Classically a stand-alone GNSS receiver estimates its velocity by forming the approximate derivative of consecutive user positions or more often by using the Doppler observable. The first method is very inaccurate, while the second one allows estimation of the order of some cm/s. The TDCP (Time-Differenced Carrier Phase) technique, which consists in differencing successive carrier phases, enables accuracies at the mm/s level. A study on the existing TDCP velocity estimation algorithms has revealed that the use of different broadcast ephemeris sets to calculate the satellite positions and clock offsets produces a discontinuity in the TDCP measurements that affects the velocity estimation. We propose a method to overcome this limitation based on the use of the same set of ephemeris to calculate the satellite positions and clock offsets at consecutive epochs. We describe in detail the TDCP algorithm used and the complete implementation in MATLAB is included

In signal-degraded environments such as urban canyons and mountainous area, many GNSS signals are either blocked or strongly degraded by natural and artificial obstacles. In such scenarios standalone GPS is often unable to guarantee a continuous and accurate positioning due to lack (or the poor quality) of signals. The combination of different GNSSs could be a suitable approach to fill this gap, because the multi-constellation system guarantees an improved satellite availability compared to standalone GPS, thus providing enhanced accuracy, continuity and integrity of the positioning. The present GNSSs are GPS, GLONASS, Galileo and Beidou, but the latter two are still in the development phase. In this work GPS/GLONASS systems are combined for single point positioning and their performance are assessed for different configurations. Using GPS/GLONASS multi-constellation implies the addition of an additional unknown, i.e. the intersystem time scale offset, which requires a sacrifice of one measurement. Since the intersystem offset is quasi-constant over a short period, a pseudo-measurement can be introduced to compensate the sacrifice.The benefit after adding a pseudo-measurement has been demonstrated in a vehicular test.

Global Navigation Satellites Systems (GNSS) is frequently used for positioning services in various applications, e.g., pedestrian and vehicular navigation. However, it is well-known that GNSS positioning performs unreliably in urban environments. GNSS shadow matching is a method of improving accuracy in the cross-street direction. Initial position and classification of observed satellite visibility between line-of-sight (LOS) and non-line-of-sight (NLOS) are essential for its performance. For the conventional LOS/NLOS classification, the classifiers are based on a single feature, extracted from raw GNSS measurements, such as signal noise ratio, pseudorange, elevation angle, etc. Especially in urban canyons, these measurements are unstable and unreliable due to the signal reflection and refraction from the surrounding buildings. Besides, the conventional least square approach for positioning is insufficient to provide accurate initialization for shadow matching in urban areas. In our study, shadow matching is improved using the initial position from robust estimator and the satellite visibility determined by support vector machine (SVM). The robust estimator has an improved positioning accuracy and the classification rate of SVM classification can reach 91.5% in urban scenarios. An important issue is related to satellites with ultra-high or low elevation angles and satellites near the building boundary that are very likely to be misclassified. By solving this problem, the SVM classification shows the potential of about 90% classification accuracy for various urban cases. With the help of these approaches, the shadow matching has a mean error of 10.27 m with 1.44 m in the cross-street direction; these performances are suitable for urban positioning.

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The integration of Global Navigation Satellite Systems (GNSS) with Inertial Navigation Systems (INS) has been very actively researched for many years due to the complementary nature of the two systems. In particular, during the last few years the integration with micro-electromechanical system (MEMS) inertial measurement units (IMUs) has been investigated. In fact, recent advances in MEMS technology have made possible the development of a new generation of low cost inertial sensors characterized by small size and light weight, which represents an attractive option for mass-market applications such as vehicular and pedestrian navigation. However, whereas there has been much interest in the integration of GPS with a MEMS-based INS, few research studies have been conducted on expanding this application to the revitalized GLONASS system. This paper looks at the benefits of adding GLONASS to existing GPS/INS(MEMS) systems using loose and tight integration strategies. The relative benefits of various constraints are also assessed. Results show that when satellite visibility is poor (approximately 50% solution availability) the benefits of GLONASS are only seen with tight integration algorithms. For more benign environments, a loosely coupled GPS/GLONASS/INS system offers performance comparable to that of a tightly coupled GPS/INS system, but with reduced complexity and development time.