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Airborne-Pseudolite (A-PL) systems have been proposed to augment Global Navigation Satellite Systems (GNSSs) in difficult areas where GNSS-only navigation cannot be guaranteed due to signal blockages, signal jamming, etc. One of the challenges in realising such a system is to determine the coordinates of the A-PLs to a high accuracy. The GNSS Precise Point Positioning (PPP) technique is a possible alternative to differential GNSS techniques such as those that generate Real-Time Kinematic (RTK) solutions. To enhance the A-PL positioning performance in GNSS challenged areas, it is assumed that inter-PL range measurements are also used in addition to GNSS measurements. When processing these new measurements, cross-correlations among A-PL estimated states introduced during measurement updates need to be accounted for so as to obtain consistent estimated states. In this paper, a distributed algorithm based on a Split Covariance Intersection Filter (SCIF) is proposed. Three commonly used means of implementing the SCIF algorithm are analysed. Another challenge is that real-time GNSS PPP relies on the use of precise satellite orbit and clock information. One problem is that these real-time orbit and satellite clock error corrections may not be always available, especially for moving A-PLs in challenging environments when signal outages occur. To maintain A-PL positioning accuracy using GNSS PPP, it is necessary to predict these error corrections during outages. Different prediction models for orbit and clock error corrections are discussed. A test was conducted to evaluate the A-PL positioning based on GNSS PPP and inter-PL ranges, as well as the performance of error prediction modelling. It was found that GNSS PPP combined with inter-PL ranges could achieve better converged positioning accuracy and a reduction in convergence time of GNSS PPP. However, the performance of GNSS PPP with inter-PL ranges was degraded by observing A-PLs with limited positioning accuracy. Although the performance improvement achieved by the SCIF-based distributed algorithms was smaller than that by the centralised algorithm, greater robustness in dealing with deteriorated observed A-PLs' trajectory data was demonstrated by the distributed algorithms. In addition, short-term prediction models were analysed, and their performance was shown to reduce the effect of error correction outages on A-PL positioning accuracy.
We describe an integrated navigation system based on Global Navigation Satellite Systems (GNSS), an Inertial Navigation System (INS) and terrestrial ranging technologies that can support accurate and seamless indoor-outdoor positioning. To overcome severe multipath disturbance in indoor environments, Locata technology is used in this navigation system. Such a “Locata-augmented” navigation system can operate in different positioning modes in both indoor and outdoor environments. In environments where GNSS is unavailable, e.g. indoors, the proposed system is designed to operate in the Locata/INS “loosely-integrated” mode. On the other hand, in outdoor environments, all GNSS, Locata and INS measurements are available, and all useful information can be fused via a decentralised Federated Kalman filter. To evaluate the proposed system for seamless indoor-outdoor positioning, an indoor-outdoor test was conducted at a metal-clad warehouse. The test results confirmed that the proposed navigation system can provide continuous and reliable position and attitude solutions, with the positioning accuracy being better than five centimetres.
The Japanese Quasi-Zenith Satellite System (QZSS) is a regional satellite navigation system capable of transmitting navigation signals that are compatible and interoperable with other Global Navigation Satellite Systems (GNSS). In addition to navigation signals, QZSS also transmits augmentation signals, e.g. the L-band Experimental (LEX) signal. The LEX signal is unique for QZSS in delivering correction messages such as orbits and clock information that enable real-time Precise Point Positioning (PPP). This study aims to evaluate the availability of the LEX signal as well as the quality of the broadcast correction messages for real-time PPP applications. The system is tested in both static and kinematic positioning modes. The results show that the availability of the LEX signal is 60% when the QZSS satellite elevation is at 30° and above 90% when the satellite is above 40° elevation. Centimetre-level position accuracy can be obtained for static PPP processing after two hours of convergence using the current MADOCA-LEX (Multi-GNSS Advanced Demonstration of Orbit and Clock Analysis) correction messages transmitted on the LEX signal; and decimetre-level point positioning accuracy can be obtained for kinematic PPP processing.
To meet the accuracy, integrity, continuity and availability required for many navigation applications the Locata technology can provide an alternative to satellite-based navigation in difficult Global Navigation Satellite System (GNSS) signal environments, especially for applications in port areas and in constricted waterways. Unlike GNSS constellations, a LocataNet – a local constellation of LocataLites – can be designed specifically for different environments to avoid signal blockages, interference or poor geometry. By using Locata technology, the optimal performance within particular areas can always be guaranteed. This paper demonstrates the influence of LocataNet configuration on the reliability and integrity of the Locata positioning system. The performance of the Locata system is investigated using the Receiver Autonomous Integrity Monitoring (RAIM) concept. Fault Detection and Exclusion (FDE) algorithm performance is validated through the computation of the Dilution of Precision (DOP), the Horizontal Protection Level (HPL) and the correlation coefficient between two failure modes that can indicate the quality of fault identification. The experimental analysis shows that a good configuration of LocataLites will enhance the accuracy and reliability of the navigation system.
