Array antenna beam forming has high potential to improve the performance of the global navigation satellite system (GNSS) in urban areas. However, the widespread application of array antennas for GNSS multipath mitigation is restricted by many factors, such as the complexity of the system, the computation load and conflicts between required performance, cost budget and limited room for the antenna placement. The scope of this work is triplicate. (1) The pre-correlation beam forming structure is first suggested for multipath mitigation to decrease the system complexity. (2) With the pre-correlation structure, the equivalence of adaptive beam forming to quiescent beam forming is revealed. Therefore, the computational load for beam forming is greatly decreased. (3) A theoretical model is established to link the benefits of beam forming with GNSS performance improvement in terms of pseudorange quality. The model can be used by industry to balance the aforementioned restrictions. Numerical results with different array settings are given, and a 2 × 2 rectangle array with $0.4\lambda $ element spacing is suggested as a cost-effective choice in GNSS positioning applications in urban canyon areas.

The multipath effect and Non-Line-Of-Sight (NLOS) reception of Global Positioning System (GPS) signals both serve to degrade performance, particularly in urban areas. Although receiver design continues to evolve, residual multipath errors and NLOS signals remain a challenge in built-up areas. It is therefore desirable to identify direct, multipath-affected and NLOS GPS measurements in order improve ranging-based position solutions. The traditional signal strength-based methods to achieve this, however, use a single variable (for example, Signal to Noise Ratio (C/N0)) as the classifier. As this single variable does not completely represent the multipath and NLOS characteristics of the signals, the traditional methods are not robust in the classification of signals received. This paper uses a set of variables derived from the raw GPS measurements together with an algorithm based on an Adaptive Neuro Fuzzy Inference System (ANFIS) to classify direct, multipath-affected and NLOS measurements from GPS. Results from real data show that the proposed method could achieve rates of correct classification of 100%, 91% and 84%, respectively, for LOS, Multipath and NLOS based on a static test with special conditions. These results are superior to the other three state-of-the-art signal reception classification methods.

The possibility of identifying the type of multipath environment and receiver motion (e.g. pedestrian, vehicular) using pattern recognition approaches based on multipath parameters is investigated. This allows the receiver to adjust its tracking strategy and optimally tune its tracking parameters to mitigate code multipath effects. A Support Vector Machine (SVM) classification method with a modified Gaussian kernel is applied in this approach. A set of temporal and spectral features is extracted from the correlation samples of the received signals in different environments to train the classifier. The latter is then used in the structure of stochastic gradient-based adaptive multipath compensation and tracking techniques to tune the signal tracking parameters based on the environment and receiver motion. Simulation and real data measurements using Galileo E1B/C signals are performed to assess the validity of the proposed environment identification approaches and to evaluate the impact of the proposed context-based receiver parameter tuning techniques on tracking performance in multipath environments. Test results showed that the proposed classifiers have an accuracy between 86% and 92%, and the tracking performance improved by about 15%.

Urban positioning using the Global Positioning System (GPS) is challenging because of multipath. Urban canyons limit open sky visibility, and cause signal reflection and diffraction, resulting in significant satellite range measurement errors. The investigations reported here have been carried out in a French project called Inturb (an acronym derived from integrity and urban positioning). So far, the project has had two phases: first, a simple Three-Dimensional (3D) geometrical city modelling, called “Urban Trench”, has been developed and engineered manually from data sets collected in different cities. Positioning improvement in terms of accuracy was quantified where the model could be applied. Second, this modelling has been automated, based on the standard national BD Topo ® map database for France, with promising results. This geometrical modelling makes it possible to distinguish between line-of-sight satellite signals and those from non-line-of-sight. The latter, apparently bona fide, signals are caused by strong reflections, usually from buildings with a lot of steel and glass in their construction. A correction of the pseudo-range measurements of the latter is also computed and applied in the position estimator. Positioning accuracy is improved, whilst availability is kept at its maximum. In the study both manual and automatic 3D models are used in extensive experimental campaigns. Results are: first, the possibility to cover entirely any urban area in the country; second, the reduction of the median error in 3D by more than 50% on data collected in Nantes, Paris and Toulouse for a total duration of nearly ten hours; third, the compliance with standards used in most embedded maps and geographical information systems, including an assessment of the trade-off between the model simplicity and the positioning improvement.

