This paper describes preliminary studies towards a navigational Knowledge-based System currently in progress within the School of Engineering and Technology Management at Liverpool Polytechnic.

Gravity Aided Navigation (GravAN) and Geomagnetism Aided Navigation (GeomAN) are two methods for correcting Inertial Navigation System (INS) errors of Autonomous Underwater Vehicles (AUVs) without compromising the AUV mission. One requirement for applying these methods is the relatively large field feature variations along the navigation trajectory. But in some regions with small gravity or geomagnetic variation, it is very difficult to achieve a reliable result solely by GravAN or GeomAN. If these two methods were combined, gravity and geomagnetism information could be complementary and the aided navigation ability could potentially be improved, especially in those regions when neither method is valid. Based on that concept, a Gravity and Geomagnetism Combined Aided Navigation (GGCAN) method is consequently proposed in this paper as a possible solution. The Gravity Anomaly Grid (GAG2) and Earth Geomagnetic Anomaly Grid (EMAG2) are utilized as the background databases, and then a Multiple Model Adaptive Estimation (MMAE) is adopted to obtain an optimal estimated navigation position. Furthermore, an Optimal Weight Allocation Principle (OWAP) is introduced to the combined GravAN and GeomAN methods, together with a weighted average. In simulation, two special regions in the Western Pacific Ocean were chosen to test the proposed method. The results show that GGCAN can improve the position success rate and reduce the error, compared to GravAN or GeomAN. Results indicate that the GGCAN method proposed in this study is able to improve the accuracy and reliability of an aided navigation system.

Ultra-wideband (UWB) ranging radios, an emerging technology that offers precise, short distance range measurements are investigated as a method to augment carrier-phase GPS positioning. A commercially available UWB ranging system is used in a tightly-coupled GPS and UWB real-time kinematic (RTK) system. The performance of the tightly-coupled system is evaluated in static and kinematic testing. This work demonstrates that UWB errors can be successfully estimated in a real-time filter. The results of static testing show that the integrated solution provides better accuracy, better ability to resolve integer ambiguities and enhanced fixed ambiguity solution availability compared with GPS alone. In kinematic testing in a degraded GPS environment, sub-decimetre accuracy was maintained.

In this paper, we propose a novel dynamic fuzzy ship domain that considers factors associated with both one's own ship and other ships. This is in contrast to existing ship domain models that operate from the perspective of one's own ship, considering only the factors relevant to that ship. First, the domain was determined by considering the distance of the ships around one's own ship in different directions, which sufficiently accounts for factors associated with one's own ship and with the other ships. At the same time, the factors were chosen based on an analysis of their importance. Second, the domain was dynamic and modelled by establishing the relationship between the domain size and the chosen factors in different directions from one's own ship, obtained by using neural networking and wavelet decomposition. Third, the domain was developed using fuzzy sets related to different safety levels and this related the model to the practical applications of estimating spatial collision risk. The model was calibrated using Automatic Identification System (AIS) data of vessel movements in the Bohai Sea and the northern Yellow Sea. The reasonableness and the superiority of establishing a ship domain model considering the factors affecting both one's own ship and other ships were analysed, and the results show that the new model can determine the spatial collision risk of the navigational situation and is thus suitable for use as part of a ship collision avoidance system.

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.

Today's mobile technology features several sensors that when integrated can provide ubiquitous navigation assistance to pedestrians including wheelchair users. Common sensors found in most smartphones are Global Positioning System (GPS), accelerometer, and compass. In this paper, a user's movement pattern recognition algorithm to improve map matching efficiency and accuracy in pedestrian/wheelchair navigation systems/services is discussed. The algorithm integrates GPS positions, orientation data from compass, and movement states recognized from accelerometer data in a client/server architecture. The algorithm is tested in an Android mobile phone, and the results show that the proposed map matching algorithm is efficient and provides good accuracy.

