Transfer Alignment (TA) is the initialisation process of the Inertial Navigation System (INS) of an air-launched weapon before its release by using the data from the aircraft INS. The aim of this paper is to improve the TA performance by taking into account the aircraft flexures deterministically. The developed procedure neither requires captive carry tests for determination of flexures nor increases the size of the problem, and can be used in real-time missions of any type of military aircraft. The procedure is evaluated for the Velocity Match (VM) method as well as the Velocity and Attitude Match (VAM) method, which are applied through a Kalman Filter (KF). Using a short-time Wing-Rock (WR) manoeuvre, the results of both methods are compared to each other for two cases in which either the flexures are taken into account deterministically, or modelled as noise by assuming that they are unknown. Standard deviations of the errors and the Circular Error Probable (CEP) variations have shown that the TA performance of the VAM method can be much improved if aircraft flexures are incorporated deterministically into the method. The improved performance makes possible target of opportunity missions at shorter weapon ranges, and it decreases target strike errors.

The transverse navigation system has been designed and developed to solve the challenges of navigation in polar regions. However, considerable theoretical errors are introduced into the system when the spherical Earth model is adopted. To tackle this problem, a transverse navigation mechanism under the ellipsoidal Earth model has been proposed in this research and the application regions of the proposed algorithm are specified and evaluated through error analysis. The analysis shows the presented transverse navigation system works in both polar and part of the non-polar regions. Field tests were conducted to evaluate the navigation performance in Nanjing, a non-polar region. A novel experimental method, where the field test data in mid-latitude areas was used to simulate the real Inertial Measurement Unit (IMU) data and the reference information in polar regions, was adopted to investigate the performance of the proposed algorithm in polar areas. The results show: that in the mid-latitude areas, the presented transverse navigation system achieves the same accuracy as the traditional inertial navigation system and that in polar regions, the proposed transverse mechanism outperforms the traditional method with a much lower error in longitude and yaw.

Micro-Electro-Mechanical System (MEMS)-based inertial technology has recently evolved. It holds remarkable potential as the future technology for various navigation related applications. This is mainly due to the significant reduction in size, cost, and weight of MEMS sensors. A major drawback of low-cost MEMS-based inertial sensors, however, is that their output signals are contaminated by high-level noise. Unless the high frequency noise component is suppressed, optimizing the pre-filtering methodology cannot be achieved. This paper proposes a neural network-based de-noising model for MEMS-based inertial data. A modular, three-layer feedforward neural network trained using the back-propagation algorithm is used for this purpose. Simulated and real MEMS-based inertial data sets are used to validate the model. It is shown that the model is capable of reducing the noise of the Crossbow's AHRS300CA IMU data by over one order of magnitude without altering the stochastic nature of the original signal. This is of utmost importance in developing a generic stochastic model for MEMS-based inertial data. A comparison between the developed neural network model and the wavelet de-noising method is made to further validate the model. It is shown that achieving the same level of noise suppression with wavelet-based de-noising model changes the stochastic characteristics of original signal.

The Precise Point Positioning (PPP) concept enables centimetre-level positioning accuracy by employing one Global Navigation Satellite System (GNSS) receiver. The main advantage of PPP over conventional Real Time Kinematic (cRTK) methods is that a local reference network infrastructure is not required. Only a global reference network with approximately 50 stations is needed because reference GNSS data is required for generating precise error correction products for PPP. However, the current implementation of PPP is not suitable for some applications due to the long time period (i.e. convergence time of up to 60 minutes) required to obtain an accurate position solution. This paper presents a new method to reduce the time required for initial integer ambiguity resolution and to improve position accuracy. It is based on combining GPS and GLONASS measurements to calculate the float ambiguity positioning solution initially, followed by the resolution of GPS integer ambiguities.

The results show that using the GPS/GLONASS float solution can, on average, reduce the time to initial GPS ambiguity resolution by approximately 5% compared to using the GPS float solution alone. In addition, average vertical and horizontal positioning errors at the initial ambiguity resolution epoch can be reduced by approximately 17% and 4%, respectively.

