Thèse (M.Sc.)--Université Laval, 2003. / Bibliogr.: f. 101-103. Publié aussi en version électronique.

Th. Ph. D.--Budapest--Technical University, 1997. / Bibliogr. p. 89-102.

Th. doct.--Technischen Hochschule Darmstadt, 1997.

A new, open-access Global Positioning System (GPS) signal, known as L1C, is the most recent of several modernized Global Positioning System (GPS) signals. The first launch of a GPS satellite with this signal is expected to occur within a few years. One of the interesting features of modern Global Navigation Satellite System (GNSS) signals, including GPS L1C, is the presence of data and pilot components. The pilot component is a carrier with a deterministic overlay code but no data symbols; whereas, the data component carries the navigation data symbols used in the receiver processing. A unique aspect of GPS L1C is the asymmetrical power split between the two components, 75% of the power is used for the pilot and the remaining power, or 25%, for the data. In addition, the pilot and the data components are transmitted in phase with orthogonal spreading codes. Unassisted acquisition of GNSS spread spectrum signals requires a two-dimensional search for the spreading code delay and Doppler frequency. For modern two-component GNSS signals, conventional GNSS acquisition schemes may be used on either component, correlating the received signal with either the pilot or the data spreading code. One obvious disadvantage of this approach is the wasting of power; hence, new techniques for combining, or joint acquisition of the pilot and the data components, have been proposed. In this dissertation, acquisition of GPS L1C is analyzed and receiver techniques are proposed for improving acquisition sensitivity. Optimal detectors for GPS L1C acquisition in additive white Gaussian noise are derived, based on various scenarios for a GPS receiver. Monte Carlo simulations are used to determine the performance of these optimal detectors, based on detection and false alarm probabilities. After investigating the optimal detectors for GPS L1C acquisition, various sub-optimal detectors that are more efficient to implement are thoroughly investigated and compared. Finally, schemes for joint acquisition of L1C and the legacy GPS C/A code signal are proposed and analyzed.

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The atmosphere in low latitude regions is of particular interest to GPS researchers because the propagation of GPS signals becomes significantly delayed compared with other regions of the world. Hence this limits GPS positioning accuracy in equatorial regions. Although the atmospheric delay can be modelled, a residual component will still remain. Reducing, or mitigating the effect of residual atmospheric delay is of great interest, and remains a challenge, especially in equatorial regions. Analysis of relative positioning accuracy of GPS baselines has confirmed that the residual atmospheric delay is distance-dependent, even in low latitude areas. Residual ionospheric delay is the largest component in terms of both absolute magnitude and variability. However it can be largely eliminated by forming the ionosphere-free combination of measurements made on two frequencies. The residual tropospheric delay is smaller in magnitude but rather problematic due to strong spatio-temporal variations of its wet component. Introducing additional troposphere ???scale factors??? in the least squares estimation of relative position can reduce the effect of the residual. In a local GPS network, the distance-dependent errors can be spatially modelled by network-based positioning. The network-based technique generates a network ???correction??? for user positioning. The strategy is to partition this network correction into dispersive and non-dispersive components. The latter can be smoothed in order to enhance the ionosphere-free combination, and can be of benefit to ambiguity resolution. After this step, both the dispersive and non-dispersive correction components can be used in the final positioning step. Additional investigations are conducted for stochastic modelling of network-based positioning. Based on the least squares residuals, the variance-covariance estimation technique can be adapted to static network-based positioning. Moreover, a two-step procedure can be employed to deal with the temporal correlation in the measurements. Test results on GPS networks in low latitude and mid-latitude areas have demonstrated that the proposed network-based positioning strategy works reasonably well in resolving the ambiguities, assisting the ambiguity validation process and in computing the user???s position. Furthermore, test results of stochastic modelling in various GPS networks suggests that there are improvements in validating the ambiguity resolution results and handling the temporal correlation, although the positioning result do not differ compared to using the simple stochastic model typically used in standard baseline processing.

GPS processing, like every processing method for geodetic applications, relies upon least-squares estimation. Quality measures must be defined to assure that the estimates are close to reality. These quality measures are reliable provided that, first, the covariance matrix of the observations (the stochastic model) is well defined and second, the systematic effects are completely removed (i.e., the functional model is good). In the GPS precise point positioning (PPP) the stochastic and functional models are not as complicated as in the differential GPS processing. We will assess the quality of the GPS Precise Point Positioning in this thesis by trying to define more realistic standard deviations for the station position estimates. To refine the functional model from systematic errors, we have 1) used the phase observations to prevent introducing any hardware bias to the observation equations, 2) corrected observations for all systematic effects with amplitudes of more than 1cm, 3) used undifferenced observations to prevent having complications (e.g. linearly related parameters) in the system of observation equations. To have a realistic covariance matrix for the observations we have incorporated the ephemeris uncertainties into the system of observation equations. Based on the above-mentioned issues a PPP processing method is designed and numerically tested on the real data of some of the International GNSS Service stations. The results confirm that undifferenced stochastic-related properties (e.g. degrees of freedom) can be reliable means to recognize the parameterization problem in differenced observation equations. These results also imply that incorporation of the satellite ephemeris uncertainties might improve the estimates of the station positions. The effect of troposphere on the GPS data is also focused in this thesis. Of particular importance is the parameterization problem of the wet troposphere in the observation equations. / <p>QC 20130218</p>