惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
美团技术团队
Last Week in AI
Last Week in AI
WordPress大学
WordPress大学
博客园 - 三生石上(FineUI控件)
博客园 - 聂微东
雷峰网
雷峰网
阮一峰的网络日志
阮一峰的网络日志
博客园 - 叶小钗
IT之家
IT之家
Google DeepMind News
Google DeepMind News
D
Docker
J
Java Code Geeks
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
Apple Machine Learning Research
Apple Machine Learning Research
博客园 - 【当耐特】
V
V2EX
Hugging Face - Blog
Hugging Face - Blog
博客园 - Franky
月光博客
月光博客
宝玉的分享
宝玉的分享
酷 壳 – CoolShell
酷 壳 – CoolShell
aimingoo的专栏
aimingoo的专栏
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More

eess.SP updates on arXiv.org

ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Federated Learning with Hypergradient-based Online Update of Aggregation Weights Soft Graph Diffusion Transformer for MIMO Detection SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting Sequential Inference for Gaussian Processes: A Signal Processing Perspective Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework Recent Advances in mm-Wave and Sub-THz/THz Oscillators for FutureG Technologies Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation Adaptive Transform Coding for Semantic Compression EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures Sparse Graph Learning from Sparse Data via Fiedler Number Maximization A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals EVT-Based Generative AI for Tail-Aware Channel Estimation Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring Speech Enhancement Based on Drifting Models Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids Explainable AI in Speaker Recognition -- Making Latent Representations Understandable Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals An Algorithm for On-Sensor Agnostic Detection of Changes in Human Activity for Ultra-Low-Power Applications
Diagnostics of GNSS-based Virtual Balise in Railway Using...
2019-02-18 · via eess.SP updates on arXiv.org

The use of GNSS in the railway sector has been postulated on the notion of a Virtual Balise (VB). The VB-based positioning system works by setting a VB point on the railway track and determining the passage of the VB point using the position solution from the GNSS receiver. Although augmentation systems such as SBAS or GBAS are able to satisfy the integrity requirements of the aviation standards down to the 10-7 level, it is difficult to satisfy the high integrity requirements of the railway sector because firstly the railway users located on the ground are affected by the ground environment such as terrain, buildings and tunnels and secondly because the stringency of the railway sector requirements extends below the 10-9 level. This paper proposes a method to detect faults in the GNSS solution due to satellite failure or local effects. Firstly, requirements for the monitoring performance are carefully derived accounting for the specificities of GNSS, namely that the possibility of consecutive VB faults cannot be discounted. The second contribution of the paper is the proposed detection using both odometry and track geometry of the onboard system. This enables to monitor all three-dimensional solution error so that higher sensitivity for the fault detection can be achieved. Simulations have been performed with both single and dual (GPS, GALILEO) solutions. It has been found that the combinations of metrics are able to achieve very small missed detection probabilities for mean failure rates from 5.0m/s down to 0.03m/s for most dual constellation geometries. The detection performance of the odometer implementation varied according to the heading of the train. On the other hand, when odometry and track geometry are used together, all the three-directional monitors can obtain stable results regardless of the heading.