Current Issue : April-June Volume : 2024 Issue Number : 2 Articles : 5 Articles
To realize the societal need for greener, safer, and smarter mobility, ambitious technical challenges need to be addressed. With this aim, the H2020-EUSPA project ESRIUM investigates various aspects of highly accurate, reliable, and assured EGNSS localization information for road vehicles with a particular focus on automated vehicles. To analyze the achievable accuracy, reliability, and availability of multi-frequency and multi-GNSS mass-market receivers, we have conducted test drives under different GNSS reception conditions. In the tests, special focus was placed on using the Galileo Open Service Navigation Message Authentication (OSNMA) service, offering an additional feature for assured PVT (position, velocity, and time) information with respect to spoofing. We analyzed the performance of three Septentrio Mosaic-X5 receivers operated with different OSNMA settings. It could be shown that strict use of OSNMA provides very good positioning accuracy as long as sufficient suitable satellites are available. However, the overall performance suffers from a reduced satellite number and is therefore limited. The performance of a receiver using authenticated Galileo with GPS signals (final status of Galileo OSNMA) is very good for a mass-market receiver: 92.55% of the solutions had a 2D position error below 20 cm during 8.5 h of driving through different environments....
The precise point positioning (PPP) technique generally takes tens of minutes to converge, severely limiting its use. This longer convergence time is mainly due to the slower variation of satellite geometry in space and the stronger correlation of unknown parameters to be estimated. Fortunately, the lower orbit altitude of Low Earth Orbit (LEO) satellites contributes to the fast variation of the satellites’ spatial geometry. In addition, high-precision atmospheric delay information has become readily available, which can help decrease unknown parameters’ correlation. This study proposes a double-augmentation PPP approach with accelerated convergence by tightly integrating the LEO/atmosphere-augmented information. The GNSS observations in both mid-latitude and low-latitude areas, and simulated LEO observations under aWalker/polar mixed constellation, are used to validate the double-augmentation PPP approach. Test results in both areas indicate that the double-augmentation PPP can converge within 0.8 min, improving the convergence time by over 73%, and over 83% compared to the LEO-only augmented PPP and atmosphere-only augmented PPP....
Ionospheric scintillation often occurs in the polar and equator regions, and it can affect the signals of the Global Navigation Satellite System (GNSS). Therefore, the ionospheric scintillation detection applied to the polar and equator regions is of vital importance for improving the performance of satellite navigation. GNSS radio occultation is a remote sensing technique that primarily utilizes GNSS signals to study the Earth’s atmosphere, but its measurement results are susceptible to the effects of ionospheric scintillation. In this study, we propose an ionospheric scintillation detection algorithm based on the Sparrow-Search-Algorithm-optimized Extreme Gradient Boosting model (SSA-XGBoost), which uses power spectral densities of the raw signal intensities from GNSS occultation data as input features to train the algorithm model. To assess the performance of the proposed algorithm, we compare it with other machine learning algorithms such as XGBoost and a Support Vector Machine (SVM) using historical ionospheric scintillation data. The results show that the SSA-XGBoost method performs much better compared to the SVM and XGBoost models, with an overall accuracy of 97.8% in classifying scintillation events and a miss detection rate of only 12.9% for scintillation events with an unbalanced GNSS RO dataset. This paper can provide valuable insights for designing more robust GNSS receivers....
Gamma-ray burst GRB221009A was of unprecedented brightness in the γ-rays and X-rays through to the far ultraviolet, allowing for identification within a host galaxy at redshift z = 0.151 by multiple space and ground-based optical/near-infrared telescopes and enabling a first association— via cosmic-ray air-shower events—with a photon of 251 TeV. That is in direct tension with a potentially observable phenomenon of quantum gravity (QG), where spacetime “foaminess” accumulates in wavefronts propagating cosmological distances, and at high-enough energy could render distant yet bright pointlike objects invisible, by effectively spreading their photons out over the whole sky. But this effect would not result in photon loss, so it remains distinct from any absorption by extragalactic background light. A simple multiwavelength average of foam-induced blurring is described, analogous to atmospheric seeing from the ground. When scaled within the fields of view for the Fermi and Swift instruments, it fits all z ≤ 5 GRB angular-resolution data of 10 MeV or any lesser peak energy and can still be consistent with the highest-energy localization of GRB221009A: a limiting bound of about 1 degree is in agreement with a holographic QG-favored formulation....
Path planning and obstacle avoidance are pivotal for intelligent unmanned aerial vehicle (UAV) systems in various domains, such as postdisaster rescue, target detection, and wildlife conservation. Currently, reinforcement learning (RL) has become increasingly popular in UAV decision-making. However, the RL approaches confront the challenges of partial observation and large state space when searching for random targets through continuous actions. This paper proposes a representation enhancement-based proximal policy optimization (RE-PPO) framework to address these issues. The representation enhancement (RE) module consists of observation memory improvement (OMI) and dynamic relative position-attitude reshaping (DRPAR). OMI reduces collision under partially observable conditions by separately extracting perception features and state features through an embedding network and feeding the extracted features to a gated recurrent unit (GRU) to enhance observation memory. DRPAR compresses the state space when modeling continuous actions by transforming movement trajectories of different episodes from an absolute coordinate system into different local coordinate systems to utilize similarity. In addition, three stepwise reward functions are formulated to avoid sparsity and facilitate model convergence. We evaluate the proposed method in three 3D scenarios to demonstrate its effectiveness. Compared to other methods, our method achieves a faster convergence during training and demonstrates a higher success rate and a lower rate of timeout and collision during inference. Our method can significantly enhance the autonomy and intelligence of UAV systems under partially observable conditions and provide a reasonable solution for UAV decision-making under uncertainties....
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