The robustness of the Sandia Inertial Terrain‐Aided Navigation (SITAN) algorithm has a pivotal influence on underwater vehicles' Gravity‐Aided Inertial Navigation Systems (GAINS). An abrupt glitch of the accuracy of the GAINS will evolve into a disaster when vehicles pass through areas of smooth gravity. In order to resolve vulnerability issues of initial error and linearization error, we propose the Correlation SITAN algorithm with Weight‐Reducing Iteration Technique (CSITAN + WRIT), in which the correlation process equals Terrain Contour Matching (TERCOM). The CSITAN algorithm is a Multipoint‐based Extended Kalman Filtering (MEKF) method, which can work in real time. First, we need to derive the state equation and observation equation of the Multipoint‐based SITAN algorithm based on the principle of the traditional SITAN algorithm. Then the accuracy of the state prediction can be improved, and the linearization error can be reduced by the correlation method based on TERCOM. Finally, the WRIT is utilized to reduce the possible influence of gross errors existing in the results of the MEKF and to extract a value with higher precision. The experimental results show that CSITAN + WRIT can achieve better accuracy and higher success rate of matching and can improve the possibilities of the occurrence of large matching errors than traditional SITAN methods in areas with smooth gravity. Copyright © 2017 Institute of Navigation

Indoor navigation has attracted commercial developers and researchers in the last few decades. The development of localization tools, methods and frameworks enables current communication services and applications to be optimized by incorporating location data. For clinical applications such as workflow analysis, Bluetooth Low Energy (BLE) beacons have been employed to map the positions of individuals in indoor environments. To map locations, certain existing methods use the received signal strength indicator (RSSI). Devices need to be configured to allow for dynamic interference patterns when using the RSSI sensors to monitor indoor positions. In this paper, our objective is to explore an alternative method for monitoring a moving user’s indoor position using BLE sensors in complex indoor building environments. We developed a Convolutional Neural Network (CNN) based positioning model based on the 2D image composed of the received number of signals indicator from both x and y-axes. In this way, like a pixel, we interact with each 10 × 10 matrix holding the spatial information of coordinates and suggest the possible shift of a sensor, adding a sensor and removing a sensor. To develop CNN we adopted a neuro-evolution approach to optimize and create several layers in the network dynamically, through enhanced Particle Swarm Optimization (PSO). For the optimization of CNN, the global best solution obtained by PSO is directly given to the weights of each layer of CNN. In addition, we employed dynamic inertia weights in the PSO, instead of a constant inertia weight, to maintain the CNN layers’ length corresponding to the RSSI signals from BLE sensors. Experiments were conducted in a building environment where thirteen beacon devices had been installed in different locations to record coordinates. For evaluation comparison, we further adopted machine learning and deep learning algorithms for predicting a user’s location in an indoor environment. The experimental results indicate that the proposed optimized CNN-based method shows high accuracy (97.92% with 2.8% error) for tracking a moving user’s locations in a complex building without complex calibration as compared to other recent methods.

The precise autonomous navigation for deep space exploration by combination of multi-source observation data is a key issue for probe control and scientific applications. In this paper, the performance of an integrated Optical Celestial Navigation (OCN) and X-ray Pulsars Autonomous Navigation (XNAV) system is investigated for the orbit of Mars Pathfinder. Firstly, OCN and XNAV single systems are realised by an Unscented Kalman Filter (UKF). Secondly, the integrated system is simulated with a Federated Kalman Filter (FKF), which can do the information fusion of the two subsystems of UKF and inherits the advantages of each subsystem. Thirdly, the performance of our system is evaluated by analysing the relationship between observation errors and navigation accuracy. The results of the simulation experiments show that the biases between the nominal and our calculated orbit are within 5 km in all three axes under complex error conditions. This accuracy is also better than current ground-based techniques.

Exploring land use evolution and its impact on carbon storage is crucial for mitigating climate change in resource-based cities and promoting green, low-carbon development. This study constructs a GMOP-PLUS-InVEST coupled model and utilizes remote sensing data from five phases of land use from 2000 to 2020. Four scenarios are established to simulate the future patterns of land use and carbon storage changes in Jining City. The results indicate that: (1) from 2000 to 2020, farmland, forest land, and grassland in Jining City show a declining trend; while construction and waters increase, resulting in a reduction of carbon storage from 167.35 × 10⁶ t in 2000 to 159.85 × 10⁶ t in 2020; (2) coal mining significantly influences nearby land utilization types and carbon storage, leading to a decline in nearby carbon reserves; (3) compared to the other three scenarios, the coordinated development scenario exhibits higher land use efficiency and carbon storage, with lower levels of human disturbance; balancing the local economy and environment, and serving as a sustainable pattern of land use for the area. The outcomes of this paper quantitatively reflect the relationship between land use, coal mining, and carbon storage in high-water-level resource-based cities; providing guidance for the local economy, urban development, and ecological environment protection.

The Earth’s rotation undergoes changes with the influence of geophysical factors, such as Earth’s surface fluid mass redistribution of the atmosphere, ocean and hydrology. However, variations of Earth Rotation Parameters (ERP) are still not well understood, particularly the short-period variations (e.g., diurnal and semi-diurnal variations) and their causes. In this paper, the hourly time series of Earth Rotation Parameters are estimated using Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), and combining GPS and GLONASS data collected from nearly 80 sites from 1 November 2012 to 10 April 2014. These new observations with combining different satellite systems can help to decorrelate orbit biases and ERP, which improve estimation of ERP. The high frequency variations of ERP are analyzed using a de-trending method. The maximum of total diurnal and semidiurnal variations are within one milli-arcseconds (mas) in Polar Motion (PM) and 0.5 milli-seconds (ms) in UT1-UTC. The semidiurnal and diurnal variations are mainly related to the ocean tides. Furthermore, the impacts of satellite orbit and time interval used to determinate ERP on the amplitudes of tidal terms are analyzed. We obtain some small terms that are not described in the ocean tide model of the IERS Conventions 2010, which may be caused by the strategies and models we used or the signal noises as well as artifacts. In addition, there are also small differences on the amplitudes between our results and IERS convention. This might be a result of other geophysical excitations, such as the high-frequency variations in atmospheric angular momentum (AAM) and hydrological angular momentum (HAM), which needs more detailed analysis with more geophysical data in the future.

Positioning and Navigation (PN) of Martian rovers still faces challenges due to limited observations. In this paper, the PN feasibilities of Mars rovers based on a Gravity-aided Odometry (GO) system are proposed and investigated in terms of numeric simulations and a case study. Statistical features of the Mars gravity field are studied to evaluate the feature diversity of the background map. The Iterative Closest Point (ICP) algorithm is introduced to match gravity measurements with the gravitational map. The trajectories of Mars Exploration Rovers (MER) and Mars Gravity Map 2011 (MGM2011) are used to complete the experiments. Several key factors of GO including odometry errors, measurement uncertainties, and grid resolution of the map are investigated to evaluate their influences on the positioning ability of the system. Simulated experiments indicate that the GO method could provide an alternative positioning solution for Martian surface rovers.