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.

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.

Clustering methods that use a similarity measurement for evaluating vessel trajectories are important for mining spatial distribution information in water transportation. To better measure the similarity of vessel trajectories, a novel similarity measure is proposed based on the dynamic time warping distance, which considers the course change of track points and the meaning at the route level. Parallel experiments were conducted based on a month of Automatic Identification System (AIS) data collected from the Zhoushan Islands area, China. After evaluation of the accuracy and the cluster degree, the novel measure demonstrated its capabilities for distinguishing different vessel trajectories and detecting similar vessel trajectories with high accuracy and has a better performance compared to some existing methods.

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.

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.

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.

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.

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.

The safety of maritime transportation has become increasingly important in recent decades. In this paper, a decision model (a multi-objective and multi-layer fuzzy optimisation model) for navigation risk in different sea areas is established. This is done according to the evaluation index system based on relative data extracted and analysed from Automatic Identification Systems (AIS) information and the multi-objective and multi-layer fuzzy optimisation theory. Then, sorted by an optimal relative membership degree vector and calculated from lower layer to higher layer, the sea areas which have higher navigation risk are selected. Finally, the decision model is shown to be scientific and practical since the results from it are basically consistent with real traffic in Chengshantou waters and the results from the fuzzy comprehensive evaluation model. With the decision model, navigation risk judgments in different sea areas can be offered. It can also provide decision making references to the design of ship routing systems, the layout of search and rescue sites, the configuration of rescue forces and the administration of navigation safety.

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.

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.

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.

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.

The purpose of this paper is to describe a statistical method for modelling and accounting for the heavy tail fault-free error distributions that have been encountered in the Local Area Augmentation System (LAAS), the FAA's version of a ground-based augmentation system (GBAS) for GPS. The method uses the Normal Inverse Gaussian (NIG) family of distributions to describe a heaviest tail distribution, and to select a suitable NIG family member as a model distribution based upon a statistical observability criterion applied to the FAA's LAAS prototype error data. Since the independent sample size of the data is limited to several thousand and the tail probability of interest is of the order of 10−9, there is a chance of mismodelling. A position domain monitor (PDM) is shown to provide significant mitigation of mismodelling, even for the heaviest tail that could be encountered, if it can meet certain stringent accuracy and threshold requirements. Aside from its application to GBAS, this paper should be of general interest because it describes a different approach to navigation error modelling and introduces the application of the NIG distribution to navigation error analysis.

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.

Precision inertial navigation depends not only on the quality of the inertial sensors (accelerometers and gyros), but also on the accuracy of the gravity compensation. With a view toward the next-generation inertial navigation systems, based on sensors whose errors contribute as little as a few metres per hour to the navigation error budget, we have analyzed the required quality of gravity compensation to the navigation solution. The investigation considered a standard compensation method using ground data to predict the gravity vector at altitude for aircraft free-inertial navigation. The navigation effects of the compensation errors were examined using gravity data in two gravimetrically distinct areas and a navigation simulator with parameters such as data noise and resolution, supplemental global gravity model noise, and on-track interpolation method. For a typical flight trajectory at 5 km altitude and 300 km/hr aircraft speed, the error in gravity compensation contributes less than 5 m to the position error after one hour of free-inertial navigation if the ground data are gridded with 2 arcmin resolution and are accurate to better than 5 mGal.

Navigating in unfamiliar road environments is a common and demanding cognitive activity. If this cannot be accomplished successfully, there are implications for increased driver workload, delays due to navigation errors, potentially unsafe road behaviour such as late lane changes, and inappropriate traffic management. To enable successful navigation through such environments, it is necessary to understand what the navigation task entails, and what a driver's corresponding information requirements are. This paper reports the results of a study that sought to identify what information is used by drivers when navigating within a range of urban driving environments, how this information is used, and what the implications are for the design of navigation aids.

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.

An integrated multi-sensor vehicle navigation system is presented that uses a low-cost rate gyro and differential odometry to supplement GPS under signal masking conditions such as tree foliage and urban canyons. Signal masking is often accompanied by extreme multi-path in urban centres with tall buildings, and is also found in wooded areas, enclosed car parks, tunnels, etc. The purpose of the system tested is to provide an accuracy of better than 20 metres almost 100% of the time throughout these interruptions, which are assumed to last up to a few minutes. The equipment used is discussed in detail, as is the method used for filtering measurements. Results are presented from tests carried out in an urban core with relatively long periods of signal loss – up to several minutes over a 6-km test circuit. Tests in urban canyons demonstrate that it is difficult to reach the above specifications with aiding from differential odometry alone due to the high precision of the wheel-scale factor required. However, with the use of a rate gyro and odometry, RMS errors are below 20 metres while availability is nearly 100%. Some of the large deviations could probably be better controlled if GPS multi-path errors were detected before they are allowed to corrupt the filtered solution.