A Semi-Supervised Learning Approach for Ranging Error Mitigation Based on UWB Waveform

• URL: http://arxiv.org/abs/2305.18208v1
• Date: Tue, 23 May 2023 10:08:42 GMT
• Title: A Semi-Supervised Learning Approach for Ranging Error Mitigation Based on UWB Waveform
• Authors: Yuxiao Li, Santiago Mazuelas, Yuan Shen
• Abstract summary: We propose a semi-supervised learning method based on variational Bayes for UWB ranging error mitigation. Our method can efficiently accumulate knowledge from both labeled and unlabeled data samples.
• Score: 29.827191184889898
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Localization systems based on ultra-wide band (UWB) measurements can have unsatisfactory performance in harsh environments due to the presence of non-line-of-sight (NLOS) errors. Learning-based methods for error mitigation have shown great performance improvement via directly exploiting the wideband waveform instead of handcrafted features. However, these methods require data samples fully labeled with actual measurement errors for training, which leads to time-consuming data collection. In this paper, we propose a semi-supervised learning method based on variational Bayes for UWB ranging error mitigation. Combining deep learning techniques and statistic tools, our method can efficiently accumulate knowledge from both labeled and unlabeled data samples. Extensive experiments illustrate the effectiveness of the proposed method under different supervision rates, and the superiority compared to other fully supervised methods even at a low supervision rate.

Related papers

• Wireless Channel Aware Data Augmentation Methods for Deep Learning-Based Indoor Localization [22.76179980847908]
  We propose methods that utilize the domain knowledge about wireless propagation channels and devices. We show that in the low-data regime, localization accuracy increases up to 50%, matching non-augmented results in the high-data regime. The proposed methods may outperform the measurement-only high-data performance by up to 33% using only 1/4 of the amount of measured data.
  arXiv  Detail & Related papers  (2024-08-12T19:01:49Z)
• Removing the need for ground truth UWB data collection: self-supervised ranging error correction using deep reinforcement learning [1.4061979259370274]
  Multipath effects and non-line-of-sight conditions cause ranging errors between anchors and tags. Existing approaches for mitigating these ranging errors rely on collecting large labeled datasets. This paper proposes a novel self-supervised deep reinforcement learning approach that does not require labeled ground truth data.
  arXiv  Detail & Related papers  (2024-03-28T09:36:55Z)
• A Channel-ensemble Approach: Unbiased and Low-variance Pseudo-labels is Critical for Semi-supervised Classification [61.473485511491795]
  Semi-supervised learning (SSL) is a practical challenge in computer vision. Pseudo-label (PL) methods, e.g., FixMatch and FreeMatch, obtain the State Of The Art (SOTA) performances in SSL. We propose a lightweight channel-based ensemble method to consolidate multiple inferior PLs into the theoretically guaranteed unbiased and low-variance one.
  arXiv  Detail & Related papers  (2024-03-27T09:49:37Z)
• Deep GEM-Based Network for Weakly Supervised UWB Ranging Error Mitigation [29.827191184889898]
  We present a learning framework based on weak supervision for UWB ranging error mitigation. Specifically, we propose a deep learning method based on the generalized expectation-maximization (GEM) algorithm for robust UWB ranging error mitigation.
  arXiv  Detail & Related papers  (2023-05-23T10:26:50Z)
• Efficient Deep Reinforcement Learning Requires Regulating Overfitting [91.88004732618381]
  We show that high temporal-difference (TD) error on the validation set of transitions is the main culprit that severely affects the performance of deep RL algorithms. We show that a simple online model selection method that targets the validation TD error is effective across state-based DMC and Gym tasks.
  arXiv  Detail & Related papers  (2023-04-20T17:11:05Z)
• Boosting Facial Expression Recognition by A Semi-Supervised Progressive Teacher [54.50747989860957]
  We propose a semi-supervised learning algorithm named Progressive Teacher (PT) to utilize reliable FER datasets as well as large-scale unlabeled expression images for effective training. Experiments on widely-used databases RAF-DB and FERPlus validate the effectiveness of our method, which achieves state-of-the-art performance with accuracy of 89.57% on RAF-DB.
  arXiv  Detail & Related papers  (2022-05-28T07:47:53Z)
• Scale-Equivalent Distillation for Semi-Supervised Object Detection [57.59525453301374]
  Recent Semi-Supervised Object Detection (SS-OD) methods are mainly based on self-training, generating hard pseudo-labels by a teacher model on unlabeled data as supervisory signals. We analyze the challenges these methods meet with the empirical experiment results. We introduce a novel approach, Scale-Equivalent Distillation (SED), which is a simple yet effective end-to-end knowledge distillation framework robust to large object size variance and class imbalance.
  arXiv  Detail & Related papers  (2022-03-23T07:33:37Z)
• DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation [57.358212277226315]
  In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator. Recent methods based on adversarial imitation learning have led to state-of-the-art performance on IfO problems, but they typically suffer from high sample complexity due to a reliance on data-inefficient, model-free reinforcement learning algorithms. This issue makes them impractical to deploy in real-world settings, where gathering samples can incur high costs in terms of time, energy, and risk. We propose a more data-efficient IfO algorithm
  arXiv  Detail & Related papers  (2021-03-31T23:46:32Z)
• Attentional-Biased Stochastic Gradient Descent [74.49926199036481]
  We present a provable method (named ABSGD) for addressing the data imbalance or label noise problem in deep learning. Our method is a simple modification to momentum SGD where we assign an individual importance weight to each sample in the mini-batch. ABSGD is flexible enough to combine with other robust losses without any additional cost.
  arXiv  Detail & Related papers  (2020-12-13T03:41:52Z)
• Robust Ultra-wideband Range Error Mitigation with Deep Learning at the Edge [0.0]
  Multipath effects, reflections, refractions, and complexity of the indoor radio environment can introduce a positive bias in the ranging measurement. This article proposes an efficient representation learning methodology that exploits the latest advancement in deep learning and graph optimization techniques. Channel Impulse Response (CIR) signals are directly exploited to extract high semantic features to estimate corrections in either NLoS or LoS conditions.
  arXiv  Detail & Related papers  (2020-11-30T10:52:21Z)

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.