This paper was presented at ETT99/SATNAV99 Conference, held by DGON/NIN in Potsdam, Germany on 9th November 1999.

In the foreseeable future, Automatic Identification Systems (AIS) will be installed aboard all sea-going ships larger than 300 g.r.t. These new devices broadcast data at regular and short intervals, by means of which the ship can be identified and tracked. Further data can be transmitted to provide additional information on the ship's motion, navigational status, intended voyage and cargo. The transmissions can be received by every interested party having an appropriate receiver both at sea and ashore. With these capabilities, AIS adds a new information quality to maritime traffic, which is relevant not only for ship/ship but also for VTS/ship interaction. This paper describes the genesis of the new systems and their operational and technical aspects. It continues with a discussion of their standard and extended applications and their – so far – unexploited potential. Where relevant, the results of R&D work and practical tests are mentioned.

Relative position awareness is a vital premise for the implementation of emerging intelligent transportation systems. However, commercial Global Satellite Navigation Systems (GNSS) receivers do not satisfy the requirements of these applications. Fortunately, Cooperative Positioning (CP) systems, based on inter-vehicle communications, have improved performance of relative positioning in a Vehicular Ad Hoc Network (VANET). CP techniques rely primarily on measurements from the Global Positioning System (GPS) to deliver measurements or positions that describe the location of individual vehicles. In urban environments, the reduced quality or complete unavailability of GPS measurements challenges the effectiveness of any CP algorithm. In this paper, a new enhanced tightly–coupled CP technique is presented by adding the measurements from low-cost inertial sensors and the Doppler shift of the carrier of Dedicated Short-Range Communications (DSRC) signals. In the enhanced CP method proposed here, vehicles communicate their Inertial Measurement Unit (IMU) data and GPS measurements. Each vehicle fuses the GPS measurements and IMU data and the inter-node range-rates based on the Doppler shift of the carrier of DSRC signals. Based on analytical and experimental results, in a full GPS coverage environment, the new tight integration CP outperforms tight CP with Inertial Navigation System (INS), tight CP and differential GPS by at least by 6%, 15%, and 28%, respectively. In a GPS outage, the performance improvement can be up to 60%, 55%, and 66% respectively.

Large volumes of data collected by the Automatic Identification System (AIS) provide opportunities for studying both single vessel motion behaviours and collective mobility patterns on the sea. Understanding these behaviours or patterns is of great importance to maritime situational awareness applications. In this paper, we leveraged AIS trajectories to discover vessel spatio-temporal co-occurrence patterns, which distinguish vessel behaviours simultaneously in terms of space, time and other dimensions (such as ship type, speed, width etc.). To this end, available AIS data were processed to generate spatio-temporal matrices and spatio-temporal tensors (i.e., multidimensional arrays). We then imposed a sparse bilinear decomposition on the matrices and a sparse multi-linear decomposition on the tensors. Experimental results on a real-world dataset demonstrated the effectiveness of this methodology, with which we show the existence of connection among regions, time, and vessel attributes.

The paper discusses the development of an adaptive Kalman filter for pipeline surveying applications. Various measurement models are developed and numerically tested using a real natural gas pipeline dataset. Since, for tactical-grade IMUs, odometer-derived velocity measurements alone cannot yield good results, two non-holonomic constraints are augmented to make the three-direction measurement model. Smoothing computation is also applied to yield the best trajectory using all the information contained in the past, current and future measurements. The trajectory errors are within 10–20 m for extreme cases, and within 10 m for most cases. RMS of the trajectory errors is expected to be about 2·5 m.

This paper describes the United States Coast Guard's efforts regarding Long Range Identification & Tracking (LRIT) of ships. Among those elements included in the LRIT plan, automatic identification systems (AIS), typically envisioned as a short-range system, may also play a long-range role. AIS vessel tracking may be accomplished through high elevation shore sites allowing reception of AIS signals to nominally 24 nm that loosely puts it in the category of a short-range sensor. Additionally, the US Coast Guard is conducting research and development efforts to determine the feasibility of using AIS receive capability on low earth orbit (LEO) satellites and high altitude, long endurance (HALE) airships or balloons. In parallel with the developing technical capabilities, the US approach with the International Maritime Organization (IMO), and domestic legislation and regulations are also addressed. Finally, a vision for the implementation of LRIT is offered. The paper was presented on 9 November 2004 at Church House, London during the Institute's NAV 04 Conference.

