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The 3GPP (Third Generation Partnership Project) Release 7 of GSM and UMTS cellular standards as well as SUPL2.0, used in IP networks, include major modifications as to how AGNSS (Assisted GNSS) assistance data is transferred from the network (cellular or IP) to the cellular terminal. Simultaneously position accuracy improvements may be introduced. One potential option is to use carrier phase -based positioning methods. This can be achieved integrally in the cellular network or by the use of Virtual Reference Stations and an IP network. The bulk of AGNSS devices will be single-frequency due to additional cost associated with two RF front-ends. Hence, this study addresses the feasibility of single-frequency carrier phase -based positioning making also comparisons with the dual-frequency case. The study shows that single-frequency carrier phase -based positioning is feasible with short baselines (<5 km) given that 1)real-time ionospheric predictions are vailable and 2)there are enough satellites available. Namely, this requires hybrid-use of GPS and Galileo.

Precise Point Positioning (PPP) is currently based on the processing of only GPS observations. Its positioning accuracy, availability and reliability are very dependent on the number of visible satellites, which is often insufficient in the environments such as urban canyons, mountain and open-pit mines areas. Even in the open area where sufficient GPS satellites are available, the accuracy and reliability could still be affected by poor satellite geometry. One possible way to increase the satellite signal availability and positioning reliability is to integrate GPS and GLONASS observations. Since the International GLONASS Experiment (IGEX-98) and the follow-on GLONASS Service Pilot Project (IGLOS), the GLONASS precise orbit and clock data have become available. A combined GPS and GLONASS PPP could therefore be implemented using GPS and GLONASS precise orbits and clock data. In this research, the positioning model of PPP using both GPS and GLONASS observations is described. The performance of the combined GPS and GLONASS PPP is assessed using the IGS tracking network observation data and the currently available precise GLONASS orbit and clock data. The positioning accuracy and convergence time are compared between GPS-only and combined GPS/GLONASS processing. The results have indicated an improvement on the position convergence time but correlates to the satellite geometry improvement. The results also indicate an improvement on the positioning accuracy by integrating GLONASS observations.

In the traditional approach to differential GNSS, the satellite error terms are eliminated by forming the so-called single differences (SD). One then gets rid of the receiver error terms by computing, for each receiver to be considered, the corresponding double differences (DD): the discrepancies between the single differences (SD) and one of them taken as reference. To handle the SD's in a homogeneous manner, one may equally well consider the discrepancies between the SD's and their mean value. In this paper, these ‘centralized differential data’ are referred to as ‘reduced differences’ (RD). In the case where the GNSS devices include only two receivers, this approach is completely equivalent to ‘double centralization’. More precisely, the information contained in the ‘double centralized observations’ is then a simple antisymmetric transcription of that contained in the reduced differences. The ambiguities are then rational numbers which are related to the traditional integer ambiguities in a very simple manner. The properties established in this paper shed a new light on the corresponding analysis. (The extension to GNSS networks with missing data will be presented in a forthcoming paper.) The corresponding applications concern the identification of outliers in real time. Cycle slips combined with miscellaneous SD biases can thus be easily identified.

Sensor networks that use wireless technology (IEEE standards) to measure distances between network nodes allow 3D positioning and real-time tracking of devices in environments where Global Navigation Satellite Systems (GNSS) have no coverage. Such a system re¬quires three key capabilities: extraction of ranges between sensor nodes, appropriate supporting network communications and positioning. Recent research has shown that the first two of these capabilities are feasible. This paper builds on this and develops an automatic and robust 3D positioning capability. A strategy is presented that enables high integrity positioning even in the presence of large mean errors in the range measurements. This is achieved by an algorithm that generates a tight, high-confidence upper bound on the error in a position estimate, given the noisy range measurements from the radio devices in view. As a core feature, we present a novel network auto-localisation algorithm that fully automatically determines the positions of all nearby fixed nodes. Results from a real network using the Cricket Indoor Location System show how all sensor nodes can be determined based on only one dynamic node. Simulations of static networks with 100 nodes demonstrate the importance of solving folding ambiguities. Studies from networks with imprecise range measurements have shown that it is possible to theoretically achieve a position deviation that is of the size of the ranging error.

