The problem of indoor navigation is investigated using a Commercial Off-the-Shelf (COTS) pseudolite system. The system is operated in synchronous and asynchronous mode. It is shown that, when the system is operated in synchronous mode, it is unsuitable for deep indoor operations: in complex propagation environments, the synchronisation required for metre level navigation is difficult to achieve and different solutions have to be adopted. Two asynchronous approaches are thus considered and indoor navigation with metre level accuracy is demonstrated using C/N0 measurements. The approaches use a modified Receiver Signal Strength (RSS) navigation algorithm and a weighted centroid technique, respectively. In both cases, a pre-filtering stage has been adopted to enhance the quality of C/N0 measurements. The two methods have been compared under different operating conditions and the advantages and drawbacks of the two techniques have been analysed. The experiments demonstrate that metre level accuracy can be achieved using asynchronous pseudolites. These results are particularly encouraging since they were obtained without exploiting map constraints and prior knowledge of the user position.

A pulse phase estimation of an X-ray pulsar with the aid of vehicle orbital dynamics is proposed. The original continue-time X-ray pulsar signal model is modified to be a term of vehicle position and velocity varying with time, and a modified definition of pulse time of arrival is given. The modified signal model is further linearized around the predicted position and velocity of the vehicle to the second order. The initial phase and the coefficients of the extended signal model can be estimated by maximum likelihood estimator. Some simulations are performed to verify the method and show the method has robustness to the initial error within initial state of the vehicle and is capable of handling the phase-estimation problem for pulsars with low fluxes.

Underwater position data is a key requirement for the navigation and control of unmanned underwater vehicles. The proposed navigation scheme can be used in any vessel or boat for any shallow water vehicle. This paper presents the position estimation algorithm developed for shallow water Remotely Operated Vehicles (ROVs) using attitude data and Doppler Velocity Log data with the initial position from the Global Positioning System (GPS). The navigational sensors are identified using the in-house developed simulation tool in MATLAB, based on the requirement of a position accuracy of less than 5%. The navigation system is built using the identified sensors, Kalman filter and navigation algorithm, developed in LabVIEW software. The developed system is tested and validated for position estimation, with an emulator consisting of a GPS-aided fibre optic gyro-based inertial navigation system as a reference, and it is found that the developed navigation system has a position error of less than 5%.

A ship lock zone represents a specific area on waterway, and control of the ship lockage process requires a comprehensive approach. This research is a practical application of a Mamdani-type fuzzy inference system and particle swarm optimisation to control this process. It presents an optimisation process that adapts control logic to the desired criteria. The initially proposed Fuzzy Expert System (FES) was developed using suggestions from lockmasters (ship lock operators) with extensive experience. Further optimisation of the membership function parameters of the input variables was performed to achieve better results in the local distribution of ship arrivals. The presented fuzzy logic-based expert system was designed as part of a Programmable Logic Controller (PLC) and Supervisory Control And Data Acquisition (SCADA) system to support decision making and control. The developed fuzzy algorithm is a rare application of artificial intelligence in navigable canals and significantly improves performance of the ship lockage process. This adaptable FES is designed to be used as a support in decision-making processes or for the direct control of ship lock operations.

A dual-axis rotational Inertial Navigation System (INS) has received wide attention in recent years because of high performance and low cost. However, some errors of inertial sensors such as stochastic errors are not averaged out automatically during navigation. Therefore a Twice Position-fix Reset (TPR) method is provided to enhance accuracy of a dual-axis rotational INS by compensating stochastic errors. According to characteristics of an azimuth error introduced by stochastic errors of an inertial sensor in the dual-axis rotational INS, both an azimuth error and a radial-position error are much better corrected by the TPR method based on an optimised error propagation equation. As a result, accuracy of the dual-axis rotational INS is prominently enhanced by the TPR method, as is verified by simulations and field tests.

