GNSS Positioning using Cost Function Regulated Multilateration and Graph Neural Networks

• URL: http://arxiv.org/abs/2402.18630v1
• Date: Wed, 28 Feb 2024 19:00:01 GMT
• Title: GNSS Positioning using Cost Function Regulated Multilateration and Graph Neural Networks
• Authors: Amir Jalalirad, Davide Belli, Bence Major, Songwon Jee, Himanshu Shah, Will Morrison
• Abstract summary: In urban environments, where line-of-sight signals from satellites are frequently blocked by high-rise objects, receivers are subject to large errors in measuring satellite ranges. Heuristic methods are commonly used to estimate these errors and reduce the impact of noisy measurements on localization accuracy. In our work, we replace these error estimations with a deep learning model based on Graph Neural Networks.
• Score: 0.0699049312989311
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In urban environments, where line-of-sight signals from GNSS satellites are frequently blocked by high-rise objects, GNSS receivers are subject to large errors in measuring satellite ranges. Heuristic methods are commonly used to estimate these errors and reduce the impact of noisy measurements on localization accuracy. In our work, we replace these error estimation heuristics with a deep learning model based on Graph Neural Networks. Additionally, by analyzing the cost function of the multilateration process, we derive an optimal method to utilize the estimated errors. Our approach guarantees that the multilateration converges to the receiver's location as the error estimation accuracy increases. We evaluate our solution on a real-world dataset containing more than 100k GNSS epochs, collected from multiple cities with diverse characteristics. The empirical results show improvements from 40% to 80% in the horizontal localization error against recent deep learning baselines as well as classical localization approaches.

Related papers

• PrNet: A Neural Network for Correcting Pseudoranges to Improve Positioning with Android Raw GNSS Measurements [7.909678289680922]
  We present a neural network for mitigating biased errors in pseudoranges to improve localization performance with data collected from mobile phones. A satellite-wise Multilayer Perceptron (MLP) is designed to regress the pseudorange bias from six satellite, receiver, context-related features. The corrected pseudoranges are then used by a model-based localization engine to compute locations.
  arXiv  Detail & Related papers  (2023-09-16T10:43:59Z)
• Learning-based NLOS Detection and Uncertainty Prediction of GNSS Observations with Transformer-Enhanced LSTM Network [2.798138034569478]
  This work proposes a deeplearning-based method to detect NLOS and predict errors by analyzing pseudo-temporal modeling problem. We use datasets from Hong Kong and Aachen to train and evaluate the proposed network. We show that the proposed method avoids trajectory divergence in real-world vehicle localization by classifying and excluding NLOS observations.
  arXiv  Detail & Related papers  (2023-09-01T14:17:02Z)
• Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection [58.789823426981044]
  We propose a novel auxiliary loss formulation that aims to align the class confidence of bounding boxes with the accurateness of predictions. Our results reveal that our train-time loss surpasses strong calibration baselines in reducing calibration error for both in and out-domain scenarios.
  arXiv  Detail & Related papers  (2023-03-25T08:56:21Z)
• Comparison of machine learning algorithms for merging gridded satellite and earth-observed precipitation data [7.434517639563671]
  We use monthly earth-observed precipitation data from the Global Historical Climatology Network monthly database, version 2. Results suggest that extreme gradient boosting and random forests are the most accurate in terms of the squared error scoring function.
  arXiv  Detail & Related papers  (2022-12-17T09:39:39Z)
• On the Effective Usage of Priors in RSS-based Localization [56.68864078417909]
  We propose a Received Signal Strength (RSS) fingerprint and convolutional neural network-based algorithm, LocUNet. In this paper, we study the localization problem in dense urban settings. We first recognize LocUNet's ability to learn the underlying prior distribution of the Rx position or Rx and transmitter (Tx) association preferences from the training data, and attribute its high performance to these.
  arXiv  Detail & Related papers  (2022-11-28T00:31:02Z)
• Adaptive Self-supervision Algorithms for Physics-informed Neural Networks [59.822151945132525]
  Physics-informed neural networks (PINNs) incorporate physical knowledge from the problem domain as a soft constraint on the loss function. We study the impact of the location of the collocation points on the trainability of these models. We propose a novel adaptive collocation scheme which progressively allocates more collocation points to areas where the model is making higher errors.
  arXiv  Detail & Related papers  (2022-07-08T18:17:06Z)
• Navigating Local Minima in Quantized Spiking Neural Networks [3.1351527202068445]
  Spiking and Quantized Neural Networks (NNs) are becoming exceedingly important for hyper-efficient implementations of Deep Learning (DL) algorithms. These networks face challenges when trained using error backpropagation, due to the absence of gradient signals when applying hard thresholds. This paper presents a systematic evaluation of a cosine-annealed LR schedule coupled with weight-independent adaptive moment estimation.
  arXiv  Detail & Related papers  (2022-02-15T06:42:25Z)
• Semantic Perturbations with Normalizing Flows for Improved Generalization [62.998818375912506]
  We show that perturbations in the latent space can be used to define fully unsupervised data augmentations. We find that our latent adversarial perturbations adaptive to the classifier throughout its training are most effective.
  arXiv  Detail & Related papers  (2021-08-18T03:20:00Z)
• Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach [59.17191114000146]
  LocUNet: A convolutional, end-to-end trained neural network (NN) for the localization task. We show that LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the estimations of radio maps.
  arXiv  Detail & Related papers  (2021-06-23T17:27:04Z)
• A Biased Graph Neural Network Sampler with Near-Optimal Regret [57.70126763759996]
  Graph neural networks (GNN) have emerged as a vehicle for applying deep network architectures to graph and relational data. In this paper, we build upon existing work and treat GNN neighbor sampling as a multi-armed bandit problem. We introduce a newly-designed reward function that introduces some degree of bias designed to reduce variance and avoid unstable, possibly-unbounded payouts.
  arXiv  Detail & Related papers  (2021-03-01T15:55:58Z)
• Uncertainty Aware Deep Neural Network for Multistatic Localization with Application to Ultrasonic Structural Health Monitoring [0.0]
  This paper uses an uncertainty-aware deep neural distribution network framework to learn robust localization models. We show that the predictive uncertainty scales as environmental uncertainty increases to provide a statistically meaningful metric for assessing localization accuracy.
  arXiv  Detail & Related papers  (2020-07-14T04:53:06Z)

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.