Global Navigation Satellite Systems like the US Global Positioning System GPS and the Russian GLONASS system are currently going through a number of modernization steps. The first satellites of the type GPS-IIR-M with L2C support were launched and from now on all new GPS satellites will transmit this new civil L2 signal. The first launch of a GPS-IIF satellite with L5 support is announced for spring 2008. Russia has started to launch GLONASS-M satellites with an extended life-time and a civil L2 signal and has announced to build up a full 18 satellite system by 2007 and a 24 satellite system by 2009. Independently of that the European Union together with the European Space Agency and other partnering countries are going to launch the new European satellite system Galileo, which will also provide worldwide satellite navigation service at some time after 2011. As a consequence we can expect to have very heterogeneous receiver hardware in these reference station networks for a transition period which could last until 2015. Network server software computing network corrections will have to deal with an increased number of signals, satellites and heterogeneity of the available data. The complexity but also the CPU load for this server software will increase dramatically. With the increasing number of signals and satellites the demands for the network server software is growing rapidly. The progress on the satellite system side is going hand in hand with the tendency of the customers to operate growing numbers of reference station receivers resulting in higher demands for CPU power. The paper presents a new approach, which allows us to process data from a large number of reference stations and multiple signals via a new federated Kalman filter approach. With the newest improvements in the GLONASS satellite system, more and more Network RTK service providers have started to use GLONASS capable receivers in their networks. Today, practically all service providers, who are using GLONASS, are applying the Virtual Reference Station (VRS) technique to deliver optimized correction streams to the users in the field. Different satellite systems and generations require different weighting in network server processing and receiver positioning. The network correction quality depends very much on the satellite and signal type. New message types have been recently developed providing individualized statistical information for each rover on unmodeled residual geometric and ionospheric errors for GPS and GLONASS satellites. The use of this information leads to RTK performance improvements, which is demonstrated in practical examples.

Location determination of pedestrians in urban and indoor environment can be very challenging if GNSS signals are blocked and only pseudorange measurements to less than four statellites are avialable. Therefore a combination with other wireless technologies for absolute position determination and dead reckoning (DR) for relative positioning has to be performed. Radio Frequency Identification (RFID) is an emerging technology that can be employed for location determination of a mobile user in indoor and urban environment. RFID transponders (or tags) can be placed at known location (so-called active landmarks) in the environment and the user who has to be positioned can carry a RFID transceiver (or reader). Then the location of the user can be obtained using cell-based positioning or with trilateration if ranges to several tags are deduced. In this paper the use of active RFID in combination with satellite positioning and DR is investigated. For that purpose the integration with GNSS and other wireless technologies is discussed and the deduction of ranges to RFID tags is investigated. Test results show that the ranges to RFID tags can be deduced from signal strength observations to tags in the surrounding environment. Two different models that describe either a logarithmic or linear relationship between the measured signal strength and the distance to the tag are analyzed. In addition, if pseudorange observations to GNSS satellites can be measured then they can also be used with ranges to RFID tags to obtain the position fx. The absolute position can then be used to update the drift rates of the DR sensors which are used for continuous position determination. Different scenarios for the correction of the DR drift are described in the paper. The presented research is conducted in a new research project at the Vienna University of Technology.

