Spoofing is a kind of deliberate interference that aims to manipulate global navigation satellite system (GNSS) receivers into counterfeit position solutions. Conventional anti-spoofing methods are implemented prior to the calculation of the position solution, depending on the specific spoofing attack mechanisms. The paper presents a spoofing detection and mitigation method implemented in the position domain. The proposed method projects the correlograms of the visible satellites to a position-clock bias domain to construct the position domain projected correlogram. P(Y) code signatures retrieved from a reference station receiver are used to identify the counterfeit position solution and remove it from the victim receiver. Compared with the conventional single-channel spoofing detection technique, the proposed anti-spoofing method is more robust against thermal noise by combining the energy from multiple satellites. Detailed mathematical derivation of the statistical characteristics of this method is presented. Its effectiveness is validated using a realistic dataset generated by a Spirent GNSS simulator and NordNav wideband front-end. Results show that the proposed algorithm is capable of not only detecting a spoofing attack but also removing the spoofing effect from the victim receiver.

Synchronisation of the received Pseudorandom (PRN) code and its locally generated replica is fundamental when estimating user position in Global Navigation Satellite System (GNSS) receivers. It has been observed through experiments that user position accuracy decreases if sampling frequency is an integer multiple of the nominal code rate. This paper provides an accuracy analysis based on the number of samples and the residual code phase of each code chip. The outcomes reveal that the distribution of residual code phases in the code phase range [0, 1/ns), where ns is the number of samples per code chip, is the root cause of accuracy degradation, rather than the ratio between sampling frequency and nominal code rate. Doppler frequencies, coherent integration periods, front-end filter bandwidths and received Carrier to Noise ratios (C/N0) also influence receiver accuracy. Also provided are a sampling frequency selection guideline and new proposed estimates of the correlation output and the Delay Locked Loop (DLL) tracking error, which can be applied to precisely model GNSS receiver baseband signal processing.

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.

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.

Collective detection is a promising approach to positioning in a weak signal environment, in which the navigation solution is directly obtained by acquisition search in a multi-dimensional position and common clock bias uncertainty space. By combining the correlation values from multiple satellites and fully utilizing the coherence between them, the detectable C/N0 of individual satellites can be lowered. However, the lack of a computationally efficient optimization algorithm due to high dimensionality and complexity has hindered its application. A multi-resolution collective detection is therefore proposed to be a coarse-to-fine searching approach to solve for the position and common clock bias estimation. Although it reduces the computation time of collective detection, there is a gap in the efficiency study, which is the contribution of this research. The features of different levels of search in a multi-resolution algorithm are investigated. For a coarse search with large horizontal position step size, a smaller common clock bias step size is proposed instead of an averaging correlogram to reduce computation complexity as well as to obtain high time resolution. For the fine search with small horizontal space step size, a 3-D Dichotomous searching scheme is designed and applied to reduce the number of searching grids. Computer simulation results using experimental raw data are provided, to demonstrate the performance improvement against the conventional methods.

Vehicular communication technologies are becoming staples of modern societies. This paper proposes a new positioning algorithm for vehicular networks. The algorithm is a non-classic Multi-Dimensional Scaling Filter (MDSF) that builds on a novel and computationally effective Multi-Dimensional Scaling (MDS) solution covariance estimation technique and also a Maximum Likelihood (ML) filter. In general a major drawback of the non-classic MDS is the high computational cost because of its iterative nature. It is shown that a special blend between vehicular Map-Matching (MM) and MDSF considerably reduces the number of iterations and the convergence time, making the MDSF a suitable algorithm for vehicular network positioning. The performance of MDSF is compared with that of an Extended Kalman Filter (EKF) together with the Cramar Rao Lower Bound (CRLB). It is shown through simulation that for all types of traffic conditions MDSF performs better than EKF and closer to CRLB than EKF. It is also shown that both MDSF and EKF algorithms are robust to typical Global Positioning System (GPS) outages in deep urban canyons. CRLB also proves that Cooperative Positioning (CP) in general has the ability to bridge short GPS outages.

Cooperative positioning (CP) is a localization technique originally developed for use across wireless sensor networks. With the emergence of Dedicated Short Range Communications (DSRC) infrastructure for use in Intelligent Transportation Systems (ITS), CP techniques can now be adapted for use in location determination across vehicular networks. In vehicular networks, the technique of CP fuses GPS positions with additional sensed information such as inter-vehicle distances between the moving vehicles to determine their location within a neighbourhood. This paper presents the results obtained from a research study undertaken to demonstrate the capabilities of DSRC for meeting the positioning accuracies of road safety applications. The results show that a CP algorithm that fully integrates both measured/sensed data as well as navigation information such as map data can meet the positioning requirements of safety related applications of DSRC (<0·5 m). This paper presents the results of a Cramer Rao Lower Bound analysis which is used to benchmark the performance of the CP algorithm developed. The Kalman Filter (KF) models used in the CP algorithm are detailed and results obtained from integrating GPS positions, inter-vehicular ranges and information derived from in-vehicle maps are then discussed along with typical results as determined through a variety of network simulation studies.

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.