ArticleScientific reports2025
Attention based LSTM framework for robust UWB and INS integration in NLOS environments.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advancements in Machine Learning-Assisted Flexible Electronics: Technologies, Applications, and Future Prospects.Biosensors · 2026Pooled it
- From IMU Streams to Real-Time Decisions: Past-Only Next-Window Badminton Action Prediction.Sensors (Basel, Switzerland) · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
This paper proposes a novel UWB/INS integration framework that utilizes attention-based Long Short-Term Memory (LSTM) neural networks to address challenges related to UWB signal degradation during non-line-of-sight (NLOS) propagation. The network is adopted to generate pseudo measurements to maintain Kalman filter measurement update during NLOS. LSTM networks are well-suited for modeling sequential data due to their ability to capture long-term dependencies, making them particularly effective in handling the temporal aspects of navigation data. By leveraging attention mechanisms, the proposed approach enhances temporal feature extraction and improves the accuracy of pseudo-UWB observations generation. Extensive experiments demonstrate that the attention-LSTM model significantly reduces positioning errors under both loosely and tightly coupled configurations in NLOS scenarios. This hybrid fusion of model-based and learning-based techniques ensures robust and precise UWB/INS localization.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.