This paper presents the results of a new multipath mitigating antenna “V-Ray” for use with terrestrial ranging signals in severe multipath indoor environments. The V-Ray antenna – as used in the Locata positioning system – forms tight beams that provide line-of-sight range measurements as well as azimuth measurements. To take advantage of these two types of measurements a new navigation algorithm – Position and Attitude Modelling System (PAMS) – is proposed for processing carrier phase and azimuth measurements via an unscented Kalman filter. The PAMS can output the complete navigation parameters of position, velocity, acceleration and attitude simultaneously. The indoor test was conducted in a metal warehouse and the results confirmed that the horizontal positioning solutions had an accuracy of better than four centimetres and an orientation accuracy of better than 1°.
Outliers in terrain data are an obstacle to achieving accurate and robust solutions of Underwater Terrain Relative Navigation (UTRN). If not handled properly, navigation may be degraded or even divergent. To address the problem, this paper proposes a terrain-matching algorithm based on the robust estimation theory. In contrast to the conventional approach, the proposed algorithm can significantly reduce the interference of the outliers. Experimental results confirm the good performance of the proposed method.
An Augmented Relative Navigation System (ARNS) is proposed for autonomous satellite formation flying in low-Earth-orbit (LEO). Inter-satellite ranging systems such as those based on radio frequency transmissions can provide additional observation information, e.g. inter-satellite distance measurement, which can be used to increase the Global Positioning System (GPS) stand-alone observation dimension, or treated as a non-linear equality constraint within a smoothly-constrained Kalman filter. Both approaches are implemented in the proposed ARNS described in this paper. An innovative phase integer ambiguity fixing and feedback scheme is implemented to increase the ambiguity fix rate of the GPS carrier phase measurements. A set of Gravity Recovery and Climate Experiment (GRACE) flight data is used to test and validate the relative navigation performance of the proposed methods. Results indicate that the augmented system can improve relative positioning accuracy by an order of magnitude.
In Global Navigation Satellite System (GNSS) positioning, it is standard practice to apply the Fault Detection and Exclusion (FDE) procedure iteratively, in order to exclude all faulty measurements and then ensure reliable positioning results. Since it is often only necessary to consider a single fault in a Receiver Autonomous Integrity Monitoring (RAIM) procedure, it would be ideal if a fault could be correctly identified. Thus, fault detection does not need to be applied in an iterative sense. One way of evaluating whether fault detection needs to be reapplied is to determine the probability of a wrong exclusion. To date, however, limited progress has been made in evaluating such probabilities. In this paper the relationships between different parameters are analysed in terms of the probability of correct and incorrect identification. Using this knowledge, a practical strategy for incorporating the probability of a wrong exclusion into the FDE procedure is developed. The theoretical findings are then demonstrated using a GPS single point positioning example.
In a car navigation system the conventional information used to guide drivers in selecting their driving routes typically considers only one criterion, usually the Shortest Distance Path (SDP). However, drivers may apply multiple criteria to decide their driving routes. In this paper, possible route selection criteria together with a Multi Objective Path Optimisation (MOPO) model and algorithms for solving the MOPO problem are proposed. Three types of decision criteria were used to present the characteristics of the proposed model. They relate to the cumulative SDP, passed intersections (Least Node Path – LNP) and number of turns (Minimum Turn Path – MTP). A two-step technique which incorporates shortest path algorithms for solving the MOPO problem was tested. To demonstrate the advantage that the MOPO model provides drivers to assist in route selection, several empirical studies were conducted using two real road networks with different roadway types. With the aid of a Geographic Information System (GIS), drivers can easily and quickly obtain the optimal paths of the MOPO problem, despite the fact that these paths are highly complex and difficult to solve manually.
Assisted-Global Navigation Satellite Systems (A-GNSS), or Assisted-Global Positioning Systems (A-GPS) in particular, are now commonly accepted as an effective way to reduce the time-to-first-fix (TTFF) in GNSS-unfriendly environments, e.g. in areas of weak GNSS signals. Today's location-based service (LBS) devices such as GPS-enabled mobile phones and personal digital assistants (PDA) rely on A-GPS; however, such commercial devices are equipped with an integrated A-GPS chip that makes customisation very difficult. The Open Source GNSS Reference Server (OSGRS) provided by the University of New South Wales is an open source Java application that can generate the necessary data for A-GPS clients. The GNSS Reference Interface Protocol (GRIP), based on extensible mark-up language (XML), is employed as the OSGRS interface protocol. This paper describes the current status of OSGRS: a client simulator is available open-source; client software which supports four different types of A-GPS-enabled receivers has been developed and used to test OSGRS. The performance of the OSGRS is analysed based on intensive tests. The challenges for OSGRS and future work are also discussed.