A multipath mechanism similar to that used in Australia sixty years ago by the Sea-cliff Interferometer is shown to generate correlations between the periods of oscillations observed by two distant radio telescopes pointed to the Sun. The oscillations are the result of interferences between the direct wave detected in the main antenna lobe and its reflection on ground detected in a side lobe. A model is made of such oscillations in the case of two observatories located at equal longitudes and opposite tropical latitudes, respectively in Ha Noi (Viet Nam) and Learmonth (Australia), where similar radio telescopes are operated at 1.4 GHz. Simple specular reflection from ground is found to give a good description of the observed oscillations and to explain correlations that had been previously observed and for which no satisfactory interpretation, instrumental or other, had been found.

Multipath propagation can cause significant impairments to the performance of Global Navigation Satellite System (GNSS) receivers and is often the dominant source of accuracy degradation for high precision GNSS applications. Commonly used time-of-arrival estimation techniques cannot provide the required estimation accuracy in severely dense multipath environments such as urban canyons. Multipath components are highly correlated and this results in a rank deficiency of the signal autocorrelation matrix. In this paper the Doppler spectrum broadening of the fast fading channel resulting from the motion of the receiver or surrounding objects is employed to decorrelate signal reflections for the purpose of high-resolution estimation of multipath delays through the subspace-based Multiple Signal Classification (MUSIC) technique. Specifically, delay-domain correlator outputs at different Doppler frequencies are combined to enhance the rank of the signal autocorrelation matrix. Simulation and results of real data collected in an urban environment (downtown Calgary) are presented to compare the performance of the proposed method with the spatial-temporal-diversity-based MUSIC technique and a widely available algorithm in commercial GNSS receivers, namely the double-delta correlator technique. The performance metrics are based upon pseudorange and positioning errors, which are derived using an accurate reference trajectory established using a high precision GNSS-INS integrated system.

Multiple global navigation satellite system (GNSS) constellations can dramatically improve the signal availability in dense urban environments. However, accuracy remains a challenge because buildings block, reflect and diffract the signals. This paper investigates three different techniques for mitigating the impact of non-line-of-sight (NLOS) reception and multipath interference on position accuracy without using additional hardware, testing them using data collected at multiple sites in central London. Aiding the position solution using a terrain height database was found to have the biggest impact, improving the horizontal accuracy by 35% and the vertical accuracy by a factor of 4. An 8% improvement in horizontal accuracy was also obtained from weighting the GNSS measurements in the position solution according to the carrier-power-to-noise-density ratio (C/N0). Consistency checking using a conventional sequential elimination technique was found to degrade horizontal positioning performance by 60% because it often eliminated the wrong measurements in cases when multiple signals were affected by NLOS reception or strong multipath interference. A new consistency checking method that compares subsets of measurements performed better, but was still equally likely to improve or degrade the accuracy. This was partly because removing a poor measurement can result in adverse signal geometry, degrading the position accuracy. Based on this, several ways of improving the reliability of consistency checking are proposed.

In this paper we quantify the efficiency of parallelism in systems that are prone to failures and exhibit power law processing delays. We characterize the performance of two prototype schemes of parallelism, redundant and split, in terms of both the power law exponent and exact asymptotics of the delay distribution tail. We also develop the optimal splitting scheme which ensures that split always outperforms redundant.

