Real-time Ionospheric Imaging of S4 Scintillation from Limited Data with Parallel Kalman Filters and Smoothness

• URL: http://arxiv.org/abs/2105.05360v1
• Date: Tue, 11 May 2021 23:09:14 GMT
• Title: Real-time Ionospheric Imaging of S4 Scintillation from Limited Data with Parallel Kalman Filters and Smoothness
• Authors: Alexandra Koulouri
• Abstract summary: We create two dimensional ionospheric images of S4 amplitude scintillation at 350 km over South America with temporal resolution of one minute. Our results show that in areas with a network of ground receivers with a relatively good coverage the produced images can provide reliable real-time results.
• Score: 91.3755431537592
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a Bayesian framework to create two dimensional ionospheric images of high spatio-temporal resolution to monitor ionospheric irregularities as measured by the S4 index. Here, we recast the standard Bayesian recursive filtering for a linear Gaussian state-space model, also referred to as the Kalman filter, first by augmenting the (pierce point) observation model with connectivity information stemming from the insight and assumptions/standard modeling about the spatial distribution of the scintillation activity on the ionospheric shell at 350 km altitude. Thus, we achieve to handle the limited spatio-temporal observations. Then, by introducing a set of Kalman filters running in parallel, we mitigate the uncertainty related to a tuning parameter of the proposed augmented model. The output images are a weighted average of the state estimates of the individual filters. We demonstrate our approach by rendering two dimensional real-time ionospheric images of S4 amplitude scintillation at 350 km over South America with temporal resolution of one minute. Furthermore, we employ extra S4 data that was not used in producing these ionospheric images, to check and verify the ability of our images to predict this extra data in particular ionospheric pierce points. Our results show that in areas with a network of ground receivers with a relatively good coverage (e.g. within a couple of kilometers distance) the produced images can provide reliable real-time results. Our proposed algorithmic framework can be readily used to visualize real-time ionospheric images taking as inputs the available scintillation data provided from freely available web-servers.

Related papers

• Weighted Spectral Filters for Kernel Interpolation on Spheres: Estimates of Prediction Accuracy for Noisy Data [21.67168506689593]
  We introduce a weighted spectral filter approach to reduce the condition number of the kernel matrix and then stabilize kernel. Using a recently developed integral operator approach for spherical data analysis, we theoretically demonstrate that the proposed weighted spectral filter approach succeeds in breaking through the bottleneck of kernel. We provide optimal approximation rates of the new method to show that our approach does not compromise the predicting accuracy.
  arXiv  Detail & Related papers  (2024-01-16T13:46:10Z)
• GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis [70.24111297192057]
  We present a new approach, termed GPS-Gaussian, for synthesizing novel views of a character in a real-time manner. The proposed method enables 2K-resolution rendering under a sparse-view camera setting.
  arXiv  Detail & Related papers  (2023-12-04T18:59:55Z)
• Few-shot Non-line-of-sight Imaging with Signal-surface Collaborative Regularization [18.466941045530408]
  Non-line-of-sight imaging technique aims to reconstruct targets from multiply reflected light. We propose a signal-surface collaborative regularization framework that provides noise-robust reconstructions with a minimal number of measurements. Our approach has great potential in real-time non-line-of-sight imaging applications such as rescue operations and autonomous driving.
  arXiv  Detail & Related papers  (2022-11-21T11:19:20Z)
• CLONeR: Camera-Lidar Fusion for Occupancy Grid-aided Neural Representations [77.90883737693325]
  This paper proposes CLONeR, which significantly improves upon NeRF by allowing it to model large outdoor driving scenes observed from sparse input sensor views. This is achieved by decoupling occupancy and color learning within the NeRF framework into separate Multi-Layer Perceptrons (MLPs) trained using LiDAR and camera data, respectively. In addition, this paper proposes a novel method to build differentiable 3D Occupancy Grid Maps (OGM) alongside the NeRF model, and leverage this occupancy grid for improved sampling of points along a ray for rendering in metric space.
  arXiv  Detail & Related papers  (2022-09-02T17:44:50Z)
• Improving trajectory calculations using deep learning inspired single image superresolution [0.0]
  We train various versions of the state-of-the-art Enhanced Deep Residual Networks for Superresolution on low-resolution ERA5 reanalysis data. We show that the resulting up-scaled wind fields have root-mean-squared half the size of the winds obtained with linear spatial inference.
  arXiv  Detail & Related papers  (2022-06-07T14:28:05Z)
• Beyond Cross-view Image Retrieval: Highly Accurate Vehicle Localization Using Satellite Image [91.29546868637911]
  This paper addresses the problem of vehicle-mounted camera localization by matching a ground-level image with an overhead-view satellite map. The key idea is to formulate the task as pose estimation and solve it by neural-net based optimization. Experiments on standard autonomous vehicle localization datasets have confirmed the superiority of the proposed method.
  arXiv  Detail & Related papers  (2022-04-10T19:16:58Z)
• Connect the Dots: In Situ 4D Seismic Monitoring of CO$_2$ Storage with Spatio-temporal CNNs [16.596385405707977]
  4D seismic imaging has been widely used in CO$$ sequestration projects to monitor the fluid flow in the volumetric region that is not sampled by wells. We develop neural-network-based models that can produce high-fidelity interpolated images effectively and efficiently. Our models are built on an autoencoder, and incorporate the long short-term memory (LSTM) structure with a new loss function regularized optical flow.
  arXiv  Detail & Related papers  (2021-05-25T02:38:22Z)
• Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereo [61.6260594326246]
  We introduce an efficient fully-convolutional architecture that can leverage both spatial and photometric context simultaneously. Using separable 4D convolutions and 2D heat-maps reduces the size and makes more efficient.
  arXiv  Detail & Related papers  (2021-03-22T18:06:58Z)
• Predicting Landsat Reflectance with Deep Generative Fusion [2.867517731896504]
  Public satellite missions are commonly bound to a trade-off between spatial and temporal resolution. This hinders their potential to assist vegetation monitoring or humanitarian actions. We probe the potential of deep generative models to produce high-resolution optical imagery by fusing products with different spatial and temporal characteristics.
  arXiv  Detail & Related papers  (2020-11-09T21:06:04Z)
• 4D Spatio-Temporal Convolutional Networks for Object Position Estimation in OCT Volumes [69.62333053044712]
  3D convolutional neural networks (CNNs) have shown promising performance for pose estimation of a marker object using single OCT images. We extend 3D CNNs to 4D-temporal CNNs to evaluate the impact of additional temporal information for marker object tracking.
  arXiv  Detail & Related papers  (2020-07-02T12:02:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.