This paper proposes a Radio Frequency Identification (RFID)/ in-vehicle sensors fusion strategy for vehicle positioning in completely Global Positioning System (GPS)-denied environments such as tunnels. The strategy employs a two-step approach, namely, the calculation of the distances between the RFID tags and the reader, and then the global fusion estimation of vehicle position. First, a Least Square Support Vector Machine (LSSVM) algorithm is developed to obtain distance. Next a novel Federated Unscented Kalman Filter (FUKF) is designed to realise the global fusion. The decentralised federated filter is adopted to combine the data from RFID and in-vehicle sensors, and the UKF is employed to design a local filter since it has better ability to deal with a nonlinear problem than an Extended Kalman Filter (EKF). Due to the optimised layout of RFID tags and the application of the decentralised filter, the number of tags is reduced. Finally, the feasibility and effectiveness of the proposed strategy are evaluated through experiments.

Accurate velocity estimates are critical in highly dynamic positioning, airborne gravimetry, and geophysics applications. This paper focuses on the evaluation of the performance of velocity estimation using the BeiDou navigation satellite system (BDS) alone and integrated Global Positioning System (GPS)/BDS. Firstly, we analyse and compare the position-derivation method and analytical method which are used to calculate BDS satellite velocity from broadcast ephemeris. Results show that the accuracy of the estimated velocity by position-derivation method can be within 1 mm/s and better than that of the analytical method. Secondly, velocity estimation tests were carried out both in static and kinematic modes. The results show that: 1) the accuracy of BDS velocity estimation is in the same order of magnitude to that of GPS; 2) Compared with a single navigation system, the stability and accuracy of velocity estimation can be remarkably improved by integrated GPS/BDS, especially under conditions of poor observation; 3) Compared with Helmert variance component estimation, it is more appropriate and efficient to assign the weights of different types of observations using equivalent weight ratio. Finally, the ionospheric influence on velocity estimation with single-frequency observations can reach several mm/s; this influence can be significantly mitigated by using ionosphere-free combination observations.

An evaluation method named vague set is proposed to describe the suitability of a geomagnetic map. It is based on the Fuzzy Decision Making (FDM) method, and overcomes the FDM model's shortcomings that favouring and opposing content cannot be taken into account simultaneously. The membership function and non-membership function are used to define the influence of the geomagnetic map parameters on map suitability, including standard deviation, information entropy, roughness and slope variance. The weight of each geomagnetic map parameter is calculated by establishing an optimisation model. Vague set data are divided into four types after classification, and Weighted Score Function Values (WSFVs) of matching areas are obtained by using the Weighted Score Function (WSF) method. Then, WSFV of each matching area are compared to select an optimal area. Simulation results demonstrate that geomagnetic map suitability is positively proportional to the function value, and matching error is negatively proportional to the WSFV of the matching area.

In waist-worn pedestrian navigation systems, the periodic vertical acceleration peak signal at body centre of gravity is widely used for detecting steps. Due to vibration and waist shaking interference, accelerometer output signals contain false peaks and thus reduce step detection accuracy. This paper analyses the relationship between periodic acceleration at pedestrian centre of gravity and walking stance during walking. An adaptive dual-window step detection method is proposed based on this analysis. The peak signal is detected by a dual-window and the window length is adjusted according to the change in step frequency. The adaptive dual window approach is shown to successfully suppress the effects of vibration and waist shaking, thereby improving the step detection accuracy. The effectiveness of this method is demonstrated through step detection experiments and pedestrian navigation positioning experiments respectively. The step detection error rate was found to be less than 0·15% in repeated experiments consisting of 345 steps, while the longer (about 1·3 km) pedestrian navigation experiments demonstrated typical positioning error was around 0·67% of the distance travelled.

