A recent introduction of the GNSS Measurement Application Programming Interface (API) by Google, Inc. has opened the room for advanced utilisation of Global Navigation Satellite System (GNSS) receivers embedded in devices running the Android operating system. The existing information and (mobile) communication concepts, such as: Software-Defined Radio and Internet of Things, may experience a huge potential for utilisation of the direct access to raw pseudorange observations collected by GNSS receivers in mobile devices. Here we present the process for exploitation of the direct access to raw GNSS pseudorange observations for determination of the GNSS-based applications Quality of Service (QoS) and the GNSS deterioration risk assessment. The process is based on the previously established post-processing and statistical analysis procedure applied on the raw GNSS pseudoranges taken by GNSS-equipped smartphone devices. Two manuscript co-authors are actively involved in the preparation of the European GNSS Agency (GSA) white paper on the GNSS community position on raw GNSS measurement utilisation in mobile consumer devices.

Ionospheric delay is the major source of satellite positioning system performance degradation. Designers of satellite positioning systems attempt to mitigate the impact of the ionospheric delay by deployment of correction models. For instance, the American GPS utilises a global standard (Klobuchar) model, based on the assumption that the daily distribution of GPS ionospheric delay values follows a biased cosine curve during day-time, while during the night-time the GPS ionospheric delay remains constant. Providing a compromise between computational complexity and accuracy, the Klobuchar model is capable of correcting up to 70% of actual ionospheric delay, mainly during quiet space weather conditions. Unfortunately, it provides a very poor performance during severe space weather, geomagnetic and ionospheric disturbances. In addition, a global approach in Klobuchar model development did not take into account particularities of the local ionospheric conditions that can significantly contribute to the general GPS ionospheric delay. Current research activities worldwide are concentrating on a better understanding of the observed GPS ionospheric delay dynamics and the relation to local ionosphere conditions.Here we present the results of a study addressing daily GPS ionospheric delay dynamics observed at a Croatian coastal area of the northern Adriatic (position ϕ=45°N, λ=15°E) in the periods of quiet space weather in 2007. Daily sets of actual GPS ionospheric delay values were assumed to be the time series of composite signals, consisting of DC, cosine and residual components, respectively. Separate models have been developed that describe components of actual GPS ionospheric delay in the northern Adriatic for summer and winter, respectively. A special emphasis was given to the statistical description of the residual component of the daily distribution of GPS ionospheric delay, obtained by removing DC (bias) and cosine components from the composite GPS ionospheric delay.Future work will be focused on further evaluation and validation of a quiet space weather GPS ionospheric delay model for the northern Adriatic, transition to a non-Klobuchar model, and on research in local GPS ionospheric delay dynamics during disturbed and severe space weather conditions.

Severe space weather conditions affect the performance of numerous modern technical systems, causing problems not only for national and global economies, but for everyday life as well. Satellite navigation systems are particularly vulnerable, despite the fact that systematic monitoring of space weather in general is still performed on a global scale. Space weather effect correction models applied within the standard satellite positioning service are not capable of tackling the effects of severe space weather conditions and local ionospheric characteristics. Severe space weather effects on the GPS ionospheric delay are intensely studied in order to provide advanced models of the space weather effects on GPS positioning performance.Here one study of severe space weather conditions and its consequences on the GPS ionospheric delay in Croatia is presented. The study takes advantage of the availability of the space weather indices and the GPS pseudorange measurements (taken at the reference site at Osijek, Croatia) related to a major severe space weather event lasting from early October 2003 to late November 2003. This paper presents the reconstruction of the severe space weather conditions and the development of ionospheric disturbances. Based on these reconstructions, the dynamics of the GPS ionospheric delay has been derived. The comparison of actual (measured) and modelled (according to standard GPS model) GPS ionospheric delay has been performed, with the aims of identifying actual behaviour of GPS ionospheric delay and examining the ability of standard (Klobuchar) GPS model to describe the GPS ionospheric delay in severe space weather conditions. Two interesting experimental models derived from the data analysis are presented, addressing the direct relations between the GPS ionospheric delay and the parameters of space weather activity (sunspot number and solar flux), as observed at the reference station Osijek, Croatia.The paper concludes with the plans for further research activities related to the regional GPS ionospheric delay model development for south-eastern Europe.

Established on the exact mathematical principles, understanding of radio-wave propagation, and statistical signal processing and information theory, satellite navigation has become an essential cornerstone of modern civilisation, and an indispensable component of the national infrastructure. The increasing number of both navigation and non-navigation applications of the Global Navigations Satellite System (GNSS) utilise its Positioning, Navigation, and Timing (PNT) service for technology and business development, daily operation of technology and socio-economic systems, and improvement of the quality of life. The inherent shortcomings and limitations of GNSS ask for a transition towards the GNSS resilient to natural and artificial detrimental effects, which degrade the GNSS positioning performance. Here a systematic overview of the causes of the GNSS positioning performance degradation is outlined. Recent developments in mathematics and computer science are discussed as fundamental in the GNSS resilience development. Formulation of the Satellite-Positioning-as-a-Service (SPaaS) is outlined, as the new fundamental paradigm for resilient GNSS. Finally, the effects of the SpaaS on the wide range of GNSS applications in science, economy, and society are discussed. The contributions to SPaaS concept and the related developments through the application of statistical learning, mathematical methods and models development, and applications discussed result from the author’s involvement in numerous international strategic, technology, regulatory, standardisation, business, and academic education development and collaboration activities.

A traditional receiver-centered GNSS positioning model addresses the GNSS resilience development with utilization of standardized global error correction models. Consideration of a GNSS receiver as a black-box unit that delivers position, velocity, and timing (PNT) services, renders the GNSS position estimation process inflexible for GNSS application development. Here we propose a novel concept and algorithm for a GNSS position estimation that relies upon the awareness of the immediate positioning environment conditions. A mathematical and algorithmic frameworks of the proposed approach in GNSS position estimation are outlined in which a mobile unit serves the radio frequency (satellite signal reception, condition and digitization) and base-band (pseudorange measurements, and navigation message parsing) domains. The GNSS position estimation and positioning environment effects mitigation become the responsibility of the navigation domain integrated with the targeted GNSS application. The accurate description of the immediate real-time positioning environment (geomagnetic, ionospheric, tropospheric, multi-path, but also jamming and spoofing) conditions is either obtained in real-time from mobile unit sensors, or provided by trusted third parties. The GNSS application adapts accordingly the GNSS position estimation algorithm, and deploys the pseudorange error correction models for the real immediate positioning environment conditions scenario. The application-centered GNSS position estimation algorithm becomes focused on the provision of the Positioning, Navigation, and Timing (PNT) Quality of Service (QoS) scaled to the application needs, thus providing the more efficient mitigation of the positioning environment adverse effects while at the same time optimising computing and energy resources. An initial proof-of-principle performance assessment with a bespoke statistical learning-based environment condition model in the case of a rapidly developing short-term geomagnetic storm shows up to 92% mean positioning error reduction, and more than 50% reduction in the positioning error standard deviation.