{
  "id": 238615,
  "title": "Papers on High Precision GNSS positioning and machine learning",
  "url": "/competitions/google-smartphone-decimeter-challenge/discussion/238615",
  "author_name": "Charlie Craine",
  "post_date": "2021-05-12T19:19:19.693000",
  "votes": 15,
  "comment_count": 0,
  "views": 0,
  "content": "<p>Hey everyone!</p>\n<p>I wanted to start a papers thread and build on it, and hope others share as well, papers to gain domain knowledge. I have no domain knowledge in this area so I have downloaded papers that appeared to be relevant after reading their abstracts. I'll be reading these over the coming days and commenting more as I go through all of them. Hope this helps!</p>\n<p><strong>Research Papers:</strong></p>\n<ul>\n<li><p><a href=\"https://www.mdpi.com/2072-4292/12/6/971\" target=\"_blank\">Anomaly Detection for Urban Vehicle GNSS Observation with a Hybrid Machine Learning System</a> - This paper attempts to construct an alternative framework for quality identification of GNSS observations combining clustering-based anomaly detection and supervised classification, in which the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm is used to label the offline dataset as normal and anomalous observations without the aid of 3D building models, and the supervised classifier in the online system learns the classification rule for real-time anomaly detection. </p></li>\n<li><p><a href=\"https://arxiv.org/abs/2006.03772\" target=\"_blank\">A precise machine learning aided algorithm for land subsidence or upheave prediction from GNSS time series</a> - This paper is aimed at the problem of predicting the land subsidence or upheave in an area, using GNSS position time series. Since machine learning algorithms have presented themselves as strong prediction tools in different fields of science, we employ them to predict the next values of the GNSS position time series. </p></li>\n<li><p><a href=\"https://ieeexplore.ieee.org/abstract/document/9253636\" target=\"_blank\">Improving GPS Code Phase Positioning Accuracy in Urban Environments Using Machine Learning</a> - The accuracy of location information, mainly provided by the global positioning system (GPS) sensor, is critical for Internet-of-Things applications in smart cities. However, built environments attenuate GPS signals by reflecting or blocking them resulting in some cases multipath and non-line-of-sight (NLOS) reception. </p></li>\n<li><p><a href=\"https://arxiv.org/abs/2006.07891\" target=\"_blank\">Lateral land movement prediction from GNSS position time series in a machine learning aided algorithm</a> - We investigate the accuracy of conventional machine learning aided algorithms for the prediction of lateral land movement in an area using the precise position time series of permanent GNSS stations.</p></li>\n<li><p><a href=\"http://kth.diva-portal.org/smash/get/diva2:1362035/FULLTEXT01.pdf\" target=\"_blank\">GNSS Position Error Estimated by Machine Learning Techniques with Environmental Information Input</a> </p></li>\n<li><p><a href=\"https://arxiv.org/abs/2011.10743\" target=\"_blank\">Semantic-Based VPS for Smartphone Localization in Challenging Urban Environments</a> </p></li>\n</ul>",
  "messages": [
    {
      "id": 1304691,
      "postDate": "2021-05-12T19:19:19.693Z",
      "content": "<p>Hey everyone!</p>\n<p>I wanted to start a papers thread and build on it, and hope others share as well, papers to gain domain knowledge. I have no domain knowledge in this area so I have downloaded papers that appeared to be relevant after reading their abstracts. I'll be reading these over the coming days and commenting more as I go through all of them. Hope this helps!</p>\n<p><strong>Research Papers:</strong></p>\n<ul>\n<li><p><a href=\"https://www.mdpi.com/2072-4292/12/6/971\" target=\"_blank\">Anomaly Detection for Urban Vehicle GNSS Observation with a Hybrid Machine Learning System</a> - This paper attempts to construct an alternative framework for quality identification of GNSS observations combining clustering-based anomaly detection and supervised classification, in which the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm is used to label the offline dataset as normal and anomalous observations without the aid of 3D building models, and the supervised classifier in the online system learns the classification rule for real-time anomaly detection. </p></li>\n<li><p><a href=\"https://arxiv.org/abs/2006.03772\" target=\"_blank\">A precise machine learning aided algorithm for land subsidence or upheave prediction from GNSS time series</a> - This paper is aimed at the problem of predicting the land subsidence or upheave in an area, using GNSS position time series. Since machine learning algorithms have presented themselves as strong prediction tools in different fields of science, we employ them to predict the next values of the GNSS position time series. </p></li>\n<li><p><a href=\"https://ieeexplore.ieee.org/abstract/document/9253636\" target=\"_blank\">Improving GPS Code Phase Positioning Accuracy in Urban Environments Using Machine Learning</a> - The accuracy of location information, mainly provided by the global positioning system (GPS) sensor, is critical for Internet-of-Things applications in smart cities. However, built environments attenuate GPS signals by reflecting or blocking them resulting in some cases multipath and non-line-of-sight (NLOS) reception. </p></li>\n<li><p><a href=\"https://arxiv.org/abs/2006.07891\" target=\"_blank\">Lateral land movement prediction from GNSS position time series in a machine learning aided algorithm</a> - We investigate the accuracy of conventional machine learning aided algorithms for the prediction of lateral land movement in an area using the precise position time series of permanent GNSS stations.</p></li>\n<li><p><a href=\"http://kth.diva-portal.org/smash/get/diva2:1362035/FULLTEXT01.pdf\" target=\"_blank\">GNSS Position Error Estimated by Machine Learning Techniques with Environmental Information Input</a> </p></li>\n<li><p><a href=\"https://arxiv.org/abs/2011.10743\" target=\"_blank\">Semantic-Based VPS for Smartphone Localization in Challenging Urban Environments</a> </p></li>\n</ul>",