This study develops a Zero-Inflated Negative Binomial (ZINB) regression model to evaluate the factors influencing the loss of human life in shipping accidents using ten years' ship accident data in the South China Sea. The ZINB regression model results show that the expected loss of human life is higher for collision, fire/explosion, contact, grounding, hull damage, machinery damage/failure and capsizing accidents occurring in adverse weather conditions during night periods. Sinking can cause the highest loss of life compared to all other accident types. There are fewer fatalities and missing people when the ship involved in an accident is moored or docked. The results also reveal that the loss of human life is associated with shipping accidents occurring far away from the coastal area/harbour/ports. The results of this study are beneficial for policy-makers in proposing efficient strategies to reduce shipping accident casualties in the South China Sea.

The integration of Precise Point Positioning (PPP) with Inertial Navigation Systems (INS) has been very intensively developed and widely applied in multiple areas. The integrated navigation system is able to provide high accuracy position and attitude with a single receiver. However, the cycle slip caused by high dynamics, signal lock and low satellite elevation will decrease accuracy and degrade the system performance. In this paper, an inertial aided cycle slip detection and repair method is applied in PPP/INS tightly coupled navigation to obtain higher accuracy navigation information. The inertial information is introduced to the wide-lane combination observation to avoid the noise and multipath from pseudorange. Detail error analysis is given to determine the critical value of INS position accuracy which makes inertial aided wide-lane combination have better performance and a new decision variable is constructed. The inertial aided cycle slip detection and repair scheme that replaces the ambiguity re-initialisation is applied in PPP. An experiment was performed to validate the new algorithm. The results indicate that the inertial aided decision variable has higher accuracy than the traditional Melbourne-Wübbena decision variable. The inertial aided scheme can efficiently detect and repair the small cycle slip (from 1 to 6) and large cycle slip (from 10 to 20). The inertial-aided cycle slip detection method introduced in PPP resolution will remove the error by ambiguity re-initialisation.

Despite the extent of works done on modelling port water collisions, not much research effort has been devoted to modelling collisions at port anchorages. This paper aims to fill this important gap in the literature by applying the Navigation Traffic Conflict Technique (NTCT) to measuring the collision potentials in anchorages and for examining the factors contributing to collisions. Building on the principles of the NTCT, a collision potential measurement model and a collision potential prediction model were developed. These models were illustrated by using vessel movement data of the anchorages in Singapore port waters. Results showed that the measured collision potentials are in close agreement with those perceived by harbour pilots. Higher collision potentials were found in anchorages attached to the shoreline and international fairways, but not at those attached to confined water. Higher operating speeds, larger numbers of isolated danger marks and day conditions were associated with reduction in the collision potentials.

Navigation and manoeuvring simulators are increasingly being used in research centres to conduct complex navigation experiments and analyses. The structure and capabilities of simulation software make it possible to reproduce any conditions, including weather conditions. The complex mathematical models of marine environmental conditions that are being implemented nowadays take into account various sea wave models, which makes simulation tests more realistic. This paper deals with issues related to evaluating and verifying vessel simulation models based on real-world studies. As a result of the present research project, a methodology for comparing vessel simulation models with their real-life counterparts was developed. A measurement platform was created for the purpose of carrying out real-world studies; it is available at the Institute of Marine Navigation and Hydrography of the Polish Naval Academy. One important research step involved developing a procedural algorithm for making real-world measurements. This paper presents the results of using this platform in comparative tests of the manoeuvring elements of real and simulated vessels.

Routing in a multimodal urban public transportation network, according to the user's preferences, can be considered as a multi-objective optimisation problem. Solving this problem is a complicated task due to the different and incompatible objective functions, various modes in the network, and the large size of the network. In this research, two optimisation algorithms are considered for solving this problem. The multi-colony and multi-pheromone Ant Colony Optimisation (ACO) algorithms are two different modes of the Multi-Objective ACO (MOACO) algorithm. Moreover, according to the acquired information, the algorithms implemented in the public transportation network of Tehran consist of four modes. In addition, three objective functions have been simultaneously considered as the problem's objectives. The algorithms are run with different initial parameters and afterwards, the results are compared and evaluated based on the different obtained routes and with the aid of the convergence and repeatability tests, diversity and convergence metrics.

