concise backstepping controller based on a Lyapunov candidate function by introducing a nonlinear function of course error to replace the course error itself in the feedback loop. The procedure of nonlinear controller design has been reduced from two steps to one step using information from controlled plant to construct the Lyapunov candidate function. Compared with the pure backstepping control, the proposed improved algorithm reserves the nonlinear item of the system, and possesses a strong disturbance rejection ability and robustness to the mathematical model uncertainty. The algorithm given here has advantages of simplified construction method, satisfactory control effect, robustness and energy saving.

To ensure high accuracy results from an integrated GPS/INS system, the carrier phase observables have to be used to update the filter's states. As a prerequisite the integer ambiguities must be resolved before using carrier phase measurements. However, a cycle slip that remains undetected (and uncorrected) will significantly degrade the filter's performance. In this paper, an algorithm that can effectively detect and identify any type of cycle slip is presented. The algorithm uses additional information provided by the INS, and applies a statistical technique known as the cumulative-sum (CUSUM) test. In this approach, cycle slip decision values can be computed from the INS-predicted position (due to the fact that its short-term accuracy is very high), and the CUSUM test used to detect cycle slips (as it is very sensitive to abrupt changes of mean values). Test results are presented to demonstrate the effectiveness of the proposed algorithm.

A loosely coupled Inertial Navigation System (INS) and Global Positioning System (GPS) are studied, particularly considering the constant lever arm effect. A five-element vector, comprising a craft's horizontal velocities in the navigation frame and its position in the earth-centred and earth-fixed frame, is observed by GPS, and in the presence of lever arm effect, the nonlinear observation equation from the state vector to the observation vector is established and addressed by the correction stage of an unscented Kalman filter (UKF). The conditionally linear substructure in the nonlinear observation equation is exploited, and a computationally efficient refinement of the UKF called marginalized UKF (MUKF) is investigated to incorporate this substructure where fewer sigma points are needed, and the computational expense is cut down while the high accuracy and good applicability of the UKF are retained. A performance comparison between UKF and MUKF demonstrates that the MUKF can achieve, if not better, at least a comparable performance to the UKF, but at a lower computational expense.

Transfer alignment is the process of initialising and calibrating a weapon INS using data from the host aircraft's navigation system. To determine which transfer alignment technique performs best, different design options have been assessed, supported by simulation work. The dependence of transfer alignment performance on environmental factors, such as manoeuvres, alignment duration, lever arm and inertial sensor quality has also been studied. ‘Rapid’ alignment, using attitude as well as velocity measurements was found to perform better than ‘conventional’ techniques using only velocity. Innovative developments include the estimation of additional acceleration and gyro states and estimation of force dependent relative orientation, which has enabled robust alignment using wing rock manoeuvres, which do not require the pilot to change trajectory. Transfer alignment has been verified in real-time by flight trials on a Tornado aircraft. In addition, techniques have been developed to prevent transients in the aircraft integrated navigation solution following GPS re-acquisition after an outage of several minutes from disrupting the transfer alignment process.

Single-frequency Precise Point Positioning (PPP) using a Global Navigation Satellite System (GNSS) has been attracting increasing interest in recent years due to its low cost and large number of users. Currently, the single-frequency PPP technique is mainly implemented using GPS observations. In order to improve the positioning accuracy and reduce the convergence time, we propose the combined GPS/GLONASS Single-Frequency (GGSF) PPP approach. The approach is based on the GRoup And PHase Ionospheric Correction (GRAPHIC) to remove the ionospheric effect. The performance of the GGSF PPP was tested using both static and kinematic datasets as well as different types of precise satellite orbit and clock correction data, and compared with GPS-only and GLONASS-only PPP solutions. The results show that the GGSF PPP accuracy degrades by a few centimetres using rapid/ultra-rapid products compared with final products. For the static GGSF PPP, the position filter typically converges at 71, 33 and 59 minutes in the East, North and Up directions, respectively. The corresponding positioning accuracies are 0·057, 0·028 and 0·121 m in the East, North and Up directions. Both positioning accuracy and convergence time have been improved by approximately 30% in comparison to the results from GPS-only or GLONASS-only single-frequency PPP. A kinematic GGSF PPP test was conducted and the results illustrate even more significant benefits of increased accuracy and reliability of PPP solutions by integrating GPS and GLONASS signals.