Terrain Referenced Navigation (TRN) is based on the comparison of terrain altitude measurements with a reference map. Similar to code acquisition and tracking in Global Positioning System (GPS), the TRN system needs to find and track a particular sequence of measurements in a larger dataset. In our earlier work, these correspondences have been exploited to design a GPS inspired algorithm for TRN. The tracking loop is implemented as an early-late correlator, based on the DLL functional principle. Differences between GPS and terrain data in terms of signal properties are often based on the analysis of an ‘ideal’ GPS signal. In reality a GPS signal suffers from disturbances such as multipath and interference. This paper focuses on the identification and evaluation of potential GPS multipath mitigation strategies to be used to partly mitigate the issues caused by the non-ideal terrain signal. Results for the TRN implementations are described in a comparative manner. The content of this paper was presented during the European Navigation Conference 2011 in London.

Global Navigation Satellite Systems (GNSS) Doppler measurements are commonly used for velocity-based relative positioning and aiding Inertial Navigation Systems (INS) in signal degraded environments. The aim of this paper is to characterise the Doppler measurements in GNSS harsh multipath environments. In multipath fading situations such as indoor and urban canyon environments, multipath components arrive to the receiver antenna from different paths and directions. These give rise to various Doppler shifts that cause errors in the velocity solution. In this work the Doppler measurements discrepancy characterised by Doppler spread in multipath environments is investigated. By assuming a ‘sphere of scatterers’ model and considering the antenna gain pattern, the theoretical Power Spectral Density (PSD) observed by a receiver is formulated. The theoretical findings are examined using two sets of measurements in dense multipath environments. Global Positioning System (GPS) live signals using non-isotropic antennas with different orientations are used for this purpose. Different motion directions are also examined using different data sets. An Assisted GPS (A-GPS) approach is utilised where the code phase and the navigation data bits are provided by a nearby outdoor antenna. By applying a ‘Block Processing’ technique, an epoch-by-epoch Doppler and velocity estimation is implemented. Herein, the Doppler and velocity measurements accuracy in addition to the Doppler spread characterization are studied. As shown both theoretically and experimentally, in harsh multipath environments the PSD of the observed signals is a function of the scatterers' geometry and the antenna gain pattern. The Doppler estimation accuracies in multipath and multipath-free cases are compared for different ranges of Carrier-to-Noise ratio (C/N0). Theoretical and experimental results revealed inaccurate Doppler estimation and poor Doppler-derived velocity solutions in dense multipath environments.

Low-cost, single-frequency GPS systems provide economical positioning solutions to many geomatics applications, including GIS and low-accuracy surveying applications. Unfortunately however, the positioning accuracy obtained with those systems is not sufficient for many surveying applications. This is mainly due to the presence of ionospheric delay and multipath. In this research ionospheric delay is accounted for using regional high-resolution ionospheric maps produced by the US National Oceanic and Atmospheric Administration (NOAA). The major remaining constraint and challenging problem is multipath. This is because multipath is environmentally-dependent, difficult to model mathematically and cannot be reduced through differential positioning. This research proposes a new approach to identify multipath-contaminated L1 measurements through wavelet analysis. First, the difference between the code and carrier-phase measurements is estimated, leaving essentially twice the ionospheric delay, multipath and system noise. The ionospheric delay is largely removed by using high-resolution ionospheric delay maps produced by NOAA. The remaining residuals contain mainly low-frequency multipath, if it exists, and high-frequency system noise, which are decomposed using Daubechies family wavelets (db8). A satellite signal is identified as contaminated by multipath based on the standard deviation of the low-frequency part of the residual component. The L1 measurements obtained from the satellites with the lowest multipath are used to compute the final positions using two software packages, namely Trimble Total Control (TTC) and Bernese scientific processing software. The Magellan AC12 low-cost single-frequency GPS receiver was extensively tested in static mode. It is shown that accuracies within 5 cm are routinely obtained for baselines up to 65 km under various multipath environments.

