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.

Global navigation satellite system (GNSS) positioning in dense urban areas remains a challenge due to the signal reflection by buildings, namely multipath and non-line-of-sight (NLOS) reception. These effects degrade the performance of low-cost GNSS receivers such as in those smartphones. An effective three-dimensional (3D) mapping aided GNSS positioning method is proposed to correct the NLOS error. Instead of applying ray-tracing simulation, the signal reflection points are detected based on a skyplot with the surrounding building boundaries. The measurements of the direct and reflected signals can thus be simulated and further used to determine the user's position based on the measurement likelihood between real measurements. Verified with real experiments, the proposed algorithm is able to reduce the computational load greatly while maintaining a positioning accuracy within 10 metres of error in dense urban environments, compared with the conventional method of ray-tracing based NLOS corrected positioning.

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.

Multipath arises from the reception of reflected or diffracted signals in addition to the Line-Of-Sight (LOS) signal. By using a block processing high sensitivity receiver scheme, this paper aims to obtain better positioning performance in urban canyon areas. Generally, the peak with the most power is utilised in high sensitivity receivers; however, this approach is not always optimal in multipath environments. Noting that signal correlation peaks may be separated in the Doppler domain by a long coherent integration time, a peak identification scheme is proposed in this work, which yields better positioning performance. It is shown that most of the multipath peaks are removed in the receiver after using the proposed algorithm.

The Global Positioning System (GPS) is unreliable in dense urban areas, known as urban canyons, which have tall buildings or narrow streets. This is because the buildings block the signals from many of the satellites. Combining GPS with other Global Navigation Satellite Systems (GNSS) significantly increases the availability of direct line-of-sight signals. Modelling is used to demonstrate that, although this will enable accurate positioning along the direction of the street, the positioning accuracy in the cross-street direction will be poor because the unobstructed satellite signals travel along the street, rather than across it. A novel solution to this problem is to use 3D building models to improve cross-track positioning accuracy in urban canyons by predicting which satellites are visible from different locations and comparing this with the measured satellite visibility to determine position. Modelling is used to show that this shadow matching technique has the potential to achieve metre-order cross-street positioning in urban canyons. The issues to be addressed in developing a robust and practical shadow matching positioning system are then discussed and solutions proposed.