A twofold architecture based on GNSS multipath environment prediction and detection is presented in a context of loosely coupled and tightly coupled IMU/GNSS integration for navigation in urban areas. A signal quality monitoring group of techniques is applied for a platform self‐contained effort to detect and exclude multipath‐contaminated GNSS signals. Additionally, the sensor integration Kalman filter stochastic model is adjusted on‐the‐fly based on a GNSS multipath environment map. The map is populated by crowdsourcing and contains the spatial distribution of average carrier‐to‐noise‐density ratio measurements, linked to the probability of non‐line‐of‐sight, multipath‐contaminated, diffracted, and attenuated signal reception. To address the map availability issue, a random forest machine learning model is developed to propagate the map to the city areas not directly surveyed by the mapping fleet based on open‐access geographic data. The architecture performance is evaluated in the automotive scenario showing 13‐17% accuracy improvement compared to a conventional Kalman filter.

The CASSIOPE (also known as Swarm‐E) satellite crossed the path of totality of the August 2017 eclipse at ~640‐km altitude ~10 min following the lunar umbra. Observations from CASSIOPE's Global Positioning System radio occultation receiver reveal total electron content variations of 0.2–0.3 total electron content units in the topside ionosphere—a signature of medium‐scale (100‐200 km) plasma disturbances in the lunar penumbra that were induced by the eclipse. The variations were only observed during the eclipse, their absence on preceding days being consistent with their very low (<10%) statistical occurrence probability. Their spectral characteristics match those of other contemporaneous measurements, and their detection is consistent with the simulated ionosphere‐thermosphere response to the eclipse. To capture the small‐scale size of the variations or to simulate those expected in the upcoming (July 2019) total eclipse, ionosphere‐thermosphere model runs with a spatial resolution of 50 km or better would be required.

We present a multiinstrumented approach for the analysis of the Arctic ionosphere during the 19 February 2014 highly complex, multiphase geomagnetic storm, which had the largest impact on the disturbance storm‐time index that year. The geomagnetic storm was the result of two powerful Earth‐directed coronal mass ejections (CMEs). It produced a strong long lasting negative storm phase over Greenland with a dominant energy input in the polar cap. We employed global navigation satellite system (GNSS) networks, geomagnetic observatories, and a specific ionosonde station in Greenland. We complemented the approach with spaceborne measurements in order to map the state and variability of the Arctic ionosphere. In situ observations from the Canadian CASSIOPE (CAScade, Smallsat and IOnospheric Polar Explorer) satellite's ion mass spectrometer were used to derive ion flow data from the polar cap topside ionosphere during the event. Our research specifically found that (1) thermospheric O/N₂ measurements demonstrated significantly lower values over the Greenland sector than prior to the storm time. (2) An increased ion flow in the topside ionosphere was observed during the negative storm phase. (3) Negative storm phase was a direct consequence of energy input into the polar cap. (4) Polar patch formation was significantly decreased during the negative storm phase. This paper addresses the physical processes that can be responsible for this ionospheric storm development in the northern high latitudes. We conclude that ionospheric heating due to the CME's energy input caused changes in the polar atmosphere resulting in N_e upwelling, which was the major factor in high‐latitude ionosphere dynamics for this storm.