2,259,977 research outputs found
Study of Robust GNSS-based Positioning Solutions for Challenging Environments
No full textL'abstract è presente nell'allegato / the abstract is in the attachmen
A GNSS Multipath and NLoS Mitigation Method for Urban Scenarios Based on Particle Filtering
No full textGlobal navigation satellite systems (GNSSs) are at the basis of many location services. However, in harsh environments such as urban canyons, the performance can be highly degraded due to lack of satellite visibility and complex reflection phenomena like multipath and Non-Line-of-Sight (NLoS). This work aims at exploiting the consistency of the information provided by GNSS receivers to detect and mitigate the effect of multipath and NLoS on the positioning solution. The proposed method extends the definition of innovation for the Particle Filter (PF), while also exploiting its native capability to handle more complex probability models of the errors. The use of multi-modal probability densities adds robustness to the filter in harsh conditions. The proposed method has been tested on real open-source datasets, showing considerable improvement in terms of position error compared to other state-of-the-art solutions based on the Extended Kalman Filter (EKF)
Experientiality and Reversibility of the Aspectual Morpheme Guo in Mandarin Chinese: Temporal and Atemporal Perspectives
No full text[[abstract]] This paper argues for the property of reversibility toward the interpretation of the aspectual guo in Mandarin Chinese. After thorough examination of studies on its meaning in the literature, I point out that traditional analyses stress too heavily on the experientiality, while recent studies- such as Hsiao (2003), Pan and Lee (2004), Lin (2007), and Wu (2008)- focus too much on the resultant state entailed by guo to derive its meaning of discontinuity. I propose that experientiality or discontinuity still serves as the inherent meaning of guo, yet the resultant state in many events further encodes an extended meaning of reversibility which relates the temporal/physical properties of a discontinued event back to its pre-existing state.The semantics of guo based on this hypothesis is arguably a temporal as well as an atemporal notion. This account of guo is possible provided that the theory of time based on the cognitive grammar as proposed in Ahrens and Huang (2002) is adopted as the framework: in their theory the concept of time is conceived as a moving point over a landscape, and the ego facing the past is attached to this point in relation to the event. Under this assumption, the meaning of guo functions to discontinue the time and provides
the ego a viewpoint to conceptualize the reversibility property
Improved weighting in particle filters applied to precise state estimation in GNSS
No full textIn the last decades, the increasing complexity of the fusion of proprioceptive and exteroceptive sensors with Global Navigation Satellite System (GNSS) has motivated the exploration of Artificial Intelligence related strategies for the implementation of the navigation filters. In order to meet the strict requirements of accuracy and precision for Intelligent Transportation Systems (ITS) and Robotics, Bayesian inference algorithms are at the basis of current Positioning, Navigation, and Timing (PNT). Some scientific and technical contributions resort to Sequential Importance Resampling (SIR) Particle Filters (PF) to overcome the theoretical weaknesses of the more popular and efficient Kalman Filters (KFs) when the application relies on non-linear measurements models and non-Gaussian measurements errors. However, due to its higher computational burden, SIR PF is generally discarded. This paper presents a methodology named Multiple Weighting (MW) that reduces the computational burden of PF by considering the mutual information provided by the input measurements about the unknown state. An assessment of the proposed scheme is shown through an application to standalone GNSS estimation as a baseline of more complex multi-sensors, integrated solutions. By relying on the a-priori knowledge of the relationship between states and measurements, a change in the conventional PF routine allows performing a more efficient sampling of the posterior distribution. Results show that the proposed strategy can achieve any desired accuracy with a considerable reduction in the number of particles. Given a fixed and reasonable available computational effort, the proposed scheme allows for an accuracy improvement of the state estimate in the range of 20–40%
A Post-Processing Multipath/NLoS Bias Estimation Method Based on DBSCAN
No full textPositioning based on Global Navigation Satellite Systems (GNSSs) in urban environments always suffers from multipath and Non-Line-of-Sight (NLoS) effects. In such conditions, the GNSS pseudorange measurements can be affected by biases disrupting the GNSS-based applications. Many efforts have been devoted to detecting and mitigating the effects of multipath/NLoS, but the identification and classification of such events are still challenging. This research proposes a method for the post-processing estimation of pseudorange biases resulting from multipath/NLoS effects. Providing estimated pseudorange biases due to multipath/NLoS effects serves two main purposes. Firstly, machine learning-based techniques can leverage accurately estimated pseudorange biases as training data to detect and mitigate multipath/NLoS effects. Secondly, these accurately estimated pseudorange biases can serve as a benchmark for evaluating the effectiveness of the methods proposed to detect multipath/NLoS effects. The estimation is achieved by extracting the multipath/NLoS biases from pseudoranges using a clustering algorithm named Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The performance is demonstrated using two real-world data collections in multipath/NLoS scenarios for both static and dynamic conditions. Since there is no ground truth for the pseudorange biases due to the multipath/NLoS scenarios, the proposed method is validated based on the positioning performance. Positioning solutions are computed by subtracting the estimated biases from the raw pseudoranges and comparing them to the ground truth