The benefits of integrated INS/GPS systems are well known. However, the knowledge required to jam GPS is becoming public and the hardware to achieve this is basic. When GPS data are unavailable and a low grade INS is used, navigation accuracy quickly degrades to an unacceptable level. The addition of one or more terrain referenced navigation (TRN) systems to an integrated INS/GPS navigation system enables the INS to be calibrated during GPS outages, increasing the robustness of the overall navigation solution. TRN techniques are compared and integration architectures are reviewed. For the initial studies of INS/GPS/TRN integration, radar altimeter based terrain contour navigation (TCN) with a batch processing algorithm is used in conjunction with a centralised integration filter. Four different approaches for using these TCN fixes to calibrate the INS are compared. These are a best fix method, a weighted fix method using a probabilistic data association filter (PDAF) and single and multi-hypothesis versions of the Iterative Gaussian Mixture Approximation of the Posterior (IGMAP) method. Simulation results are presented showing that the single hypothesis IGMAP technique offers the best balance between accuracy, robustness and processing efficiency.

In the work package “Integrated Positioning” of the research project NAVIO (Pedestrian Navigation Systems in Combined Indoor/Outdoor Environments) we are dealing with the navigation and guidance of visitors to our University. The start points are the public transport stops in the surroundings of the Vienna University of Technology and the system users should be guided to certain office rooms or persons. For the user's position determination different location sensors are employed, i.e., for outdoor positioning GPS and dead reckoning sensors, such as a digital compass and gyro for heading determination, accelerometers for the determination of the distance travelled, a barometric pressure sensor for altitude determination and, for indoor areas, location determination using WiFi fingerprinting. All sensors and positioning methods are combined and integrated using a Kalman filter. An optimal estimate of the current location of the user is obtained using the filter. To perform an adequate weighting of the senors in the stochastic filter model, the sensor characteristics and performance were investigated in several tests. The tests were performed in different environments either with free satellite visibility, in urban canyons or inside buildings. The tests have shown that it is possible to determine the user's location continuously with the required precision and that the selected sensors provide a good performance and high reliability. Selected tests results and our approach are presented in the paper.

Operational deep-sea manned submersibles are important underwater vehicles that provide strong technical support for international deep-sea research. Navigation and positioning technologies are expected to facilitate the complete exploitation of the scientific value of samples and data collected by such submersibles, improve their underwater operation efficiency and enhance their safety. This paper first briefly describes six operational deep-sea manned submersibles that can dive to depths of more than 4,500 m. Then, the paper focuses on the navigation and positioning technologies used in these submersibles. Finally, considering the existing problems in the navigation and positioning systems of China's manned submersible Jiaolong, the paper discusses future development trends of the navigation and positioning technologies used in operational deep-sea manned submersibles.

The E5/L5 frequency band for the new Global Navigation Satellite System (GNSS) signals is crowded with aeronautical pulsed emitters. This results in severe degradation of the performance of GNSS receivers. This paper describes a novel technique for estimating and suppressing time-varying pulsed interference signals such as the Distance Measuring Equipment (DME)/Tactical Air Navigation (TACAN) signals generated by these pulsed emitters. The proposed technique involves the integration of the time-based pulse blanker and the wavelet-based interference mitigation technique. Aviation is the key application considered although many other applications such as transport management and navigation, environmental monitoring, and telecommunications would benefit. A performance assessment of the new technique is carried out by determining the degradation of the carrier-to-noise ratio (CNR) at the output of the correlator. The performance of the new technique is compared with the traditional time domain pulse blanking approach. The results show that the proposed technique performs better than both the time domain pulse blanker and the wavelet-based interference mitigation algorithm. Hence, the integrated pulse mitigation approach can be employed to provide an enhanced degree of interference detection and suppression.

In-motion alignment plays an important role in improving the manoeuvring capability of a vehicle, and allows the initialisation of a Strapdown Inertial Navigation System (SINS) while moving. Odometer (OD) aided in-motion alignment is widely adopted owing to its fully self-contained characteristic. This paper proposes a complete in-motion alignment algorithm for a vehicle-carried SINS based on odometer aiding, in which an in-motion coarse alignment method using the integration form of the velocity update equation in the body frame to give a rough initial angle is introduced and a new measurement equation in the body frame with a Kalman filter (KF) for the in-motion fine alignment is established. The advantages of the proposed method are verified by simulation and measured data.