Maritime video surveillance has become an essential part of the vessel traffic services system, intended to guarantee vessel traffic safety and security in maritime applications. To make maritime surveillance more feasible and practicable, many intelligent vision-empowered technologies have been developed to automatically detect moving vessels from maritime visual sensing data (i.e., maritime surveillance videos). However, when visual data is collected in a low-visibility environment, the essential optical information is often hidden in the dark, potentially resulting in decreased accuracy of vessel detection. To guarantee reliable vessel detection under low-visibility conditions, the paper proposes a low-visibility enhancement network (termed LVENet) based on Retinex theory to enhance imaging quality in maritime video surveillance. LVENet is a lightweight deep neural network incorporating a depthwise separable convolution. The synthetically-degraded image generation and hybrid loss function are further presented to enhance the robustness and generalisation capacities of LVENet. Both full-reference and no-reference evaluation experiments demonstrate that LVENet could yield comparable or even better visual qualities than other state-of-the-art methods. In addition, it takes LVENet just 0⋅0045 s to restore degraded images with size 1920 × 1080 pixels on an NVIDIA 2080Ti GPU, which can adequately meet real-time requirements. Using LVENet, vessel detection performance can be greatly improved with enhanced visibility under low-light imaging conditions.

This paper presents a concept for selecting a fixed time ocean route via a prefixed grid of points. The ship is to sail with a fixed pitch propeller rotating at a constant rate N. The starting and arrival points A and B are given, as well as the starting and arrival times t0 and t1. The criterion is to obtain a route with a minimal value of N. The method is designed for a discrete navigating system. Thus it can be seen as an application of Bellman's Dynamic Programming Theory1. The ship's speed is estimated with the use of a modern algorithm, based on a well founded and well tested hydrodynamic model.

The multipath effect and Non-Line-Of-Sight (NLOS) reception of Global Positioning System (GPS) signals both serve to degrade performance, particularly in urban areas. Although receiver design continues to evolve, residual multipath errors and NLOS signals remain a challenge in built-up areas. It is therefore desirable to identify direct, multipath-affected and NLOS GPS measurements in order improve ranging-based position solutions. The traditional signal strength-based methods to achieve this, however, use a single variable (for example, Signal to Noise Ratio (C/N0)) as the classifier. As this single variable does not completely represent the multipath and NLOS characteristics of the signals, the traditional methods are not robust in the classification of signals received. This paper uses a set of variables derived from the raw GPS measurements together with an algorithm based on an Adaptive Neuro Fuzzy Inference System (ANFIS) to classify direct, multipath-affected and NLOS measurements from GPS. Results from real data show that the proposed method could achieve rates of correct classification of 100%, 91% and 84%, respectively, for LOS, Multipath and NLOS based on a static test with special conditions. These results are superior to the other three state-of-the-art signal reception classification methods.

This paper introduces an approach for solving a safe ship trajectory planning problem. The algorithm, utilising the concept of a discrete artificial potential field and a path optimisation algorithm, calculates an optimised collision-free trajectory for a ship. The method was validated by simulation tests with the use of real navigational data registered on board the research and training ship Horyzont II. Results of simulation studies demonstrate that the approach is capable of finding a collision-free trajectory in near-real time, and this proves its applicability in commercial collision avoidance systems for ships. The paper contributes to the development of decision support systems for ships and autonomous navigation.

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.

Application of map-matching techniques to GPS positions can provide accurate vehicle location information in challenging situations. The Hidden Markov Model (HMM) is a statistical model that is well known for providing solutions to temporal recognition applications such as text and speech recognition. This paper introduces a novel map-matching algorithm based on HMM for GPS-based wheelchair navigation. Given GPS positions, a hidden Markov chain model is established by using both geometric data and the topology of sidewalk segments. The map-matching algorithm employs the Viterbi algorithm to estimate correct sidewalk segments as hidden states in a HMM in order to match GPS trajectory on the corresponding segment sequence. The HMM-based map-matching algorithm was validated on a campus sidewalk network for wheelchair navigation. The results show an improvement in tracking a wheelchair in dense urban conditions both in accuracy and in computational time.

Map Matching (MM) algorithms are usually employed for a range of transport telematics applications to correctly identify the physical location of a vehicle travelling on a road network. Two essential components for MM algorithms are (1) navigation sensors such as the Global Positioning System (GPS) and dead reckoning (DR), among others, to estimate the position of the vehicle, and (2) a digital base map for spatial referencing of the vehicle location. Previous research by the authors (Quddus et al., 2003; Ochieng et al., 2003) has developed improved MM algorithms that take account of the vehicle speed and the error sources associated with the navigation sensors and the digital map data previously ignored in conventional MM approaches. However, no validation study assessing the performance of MM algorithms has been presented in the literature. This paper describes a generic validation strategy and results for the MM algorithm previously developed in Ochieng et al. (2003). The validation technique is based on a higher accuracy reference (truth) of the vehicle trajectory as determined by high precision positioning achieved by the carrier-phase observable from GPS. The results show that the vehicle positions determined from the MM results are within 6 m of the true positions. The results also demonstrate the importance of the quality of the digital map data to the map matching process.