      "rawMarkdown": "Hey everyone!\n\nI wanted to start a papers thread and build on it, and hope others share as well, papers to gain domain knowledge. I have no domain knowledge in this area so I have downloaded papers that appeared to be relevant after reading their abstracts. I'll be reading these over the coming days and commenting more as I go through all of them. Hope this helps!\n\n**Research Papers:**\n\n- [Anomaly Detection for Urban Vehicle GNSS Observation with a Hybrid Machine Learning System](https://www.mdpi.com/2072-4292/12/6/971) - This paper attempts to construct an alternative framework for quality identification of GNSS observations combining clustering-based anomaly detection and supervised classification, in which the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm is used to label the offline dataset as normal and anomalous observations without the aid of 3D building models, and the supervised classifier in the online system learns the classification rule for real-time anomaly detection. \n\n- [A precise machine learning aided algorithm for land subsidence or upheave prediction from GNSS time series](https://arxiv.org/abs/2006.03772) - This paper is aimed at the problem of predicting the land subsidence or upheave in an area, using GNSS position time series. Since machine learning algorithms have presented themselves as strong prediction tools in different fields of science, we employ them to predict the next values of the GNSS position time series. \n\n- [Improving GPS Code Phase Positioning Accuracy in Urban Environments Using Machine Learning](https://ieeexplore.ieee.org/abstract/document/9253636) - The accuracy of location information, mainly provided by the global positioning system (GPS) sensor, is critical for Internet-of-Things applications in smart cities. However, built environments attenuate GPS signals by reflecting or blocking them resulting in some cases multipath and non-line-of-sight (NLOS) reception. \n\n- [Lateral land movement prediction from GNSS position time series in a machine learning aided algorithm](https://arxiv.org/abs/2006.07891) - We investigate the accuracy of conventional machine learning aided algorithms for the prediction of lateral land movement in an area using the precise position time series of permanent GNSS stations.\n\n- [GNSS Position Error Estimated by Machine Learning Techniques with Environmental Information Input](http://kth.diva-portal.org/smash/get/diva2:1362035/FULLTEXT01.pdf) \n\n- [Semantic-Based VPS for Smartphone Localization in Challenging Urban Environments](https://arxiv.org/abs/2011.10743) ",
      "votes": 14
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "1304691": "Hey everyone!\n\nI wanted to start a papers thread and build on it, and hope others share as well, papers to gain domain knowledge. I have no domain knowledge in this area so I have downloaded papers that appeared to be relevant after reading their abstracts. I'll be reading these over the coming days and commenting more as I go through all of them. Hope this helps!\n\n**Research Papers:**\n\n- [Anomaly Detection for Urban Vehicle GNSS Observation with a Hybrid Machine Learning System](https://www.mdpi.com/2072-4292/12/6/971) - This paper attempts to construct an alternative framework for quality identification of GNSS observations combining clustering-based anomaly detection and supervised classification, in which the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm is used to label the offline dataset as normal and anomalous observations without the aid of 3D building models, and the supervised classifier in the online system learns the classification rule for real-time anomaly detection. \n\n- [A precise machine learning aided algorithm for land subsidence or upheave prediction from GNSS time series](https://arxiv.org/abs/2006.03772) - This paper is aimed at the problem of predicting the land subsidence or upheave in an area, using GNSS position time series. Since machine learning algorithms have presented themselves as strong prediction tools in different fields of science, we employ them to predict the next values of the GNSS position time series. \n\n- [Improving GPS Code Phase Positioning Accuracy in Urban Environments Using Machine Learning](https://ieeexplore.ieee.org/abstract/document/9253636) - The accuracy of location information, mainly provided by the global positioning system (GPS) sensor, is critical for Internet-of-Things applications in smart cities. However, built environments attenuate GPS signals by reflecting or blocking them resulting in some cases multipath and non-line-of-sight (NLOS) reception. \n\n- [Lateral land movement prediction from GNSS position time series in a machine learning aided algorithm](https://arxiv.org/abs/2006.07891) - We investigate the accuracy of conventional machine learning aided algorithms for the prediction of lateral land movement in an area using the precise position time series of permanent GNSS stations.\n\n- [GNSS Position Error Estimated by Machine Learning Techniques with Environmental Information Input](http://kth.diva-portal.org/smash/get/diva2:1362035/FULLTEXT01.pdf) \n\n- [Semantic-Based VPS for Smartphone Localization in Challenging Urban Environments](https://arxiv.org/abs/2011.10743) "
  }
}