The Chinese BeiDou Satellite Navigation System consists of three kinds of constellations: the geostationary Earth orbit (GEO), the inclined geosynchronous satellite orbit (IGSO), and the medium Earth orbit (MEO). The BeiDou has expanded its service coverage from regional to global. Recently, the BeiDou has been widely used in ionospheric total electron content (TEC) research. In this study, we analyzed the BeiDou signals for ionospheric TEC monitoring over Jeju Island in South Korea. The BeiDou GEO TEC showed a clear pattern of diurnal variations. In addition, we compared the TEC values from the BeiDou GEO, the BeiDou IGSO, GPS, and International GNSS Service (IGS) Global Ionosphere Maps (GIM). There was a difference of about 5 TEC units between the BeiDou GEO and the IGS GIM. This may be due to the altitude difference between the different navigation satellites.

As a countermeasure against the threat of jamming which can disrupt operation of the Global Positioning System (GPS) receivers, various kinds of technique to estimate the Direction-Of-Arrivals (DOAs) of incoming jamming signals have been widely studied, and among them, the MUltiple SIgnal Classification (MUSIC) algorithm is known to provide very high resolution. However, since the previous studies regarding the MUSIC algorithm does not consider the orientation of each antenna element of antenna arrays, there is a possibility that DOA estimation performance degrades in the case of a misaligned antenna array whose antenna elements are not oriented along the same direction. As an effort to solve this problem, there exists a previous work which presents an MUSIC-based method for DOA estimation. However, the error between the real and measured values of each antenna orientation is not taken into consideration. Therefore, in this paper, the effect of the aforementioned error on the DOA estimation performance in the case of a misaligned antenna array is analyzed by simulations.

In October 2019, the European Galileo navigation system operates a total of 24 satellites, two of them are in the testing phase. There are enough satellites in operation to enable precise point positioning (PPP) using Galileo signals. The number of visible satellites for Galileo in South Korea is investigated. In addition, to assess the latest performance of the Galileo kinematic PPP, data received at DAEJ reference station from October 1 to October 7, 2019, are analyzed. Galileo kinematic PPP presents some results in two categories, single-frequency PPP (SPPP) and dual-frequency PPP (DPPP). The positioning accuracy for Galileo kinematic SPPP solutions is less than 1 m root mean square (RMS) in all direction components. The Galileo kinematic DPPP achieves the positioning accuracy with an RMS value of less than 7 cm in all direction components. The results show that the latest performance of Galileo kinematic PPP at DAEJ station in South Korea is still relatively poor compared to GPS kinematic PPP. However, the residuals of Galileo code measurements are smaller than those of GPS code measurements.

It is essential to estimate the vehicle localization for an autonomous safety driving. In particular, since LIDAR provides precise scan data, many studies carried out to estimate the vehicle localization using LIDAR and pre-generated map. The road marking always exists on the road because of provides driving information. Therefore, it is often used for map information. In this paper, we propose to generate the Gaussian mixture map based on road-marking information and localization method using this map. Generally, the probability distributions map stores the single Gaussian distribution for each grid. However, single resolution probability distributions map cannot express complex shapes when grid resolution is large. In addition, when grid resolution is small, map size is bigger and process time is longer. Therefore, it is difficult to apply the road marking. On the other hand, Gaussian mixture distribution can effectively express the road marking by several probability distributions. In this paper, we generate Gaussian mixture map and perform vehicle localization using Gaussian mixture map. Localization performance is analyzed through the experimental result.

In Integration Approach (IA)-based Pedestrian Dead Reckoning (PDR), it is important to detect the exact zero-velocity of the foot with an Inertial Measurement Unit (IMU). By detecting zero-velocity during the stance phase of the foot touching the ground and executing Zero-velocity UPdaTe (ZUPT) at the exact time, stable navigation information can be provided by the PDR. When the pace is fast, however, it is not easy to accurately detect the zero-velocity because of the small stance phase interval and the large signal variance of the corresponding interval. Incorrect zero-velcity detection greatly causes navigation errors of IA-based PDR. In this paper, we propose a method to detect the zero-velocity stably even at high speed by novel buffering of IMU's output data and signal processing of the buffer. And we design a PDR based on this. By analyzing the performance of the proposed Zero-Velocity Detection (ZVD) algorithm and ZVD-based PDR through experiemnts, we confirm that the proposed method can provide accurate navigation information of pedestrians such as firefighters in the indoor space.