Classically difficult positioning environments often call for augmentation technology to assist the GPS, or more generally the Global Navigation Satellite System (GNSS) technology. The “Locata” ground-based ranging technology offers augmentation, and even replacement, to GPS in such environments. However, like any other system relying on wireless technology, a Locata positioning network also faces issues in the presence of RF interference (RFI). This problem is magnified due to the fact that Locata operates in the licence-free 2·4 GHz Industrial, Scientific and Medical (ISM) band. The licence-free nature of this band attracts a much larger number of devices using a wider range of signal types than for licensed bands, resulting in elevation of the noise floor. Also, harmonics from out-of-band signals can act as potential interferers. WiFi devices operating in this band have been identified as the most likely potential interferer, due partially to their use of the whole ISM band, but also because Locata applications often also may use a wireless network. This paper evaluates the performance of Locata in the presence of both narrow- and wide-band interfering signals. Effects of received interference on both raw measurements and final solutions are reported and analysed. Test results show that Locata performance degrades in the presence of received interference. It is also identified that high levels of received interference can affect Locata carriers even if the interference is not in co-frequency situation with the affected carrier. Finally, Locata characteristics have been identified which can be exploited to mitigate RFI issues.
Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) technologies can overcome the drawbacks of the individual systems. One of the advantages is that the integrated solution can provide continuous navigation capability even during GPS outages. However, bridging the GPS outages is still a challenge when Micro-Electro-Mechanical System (MEMS) inertial sensors are used. Methods being currently explored by the research community include applying vehicle motion constraints, optimal smoother, and artificial intelligence (AI) techniques. In the research area of AI, the neural network (NN) approach has been extensively utilised up to the present. In an NN-based integrated system, a Kalman filter (KF) estimates position, velocity and attitude errors, as well as the inertial sensor errors, to output navigation solutions while GPS signals are available. At the same time, an NN is trained to map the vehicle dynamics with corresponding KF states, and to correct INS measurements when GPS measurements are unavailable. To achieve good performance it is critical to select suitable quality and an optimal number of samples for the NN. This is sometimes too rigorous a requirement which limits real world application of NN-based methods.
The support vector machine (SVM) approach is based on the structural risk minimisation principle, instead of the minimised empirical error principle that is commonly implemented in an NN. The SVM can avoid local minimisation and over-fitting problems in an NN, and therefore potentially can achieve a higher level of global performance. This paper focuses on the least squares support vector machine (LS-SVM), which can solve highly nonlinear and noisy black-box modelling problems. This paper explores the application of the LS-SVM to aid the GPS/INS integrated system, especially during GPS outages. The paper describes the principles of the LS-SVM and of the KF hybrid method, and introduces the LS-SVM regression algorithm. Field test data is processed to evaluate the performance of the proposed approach.
This paper analyses flight trial results to study the overall performance and limitations of a GPS/Pseudolite/INS integration approach for aircraft precision approach and landing applications. For this purpose, the series of approaches were flown at Wedderburn Airfield, Australia. The analysed results show that pseudolite signals strengthen the ranging signal availability and the satellite geometry. Most of the geometry enhancement is found in the vertical position component, improving the accuracy of the aircraft's altitude. Furthermore, the results reveal that the inclusion of a pseudolite enhances both internal and external reliabilities. A dramatic improvement of the external reliability in the vertical component is observed.
The central task of GPS/INS integration is to effectively blend GPS and INS data together to generate an optimal solution. The present data fusion algorithms, which are mostly based on Kalman filtering (KF), have several limitations. One of those limitations is the stringent requirement on precise a priori knowledge of the system models and noise properties. Uncertainty in the covariance parameters of the process noise (Q) and the observation errors (R) may significantly degrade the filtering performance. The conventional way of determining Q and R relies on intensive analysis of empirical data. However, the noise levels may change in different applications. Over the past few decades adaptive KF algorithms have been intensively investigated with a view to reducing the influence of the Q and R definition errors. The covariance matching method has been shown to be one of the most promising techniques. This paper first investigates the utilization of an online stochastic modelling algorithm with regards to its parameter estimation stability, convergence, optimal window size, and the interaction between Q and R estimations. Then a new adaptive process noise scaling algorithm is proposed. Without artificial or empirical parameters being used, the proposed adaptive mechanism has demonstrated the capability of autonomously tuning the process noise covariance to the optimal magnitude, and hence improving the overall filtering performance.
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.
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