Many countries are making increased efforts to improve marine security and safety and develop ship surveillance techniques to satisfy the increasing demands. Space-borne Synthetic Aperture Radar (SAR) delivers high performance day/night all weather capabilities and a space-based Automatic Identification System (AIS) can give near real time and global coverage. Limited by the development of sensors and data processing techniques, the integration of space-borne SAR and AIS has much to offer ship surveillance. State-of-the-art data fusion methods have generally provided satisfactory performance. However, in high-density shipping or high sea-states, performance quality is less assured. This paper firstly investigates improved data association methods. The association methods based on the position feature are improved, and multi-feature-based association methods are proposed. Then, ship identification and tracking by the integration of space-borne SAR and AIS are researched further. Multi-source data fusion strategy is also investigated. Finally, the discussion is presented and the future works are emphasized in the conclusion.

From the Surface Velocity Program (SVP) drifter current data, a detailed and complete track of strong ocean currents in the north-western Pacific is provided using the bin average method. The focus of this study is on the Kuroshio, the strong western boundary current of the North Pacific flowing northward along the east coast of Taiwan and then turning eastward off southern Japan. With its average flow speed of about 2 knots, the Kuroshio can significantly increase the ship's speed for a “super-slow-steaming” container ship travelling at speeds of 12 knots between the ports of Southeast Asia and Japan. By properly utilizing knowledge of strong ocean currents to follow the Kuroshio on the northbound runs and avoid it on the return trip, considerable fuel can be saved and the transit time can be reduced. In the future, the detailed Kuroshio saving-energy route could be built into electronic chart systems for all navigators and shipping routers.

A redundant Inertial Measurement Unit (IMU) is an inertial sensing device composed of more than three accelerometers and three gyroscopes. This paper analyses the performance of redundant IMUs and their various sensor configurations. The inertial instruments can achieve high reliability for long periods of time only by redundancy. By suitable geometric configurations it is possible to extract the maximum amount of reliability and accuracy from a given number of redundant single-degree-of-freedom gyros or accelerometers. This paper gives a general derivation of the optimum matrix which can be applied to the outputs of any combination of three or more sensors to obtain three orthogonal vector components based on their geometric configuration and error characteristics. Certain combinations of four or more instruments are able to detect an instrument malfunction, and combinations of five have the additional capability of isolating that malfunction to a particular sensor. Finally, this paper offers a major improvement in reliability, although the improvement in accuracy is minor.

Most Differential Global Positioning System (DGPS) correction formats are based on range information, and thus typical DGPS systems can be implemented only on correction message-readable or raw observable-providing devices. There is no other way to improve an already-calculated position than a ‘block shift technique’, which has a very limited applicability. This paper suggests an algorithm to project measurement correction directly to position domain data without requiring raw pseudorange data. By post-processing methodology, we evaluated the performance of our new algorithm compared to conventional DGPS, which requires raw pseudorange data; the observed difference between them was only 0·1 mm . The proposed correction projection algorithm can be used with commercial off-the-shelf receivers that provide National Marine Electronics Association (NMEA) format data. Our testing with a U-blox LEA-5H receiver resulted in a drastic reduction of horizontal Root Mean Square (RMS) error from 4·75 m to 1·09 m.

Trajectory prediction is an important support for analysing the vessel motion behaviour, judging the vessel traffic risk and collision avoidance route planning of intelligent ships. To improve the accuracy of trajectory prediction in complex situations, a Generative Adversarial Network with Attention Module and Interaction Module (GAN-AI) is proposed to predict the trajectories of multiple vessels. Firstly, GAN-AI can infer all vessels’ future trajectories simultaneously when in the same local area. Secondly, GAN-AI is based on adversarial architecture and trained by competition for better convergence. Thirdly, an interactive module is designed to extract the group motion features of the multiple vessels, to achieve better performance at the ship encounter situations. GAN-AI has been tested on the historical trajectory data of Zhoushan port in China; the experimental results show that the GAN-AI model improves the prediction accuracy by 20%, 24% and 72% compared with sequence to sequence (seq2seq), plain GAN, and the Kalman model. It is of great significance to improve the safety management level of the vessel traffic service system and judge the degree of ship traffic risk.