HIF-1α Accumulation in Response to Transient Hypoglycemia May Worsen Diabetic Eye Disease (Guo et al data)
No full textUncut western blot gels for HIF-1α Accumulation in Response to Transient Hypoglycemia May Worsen Diabetic Eye Disease (Guo et al data
Improved Outdoor Target Tracking via EKF-based GNSS/UWB Tight Integration with Online Time Synchronisation
No full textAccurate and robust positioning technology in the mass-market segment is pivotal to support a number of critical Positioning, Navigation and Timing (PNT) applications. State-of-the-art Global Navigation Satellite System (GNSS) receivers design has been increasingly targeting flexible, embedded architectures integrating low-cost sensors to overcome GNSS limitations. The widespread proliferation of Ultra-Wide Band (UWB) technology, which enables centimeter-level accurate ranging in cluttered environments, is an appealing candidate for tight hybridisation with GNSS. When dealing with data streams from different Commercial-Off-The-Shelf (COTS) sensors, it is known that temporal misalignment is of concern, and accurate state-estimation via centralised, recursive filtering architectures can be undermined. As a first contribution, this work theoretically analyses the accuracy impact of asynchronous data association in the framework of a tightly integrated GNSS/UWB system leveraging plain Extended Kalman Filter (EKF) integration. Then, it puts forward a novel EKF-based model implementing online time offset estimation and compensation (i.e., time calibration) for GNSS/UWB tight integration. Results obtained in a multi-agent, cooperative scenario demonstrate that the proposed hybridisation methodology can achieve horizontal and vertical positioning accuracy gains of \SI{33.95}{\%} and \SI{59.33}{\%} , respectively, in Root-Mean-Square Error (RMSE) terms
Enhanced EKF-based Time Calibration for GNSS/UWB Tight Integration
No full textTight integration of low-cost Ultra-Wide Band (UWB) ranging sensors with mass-market Global Navigation Satellite System (GNSS) receivers is gaining attention as a high-accuracy positioning strategy for consumer applications dealing with challenging environments. However, due to independent clocks embedded in Commercial-Off-The-Shelf (COTS) chipsets, the time scales associated with sensor measurements are misaligned, leading to inconsistent data fusion. Centralized, recursive filtering architectures can compensate for this offset and achieve accurate state estimation. In line with this, a GNSS/UWB tight integration scheme based on an Extended Kalman Filter (EKF) is developed that performs online time calibration of the sensors' measurements by recursively modeling the GNSS/UWB time-offset as an additional unknown in the system state-space model. Furthermore, a double-update filtering model is proposed that embeds optimizations for the adaptive weighting of UWB measurements. Simulation results show that the double-update EKF algorithm can achieve a horizontal positioning accuracy gain of 41.60% over a plain EKF integration with uncalibrated time-offset and of 15.43% over the EKF with naive time-offset calibration. Moreover, a real-world experimental assessment demonstrates improved Root-Mean-Square Error (RMSE) performance of 57.58% and 31.03%, respectively
Comparison of GNSS Multipath/NLoS Characterization Between Geodetic Receivers and Smartphones Across GPS L1 C/A and L5 Signals
No full textThe issue of multipath and Non-Line-of-Sight (NLoS) interferences has significantly impacted the performance of Global Navigation Satellite System (GNSS) services in various emerging applications, such as autonomous vehicles and smart wearables. Characterizing the statistical pattern for multipath/NLoS interference might enlighten the development of techniques for detecting and mitigating such interferences. For this purpose, this research first introduces a method to estimate pseudorange biases caused by multipath/NLoS using a clustering algorithm. Then, the estimation method for the multipath/NLoS bias is extended to dual-frequency GNSS signals, including Global Positioning System (GPS) L1 C/A and L5. Subsequently, an experiment is carried out to collect and analyze real-world static GNSS data under multipath/NLoS environments. This analysis involved a comparative study of multipath/NLoS patterns using both a geodetic receiver and a smartphone across GPS L1 C/A and L5 signals. The experimental results uncovered some patterns in multipath/NLoS behaviors, offering insights that could potentially guide the development of new algorithms to detect and mitigate such interferences
Optimization analysis of subsea freight-glider
No full textThis master's thesis is written under the supervision of Professor Yihan Xin, assignment write about Subsea freight glider, which is designed with system ANSYS 2020. This master's thesis writes about how to improve Subsea Freight Glider construction, increase construction strength, increases transport quantity. In the best possible way to reduce deformation due to ambient pressure in underwater situation. This thesis uses Ansys 2020 system with drawing collaboration with professor Yihan, Ansys drawing (3D) is first analyzed with steel, to improve structural strength this thesis will analyze more about lightweight carbon fiber material, and analyze structure to find improving path that adapts to practical environment
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