In INS (Inertial Navigation System) /GPS (Global Positioning System) integration there are nonlinear models should be properly handled. The most popular and commonly used method is the Extended Kalman Filter (EKF) which approximates the nonlinear state and measurement equations using the first order Taylor series expansion. On the other hand, recently, some nonlinear filtering methods such as Gaussian Sum filter, particle filter and unscented Kalman filter have been applied to the integrated systems. In this paper, we propose a modified Gaussian Sum filtering method and apply it to land-vehicle INS/GPS integrated navigation as well as the in motion alignment systems. The modification of Gaussian Sum filter is based on a combination of Gaussian Sum filter and so-called unscented transformation which is utilized in the unscented Kalman filter in order to improve the treatment of the nonlinearity in Gaussian Sum filter. In this paper, the performance of modified Gaussian Sum filter based integrated systems is compared with other filters in numerical simulations. From simulation results, it was found that the proposed filter can improve transient responses of the filter under large initial estimation errors.

Earth atmospheric information has been primarily observed by a global network of radiosonde weather observation stations for global weather forecasting and climatologic studies for many years. However, the main disadvantage of this method is that it can not sufficiently capture the complex dynamics of the Earth’s atmosphere since its limited and heterogeneous geographic distribution of launching stations. Since the first low earth orbit (LEO) satellite equipped with a GPS receiver was launched in early 1990s, there are more than a dozen of GPS receivers onboard LEO satellites used for Earth atmospheric observation. Recent research has shown that the Global Navigation Satellite System (GNSS) radio occultation (RO) derived atmospheric profiles have great potentials to overcome many limitations of existing atmospheric observation methods. Constellation Observing Systems for Meteorology, Ionosphere, and Climate (COSMIC) retrieved atmospheric profiles are investigated using radiosonde measurements at 42 collocated stations in the Australian region. Statistical results show that the difference in average temperature is about 0.05°C with a standard deviation of 1.52°C and the difference in average pressure is -1.06 hPa with a standard deviation of 0.91 hPa. This research has also demonstrated that the GNSS RO derived atmospheric profiles have good agreement with the radiosonde observations.

The integration of Global Positioning System (GPS)/Inertial Navigation System (INS) has become very important in various navigation applications. In the last decade, with the rapid development of Micro Electro Mechanical Sensors (MEMS), great interest has been generated in low cost integrated GPS/INS applications. This paper presents a PC104 based low cost GPS/INS integrated navigation platform. The platform hardware consists of low cost inertial sensors and an assembly of various PC104 compatible peripherals, such as data acquisition card, GPS receiver, Ethernet card, mother board, graphic card, etc. The platform software including inertial/GPS data acquisition, inertial navigation calculation and integrated GPS/INS Kalman filter is implemented with Simulink, which can be directly loaded and processed in the PC104 mother board with the aid of Matlab Real-Time Workshop (RTW) utility. This platform is totally self-embedded and can be applied independently or as part of a system. Simulation and real data experiments have been performed to validate and evaluate the proposed design. A very low cost MEMS inertial sensor was utilized in the experiments. The reference is the navigation solution derived from a tactic grade Inertial Measurement Unit (IMU). Test results show that PC104 navigation platform delivers the integrated navigation solutions comparable to the reference solutions, which were calculated with a conventional laptop computer, however with less power consumptions, less system volume/complexity and much lower over-all costs. Moreover the platform hardware is compatible to various inertial sensors of different grades by configuring the related parameters in the system software.

This paper describes an innovative solution that can be used to recover the navigation data from Alternative Binary Offset Carrier (AltBOC) modulated signals, a modulation scheme foreseen for the Galileo satellite navigation system to transmit four channels in the E5 band (1164-1215 MHz). In this paper a novel data demodulation approach, called Side-Band Translator (SBT), suitable to coherent dual band AltBOC receiver architectures, is introduced and validated from the analytical point of view. This patented approach is based on the idea to perform a ‘translation operation’: this means that the two separate in-phase components of the AltBOC signal, containing the navigation data, are recovered from the received signal with a proper signal processing, moving the information from the side lobes of the AltBOC spectrum to the baseband. The innovative aspects of this demodulation technique are pointed out in the paper, highlighting the main advantages with respect to already proposed techniques.