The UnScented QUaternion Estimator (USQUE) has been approved as a promising substitute for the extended Kalman filter when applied in the Standard Inertial Navigation Equations (SINE)-based inertial navigation system/global positioning system integration. However, the expensive computational burden makes it untenable in real time applications. In this paper, a computationally efficient filtering algorithm called Marginalised USQUE (MUSQUE) is derived by embedding the Marginalised Unscented Transformation (MUT) that is applicable to nonlinear systems with a linear substructure into the widely used USQUE. The contributions of the MUSQUE developed here are twofold. Firstly, the SINE are reconstructed to be only nonlinear in the attitude quaternion while linear in velocity and position, which makes the MUT potentially applicable in the USQUE. Secondly, the quaternion and generalised Rodrigues parameter-based sigma points are propagated simultaneously in the MUSQUE to deal with the dimensional mismatching hierarchy of the USQUE. Compared with the USQUE the number of sigma points can be decreased substantially, thereby making the applied MUSQUE computationally tenable. The experimental results show that the proposed MUSQUE has nearly identical performance with the USQUE but with much reduced computational burden.

The Global Positioning System (GPS) has become widespread in many civilian applications. GPS signals are vulnerable to interference and even low-power interference can easily spoof GPS receivers. In this paper, two techniques are proposed based on correlators and adaptive filtering to diminish the effect of spoofing on GPS-based positioning. The suggested algorithms are implemented in the tracking loop of the receiver. As a first method, a high-resolution correlator is utilised to avoid big parts of the influence of interference. To improve the results, a multicorrelator technique is also employed. In the second method, an adaptive filter is used for estimating the parameters of authentic plus spoof signals. Interference elimination is performed by subtracting the estimated conflict effects from the measured correlation function. These techniques provide easy-to-implement quality assurance tools for anti-spoofing. As a primary step, in this article, the proposed algorithms have been implemented in a Software Receiver (SR) to prove the concept of idea in multipath-free environments.

BeiDou satellites transmit triple-frequency signals, which bring substantial benefits to carrier phase Ambiguity Resolution (AR). The traditional geometry-free model Three-Carrier Ambiguity Resolution (TCAR) method looks for a suitable combination of carrier phase and code-range observables by searching and comparing in the integer range, which limits the AR success probability. By analysing the error characteristics of the BeiDou triple-frequency observables, we introduce a new procedure to select the optimal combination of carrier phase and code observables to resolve the resolution of Extra-Wide-Lane (EWL) and Wide-Lane (WL) ambiguity. We also investigate a geometry-free and ionosphere-eliminated method for AR of the Medium-Lane (ML) and Narrow-Lane (NL) observables. In order to evaluate the performance of the improved TCAR method, real BeiDou triple-frequency observation data for different baseline cases were collected and processed epoch-by-epoch. The results show that the improved geometry-free TCAR method increases the single epoch AR success probability by up to 90% for short baseline and 80% for long baseline. The A perfect (100%) AR success probability can also be effortlessly achieved by averaging the float ambiguities over just tens of epochs.

For the integrated navigation system, the correctness and the rapidity of fault detection for each sensor subsystem affects the accuracy of navigation. In this paper, a novel fault detection method for navigation systems is proposed based on Gaussian Process Regression (GPR). A GPR model is first used to predict the innovation of a Kalman filter. To avoid local optimisation, particle swarm optimisation is adopted to find the optimal hyper-parameters for the GPR model. The Fault Detection Function (FDF), which has an obvious jump in value when a fault occurs, is composed of the predicted innovation, the actual innovation of the Kalman filter and their variance. The fault can be detected by comparing the FDF value with a predefined threshold. In order to verify its validity, the proposed method is used in a SINS/GPS/Odometer integrated navigation system. The comparison experiments confirm that the proposed method can detect a gradual fault more quickly compared with the residual chi-squared test. Thus the navigation system with the proposed method gives more accurate outputs and its reliability is greatly improved.