In recent months the US and the EU have agreed to change the modulation scheme for the new civil GPS and Galileo signals in the L1 band from Binary Offset Carrier (1,1) (BOC(1,1)) to a scheme having the so-called Multiplexed Binary Offset Carrier (MBOC) spectrum, although each system will implement this differently. This paper compares the performance of the new system in a number of areas against the original baseline. It is shown that the MBOC signal offers significant advantages in multipath and tracking performance, while retaining compatibility with BOC(1,1) receivers at the cost of a small correlation loss.

In the last decades, robust estimation has been widely applied to overcome the presence of gross errors in observations. Recently it has been shown that robust estimation, if computed appropriately, is able to cope with a larger amount of gross errors and also capable of providing reliable estimations in situations where systematic errors are present. It is the purpose of this paper to propose the use of global robust estimation for computing baselines strongly affected by multipath effects.

The modelling of most Global Navigation Satellite System (GNSS) errors/biases and developments of data processing techniques have been improved substantially since the birth of Global Positioning System (GPS), however, there has been much less progress in the improvement of phase multipath mitigation techniques. Multipath therefore remains one of the most important error sources in high precision GNSS positioning. This is because multipath is site-dependent and therefore cannot be eliminated by differencing techniques. Also multipath is highly dependent on satellite-reflector-antenna geometry, which usually causes rapid changes in phase multipath errors especially in Real-Time Kinematic (RTK) applications. Multipath mitigation for static antennas such as those at reference stations can be carried out by site calibration, averaging over long observation times, and through the estimation of the error using filtering based on signal-to-noise ratio (SNR) data. However, multipath mitigation for kinematic antennas is still very difficult today.

Nevertheless, much research has been carried out on a particular class of phase multipath mitigation techniques: ones that can be applied within positioning algorithms (rather than incorporated into the receiver tracking loops or antennas). This paper investigates and further develops a number of state-of-the-art techniques in this category. They include phase multipath estimation using SNR data, phase multipath estimation through the use of closely spaced antennas, multipath mitigation stochastic models such as the satellite elevation angle model and SNR-based models (SIGMA-∊ model and our modified SNR-based model), and our own novel ray-tracing method. The techniques are tested with both real and simulated data, the real test datasets have been collected on the Laboratoire Central des Ponts et Chaussées (LCPC) testbed near Nantes in France, and on the campus of the University of Nottingham during SPACE data collection experiments.

Multipath is still one of the major error sources that degrades the accuracy of GPS positioning. The amount of multipath is highly dependent on the antenna's environment, which makes it difficult to isolate. Usually there is at least one in-view satellite which is more susceptible to multipath, particularly the one with the lowest elevation angle. To increase the positioning the best satellites must be selected (i.e. by least square or multipath mitigation) for computing a position. In this paper we propose an algorithm which picks up the best satellites (when there are more than four satellites in view) based on wavelet analysis for calculating a position. In this experiment, code and carrier measurements were collected in 15-minute segments by exploiting a single frequency (L1), stationary, navigation-grade receiver in a high-multipath environment. The magnitudes of these pseudoranges were often inflated by multipath error. We then post-processed the received data by applying wavelet filtering to the residuals (code minus carrier) to approximate the multipath values, and compute the receiver's position based on the selected satellites. Satellites were selected based on the residual values. To compare the results with the raw measurements, statistical elements were computed. The results showed significant improvement in variance of the estimated positions and, most importantly, a normalization of the data scatter-distribution was observed.

Positioning in the urban environment using GNSS is hampered by poor satellite availability due to signal obstruction created by both man-made and natural features of the urban environment. In addition, range measurement to satellites for positioning and for navigation is severely degraded by the multipath effect. The arrival and continuous enhancement of computerised geometric city models makes it possible to tackle these problems through modelling. In this paper description is given of a method for determining the local multipath environment, defined by the surfaces within a city model that will cause disruptive signal reflections to be presented to a receiver. An example simulation is performed, and graphical and numerical results are produced.