Ionospheric disturbances caused by acoustic waves emitted during earthquakes were studied using the Global Navigation Satellite System (GNSS) to analyze the changes in total electron content (TEC) values. GNSS signals normally propagate from satellites to receivers through the ionosphere layer. The delayed signals can be used to obtain TEC values by passing through the layer. Therefore, this study aims to analyze Coseismic Ionospheric Disturbances (CIDs) in six earthquakes, including 2016 M7.8 New Zealand (about 0.49 TECU), 2018 M7.9 Alaska (about 0.20 TECU), 2005 M7.2 California (about 0.29 TECU), 2023 M7.5 Turkey (about 0.49 TECU), 2012 M8.6 Sumatra (about 2.98 TECU), and 2012 M8.2 Sumatra (about 1.49 TECU) earthquakes. The propagation speed of the wave from the earthquake epicenter, identified as an acoustic wave, was estimated to be between 0.6 and 1.0 km/s. The 3D tomography modeling was performed to analyze the TEC height variations in the ionosphere to obtain a more accurate spatial analysis of TEC due to earthquakes. Moreover, checkerboard accuracy tests were applied to test the resolution of the 3D tomography model. The maximum ionization correlation test was also conducted for the six earthquakes to determine variations in the maximum ionization height of the ionosphere. The correlation test results between magnitude and maximum CID height produced a moderate correlation. The greater the earthquake magnitude, the higher the maximum CID detected. This is because greater earthquake produces compressed energy, which reduces the ionospheric density and reaches the maximum height. In addition, the maximum CID height is higher at night than in the afternoon because the E layer disappears at night.

For the last two decades, low-cost Global Navigation Satellite System (GNSS) receivers have been used in various applications. These receivers are mini-size, less expensive than geodetic-grade receivers, and in high demand. Irrespective of these outstanding features, low-cost GNSS receivers are potentially poorer hardwares with internal signal processing, resulting in lower quality. They typically come with low-cost GNSS antenna that has lower performance than their counterparts, particularly for multipath mitigation. Therefore, this research evaluated the low-cost GNSS device performance using a high-rate kinematic survey. For this purpose, these receivers were assembled with an Inertial Measurement Unit (IMU) sensor, which actively transmited data on acceleration and orientation rate during the observation. The position and navigation parameter data were obtained from the IMU readings, even without GNSS signals via the U-blox F9R GNSS/IMU device mounted on a vehicle. This research was conducted in an area with demanding conditions, such as an open sky area, an urban environment, and a shopping mall basement, to examine the device's performance. The data were processed by two approaches: the Single Point Positioning-IMU (SPP/IMU) and the Differential GNSS-IMU (DGNSS/IMU). The Unscented Kalman Filter (UKF) was selected as a filtering algorithm due to its excellent performance in handling nonlinear system models. The result showed that integrating GNSS/IMU in SPP processing mode could increase the accuracy in eastward and northward components up to 68.28% and 66.64%. Integration of DGNSS/IMU increased the accuracy in eastward and northward components to 93.02% and 93.03% compared to the positioning of standalone GNSS. In addition, the positioning accuracy can be improved by reducing the IMU noise using low-pass and high-pass filters. This application could still not gain the expected position accuracy under signal outage conditions.

Mount Semeru, an active volcano in East Java, Indonesia, erupted on December 4, 2021, following extreme rainfall that caused an avalanche of hot pyroclastic flows and lava. The tropospheric conditions and dominant particle components in the atmosphere can be monitored using Global Navigation Satellite System (GNSS) technology and remote sensing satellites. GNSS signal propagation delay in Precise Point Positioning (PPP) processing can be used to determine Zenith Tropospheric Delay (ZTD) and Precipitable Water Vapor (PWV) variables so that atmospheric conditions can be generated. In addition, by using remote sensing satellite data, it is possible to obtain rainfall data with high temporal resolution as well as the dominant particle and gas content values during eruptions. During the eruption period, the high value of PWV was dominated by the high intensity of precipitation during the rainy season. High rainfall before the eruption caused activity inside the mountain to increase, which occurred in avalanche type eruption. Apart from that, the atmosphere around Semeru was also dominated by SO₂ content, which spreaded for tens of kilometers. SO₂ content began to be detected significantly by remote sensing sensors on December 7, 2021. In this study, deformation and atmospheric monitoring were also carried out using low-cost GNSS at the Semeru Monitoring Station on September 9–15, 2022. The results of the ZTD and ZWD values show the dominance of the wet component, which is directly proportional to rainfall activity in this period.

The Unmanned Surface Vehicle (USV) navigation system needs an accurate, firm, and reliable performance to avoid obstacles, as well as carry out automatic movements during missions. The Global Positioning System (GPS) is often used in these systems to provide absolute position information. However, the GPS measurements are affected by external conditions such as atmospheric bias and multipath effects. This leads to the inability of the stand-alone GPS to provide accurate positioning for the USV systems. One of the solutions to correct the errors of this sensor is by conducting GPS and Inertial Measurement Unit (IMU) fusion. The IMU sensor is complementary to the GPS and not affected by external conditions. However, it accumulates noise as time elapses. Therefore, this study aims to determine the fusion of the GPS and IMU sensors for the i-Boat navigation system, which is a USV developed by Institut Teknologi Sepuluh Nopember (ITS) Surabaya. Using the Unscented Kalman filter (UKF), sensor fusion was carried out based on the state equation defined by the dynamic and kinematic mathematical model of ship motion in 6 degrees of freedom. Then the performance of this model was tested through several simulations using different combinations of attitude measurement data. Two scenarios were conducted in the simulations: attitude measurement inclusion and exclusion (Scenarios Ⅰ and Ⅱ, respectively). The results showed that the position estimation in Scenario Ⅱ was better than in Scenario Ⅰ, with the Root Mean Square Error (RMSE) value of 0.062 m. Further simulations showed that the presence of attitude measurement data caused a decrease in the fusion accuracy. The UKF simulation with eight measurement parameters (Scenarios A, B and C) and seven measurement parameters (Scenarios D, E and F), as well as analytical attitude movement, indicated that yaw data had the largest noise accumulation compared to roll and pitch.