This paper proposes a method that combines compact real-time kinematic (RTK) and reference station (RS) networking techniques, and shows that this approach can reduce both the temporal and spatial decorrelation error. The compact RTK method compatibility with all the conventional network RTK systems, i.e., Master-Auxiliary Concept (MAC), Virtual Reference Stations (VRS), and Flächen-Korrektur Parameter (FKP), is examined theoretically in this paper. To prove that the compact RTK approach is not only valid, but also helpful to the network RTK system, a field test was held using one hour of Receiver Independent Exchange Format (RINEX) data logged every second from Continuously Operating Reference Stations (CORS). No matter which network RTK method is applied, the Compact Network RTK approach resolves the ambiguity of the carrier phase in 10–40 s and determines position with 6–7 cm horizontal and 7–8 cm vertical error (95%) in a 100 by 100 km region. Moreover, the Compact Network RTK approach enables network RTK service providers to reduce the data-link bandwidth for correction messages to 5–700 bps (bit/s) down from several thousand bps, currently 9600 bps of GPRS/GSM, without a severe degradation of accuracy.

This study investigates the accuracy of an online Precise Point Positioning (PPP) service operated by the Geodetic Survey Division of Natural Resources Canada (NRCan), Canadian Spatial Reference System (CSRS)-PPP, by using single/dual-frequency Global Positioning System (GPS) data collected by dual-frequency geodetic-grade and Original Equipment Manufacturer (OEM) board type single-frequency GPS receivers. In this work, a kinematic test was carried out in Halic Bay (Golden Horn), Istanbul, Turkey, to assess the performance of the PPP method in a dynamic environment. Based on this study, it can be concluded that the coordinates estimated from the online CSRS-PPP service have a potential of about metre-level accuracy by processing single frequency data collected by an OEM receiver and about a decimetre to a few centimetres level accuracy by processing dual frequency data collected by a geodetic-grade receiver. In general, results show that the PPP technique has become a significant alternative to the conventional relative (differential) positioning techniques (i.e., Differential GPS (DGPS), Real-time Kinematic (RTK)). The technique does not suffer from the drawbacks of the DGPS technique and has potential to provide the same position accuracy without the requirement for a reference station. Consequently, it has been concluded that the PPP technique may be effectively used in marine applications due to its ease of use and provision of high accuracy, as well as being able to offer reduced field operational costs.

This paper describes a fully autonomous real-time in-motion alignment algorithm for Strapdown Inertial Navigation Systems (SINS) in land vehicle applications. Once the initial position is available, the vehicle can start a mission immediately with accurate attitude, position and velocity information determined within ten minutes. This is achieved by two tightly coupled stages, that is, real-time Double-vector Attitude Determination Coarse Alignment (DADCA) and Backtracking Fine Alignment (BFA). In the DADCA process, the vehicle motion is omitted to roughly estimate the attitude at the very start of the alignment. Meanwhile, attitude quaternions and velocity increments are extracted and recorded. The BFA process utilises the stored data and exploits the Non-Holonomic Constraints (NHC) of a vehicle to obtain virtual velocity measurements. A linear SINS/NHC Kalman filter with mounting angles as extended states is constructed to improve the fine alignment accuracy. The method is verified by three vehicle tests, which shows that the accuracy of alignment azimuth is 0·0358° (Root Mean Square, RMS) and the positioning accuracy is about 15 m (RMS) at the end of the alignment.

Suitable route planning is related to the safety and economy of navigation. However, route planning has become increasingly complex over the years and the planning process requires a large amount of oceanic environmental information. In order to use the oceanic environmental information effectively and improve the efficiency of route planning, this research employed a Geographic Information System (GIS) as the platform for enabling two-phase automatic route generation design. Firstly, through GIS's spatial data management, spatial analysis and geometric computation capability, the presence of the obstacle is detected and candidate routes are automatically generated. These are provided to the evolutionary algorithm as the basis for preliminary population calculation. Then, a specially designed evolutionary algorithm is used for route elimination to obtain the optimal route, resulting in the most-recommended routes that encompass safety and economy. This technique is more efficient than evolutionary computation techniques that use traditional random searches. At the same time, this targets safety and economy, providing a reference for developing a route planning strategy.

