Satellite navigation systems such as the Global Positioning System (GPS) makes it possible for users to find their relative or absolute position. Thanks to its mobility and reliability, the GPS is used in many civil and military applications. However, the GPS does not provide an advanced level of security. Therefore, it could be potentially a target of attacks. With the development of new GPS attacks, the security knowledge has to grow at the same rate, so existing attacks can be detected by updated versions of receiver software or hardware. In this paper, a comparative analysis of GPS receiver resilience to software attacks is performed with the help of GNSS simulator from Spirent. The main objective of this work is to perform a sensitivity analysis of variables involved in calculation of position of the GPS receivers from different price bands that might be targeted by existing or future GPS attack. Variables making the biggest impact on calculated position are determined using the model. Experimentation validation of their influence is performed using selected receivers and corrupted signals generated by GNSS simulator. The testing is based on tuning the selected variables in order to simulate the theoretical error obtained from the sensitivity analysis. The results obtained from testing are discussed in order to analyse the behaviour of the considered GNSS receivers (including the premium class ones) and establish whether they provide a protection from existing or potential GPS attacks.
One of the most crucial factors for the overall success of an Unmanned Aerial Vehicle (UAV) mission is navigation performance, which is severely affected in Global Navigation Satellite Systems (GNSS) challenging environments. A solution to this problem could come through path planning optimization. This paper investigates the impact that GNSS quality information included in the UAV path planning process would have on the overall UAV mission success rate (MSR) when flying through an urban canyon. Number of visible satellites and Horizontal Dilution of Precision (HDOP) in addition to mission-specific requirements are given as input to the Particle Swarm Optimization (PSO) algorithm to calculate the optimal path for two cases. One includes the GNSS observables, and the other does not. Optimal paths for three different altitudes are obtained. All paths are simulated by a GNSS signal simulator, including a comprehensive multipath model. GNSS data are collected by a hardware receiver for analysis of the UAV positioning error and GNSS availability. Mission failures cases are defined accordingly, and the overall mission success rate (MSR) of each scenario is assessed. By analyzing the findings, it is concluded that in 83% of cases, the path planning process that included GNSS information was able to increase the MSR. Also, the increase in MSR was bigger when flying at low altitude.
As the primary navigation source, GNSS performance monitoring and prediction have critical importance for the success of mission-critical urban air mobility and cargo applications. In this paper, a novel machine learning based performance prediction algorithm is suggested considering environment recognition. Valid environmental parameters that support recognition and prediction stages are introduced, and K-Nearest Neighbour, Support Vector Regression and Random Forest algorithms are tested based on their prediction performance with using these environmental parameters. Performance prediction results and parameter importances are analyzed based on three types of urban environments (suburban, urban and urban-canyon) with the synthetic data generated by a high quality GNSS simulator.
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