The determination of the correct integer number of carrier cycles (integer ambiguity) is the key to high accuracy positioning with carrier phase measurements from Global Navigation Satellite Systems (GNSS). There are a number of current methods for resolving ambiguities including the Least-squares AMBiguity Decorrelation Adjustment (LAMBDA) method, which is a combination of least-squares and a transformation to reduce the search space. The current techniques to determine the level of confidence (integrity) of the resolved ambiguities (i.e. ambiguity validation), usually involve the construction of test statistics, characterisation of their distribution and definition of thresholds. Example tests applied include ratio, F-distribution, t-distribution and Chi-square distribution. However, the assumptions that underpin these tests have weaknesses. These include the application of a fixed threshold for all scenarios, and therefore, not always able to provide an acceptable integrity level in the computed ambiguities. A relatively recent technique referred to as Integer Aperture (IA) based on the ratio test with a large number of simulated samples of float ambiguities requires significant computational resources. This precludes the application of IA in real time.

This paper proposes and demonstrates the power of an integrity monitoring technique that is applied at the ambiguity resolution and positioning stages. The technique has the important benefit of facilitating early detection of any potential threat to the position solution, originating in the ambiguity space, while at the same time giving overall protection in the position domain based on the required navigation performance. The proposed method uses the conventional test statistic for ratio testing together with a doubly non-central F distribution to compute the level of confidence (integrity) of the ambiguities. Specifically, this is determined as a function of geometry and the ambiguity residuals from least squares based ambiguity resolution algorithms including LAMBDA. A numerical method is implemented to compute the level of confidence in real time.

The results for Precise Point Positioning (PPP) with simulated and real data demonstrate the power and efficiency of the proposed method in monitoring both the integrity of the ambiguity computation and position solution processes. Furthermore, due to the fact that the method only requires information from least squares based ambiguity resolution algorithms, it is easily transferable to conventional Real Time Kinematic (RTK) positioning.

This paper reports on the potential application of interval Kalman filtering techniques in the design of a navigation system for an uninhabited surface vehicle named Springer. The interval Kalman filter (IKF) is investigated for this task since it has had limited exposure for such usage. A state-space model of the Springer steering dynamics is used to provide a framework for the application of the Kalman filter (KF) and IKF algorithms for estimating the heading angle of the vessel under erroneous modelling assumptions. Simulations reveal several characteristics of the IKF, which are then discussed, and a review of the work undertaken to date presented and explained in the light of these characteristics, with suggestions on potential future improvements.

Code Differential Global Positioning System (DGPS) is widely used in satellite navigation and positioning because of its simple algorithm and preferable precision. Multi-Global Navigation Satellite System (GNSS) is expected to enhance the accuracy, reliability and availability of Differential GNSS (DGNSS) positioning. Traditional DGNSS models should set separate clock parameters due to the clock differences between the different systems. Awareness of the Inter-System Bias (ISB) could help to maximise the redundancy of the positioning model, thus improving the performance of multi-GNSS positioning. This paper aims to examine the inter-system bias of GPS/GLONASS/BeiDou (BDS)/Galileo and their benefits in DGNSS positioning. Results show that Differential ISB (DISB) characteristics vary with different receiver types and systems. The size of DISB could reach metre-level and the precision of estimated DISBs can reach approximately several centimetres within tens of epochs. Therefore, a new real-time DGNSS model that accounts for ISB is proposed. After differential ISBs are initialised, positioning with four satellites from arbitrarily the same or different systems can be realised. Moreover, compared with the traditional DGNSS model, the precision of the positioning results with the new model are obviously improved, especially in harsh environments.