In low-cost self-contained pedestrian navigation systems, traditional Pedestrian Dead Reckoning (PDR) solutions utilize accelerometers to derive the speed as well as the distance travelled, and obtain the walking heading from magnetic compasses or gyros. However, these measurements are sensitive to instrument errors and disturbances from ambient environment. To be totally different from these signals in nature, the electromyography (EMG) signal is a typical kind of biomedical signal that measures electrical potentials generated by muscle contractions from the human body. This kind of signal would reflect muscle activities during human locomotion, so that it can not only be used for speed estimation, but also disclose the azimuth information from the contractions of lumbar muscles when changing the direction of walking. Therefore, investigating how to utilize the EMG signal for PDR is interesting and promising. In this paper, a novel EMG-based speed estimation method is presented, including setup of the EMG equipment, pre-processing procedure, stride detection and stride length estimation. Furthermore, this method suggested is compared with the traditional one based on accelerometers by means of several field tests. The results demonstrate that the EMG-based method is effective and its performance in PDR can be comparable to that of the accelerometer-based method.

In the liner shipping market carriers share container slots to offer better service and realize economies of scale. This paper studies slot co-allocation planning for a joint fleet in a round trip for a shipping alliance in the liner shipping industry. In particular, a conceptual model is developed based on joint fleet and slot co-allocation management. The factors affecting slot co-allocation planning are explored in detail. A large-scale integer programming model is formulated to guide carriers in an alliance in pursuing an optimal slot co-allocation strategy. In contrast to the existing research, this approach leads to a more accurate representation of the situation for cooperative services in the liner shipping market. Extensive numerical experiments based on a true Asia-Europe cooperative route of COSCO and HANJIN show that the proposed model can be efficiently solved by LINGO11.0 for the case study. The computational results suggest that the mechanism and model can be used to benefit carriers in making better decisions in shipping cooperation services.

Precise Point Positioning/Inertial Navigation System (PPP/INS) integrated navigation based on PPP and low-accuracy INS is often used to provide position and attitude information for vehicle-mounted or airborne mobile mapping systems. With proper processing, the position accuracy of PPP/INS is comparable to that of Differential Global Navigation Satellite System (DGNSS)/INS, but the accuracy of the attitude, especially the yaw angle, cannot be guaranteed. However, the yaw angle is crucial for mobile mapping systems. To compensate for the insufficiency of PPP/INS, we have designed a Multi-Antenna GNSS (MAGNSS)/INS integrated navigation system. First, the attitude determination method using MAGNSS is presented in detail. Then, the MAGNSS attitude is combined with the PPP position and velocity as measurements for integration with the INS. Thus, PPP/INS integrated navigation was improved to MAGNSS/INS integrated navigation. Finally, a three-hour car-borne test was conducted to evaluate the performance of the proposed method. The results indicate that the attitude determined from MAGNSS is accurate and stable over time. Compared to PPP/INS, MAGNSS/INS integrated navigation can improve the attitude accuracy significantly because of the inclusion of MAGNSS attitude.

Map matching has been widely applied in car navigation systems as an efficient method to display the location of vehicles on maps. Various map-matching algorithms have been proposed. Inevitably, the correctness of the map matching is closely related to the accuracy of positioning sensors, such as GPS or Dead Reckoning (DR), and the complexity of the road network and map, especially in urban areas where the GPS signal may be constantly blocked by buildings and the road network is complicated. The existing map matching algorithms cannot resolve the positioning problems under all circumstances. They sometimes give the wrong position estimates of the car on road; the result is called mismatching. In order to improve the quality of map matching, a deep understand of the accuracy of sensor errors on mismatching is important. This paper analyses various factors that may affect the quality of map matching based on extensive tests in Hong Kong. Suggestions to improve the success rate of map matching are also provided.

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.

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.

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.