ArticleSensors (Basel, Switzerland)2019
A Deep Learning Approach to Position Estimation from Channel Impulse Responses.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
9 citing papers in PubMed.
- Location-Aware Range-Error Correction for Improved UWB Localization.Sensors (Basel, Switzerland) · 2024Article
- Applied Machine Learning for IIoT and Smart Production-Methods to Improve Production Quality, Safety and Sustainability.Sensors (Basel, Switzerland) · 2022Article
- A Direct Position Determination Method Based on Subspace Orthogonality in Cross-Spectra under Multipath Environments.Sensors (Basel, Switzerland) · 2022Article
- RNN-Aided Human Velocity Estimation from a Single IMU.Sensors (Basel, Switzerland) · 2020Article
- Deep Learning-Based Human Activity Real-Time Recognition for Pedestrian Navigation.Sensors (Basel, Switzerland) · 2020Article
- Acoustic Indoor Localization Augmentation by Self-Calibration and Machine Learning.Sensors (Basel, Switzerland) · 2020Article
- A Robust PDR/UWB Integrated Indoor Localization Approach for Pedestrians in Harsh Environments.Sensors (Basel, Switzerland) · 2019Article
- UWB Channel Impulse Responses for Positioning in Complex Environments: A Detailed Feature Analysis.Sensors (Basel, Switzerland) · 2019Article
- 3D Tdoa Problem Solution with Four Receiving Nodes.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Radio-based locating systems allow for a robust and continuous tracking in industrial environments and are a key enabler for the digitalization of processes in many areas such as production, manufacturing, and warehouse management. Time difference of arrival (TDoA) systems estimate the time-of-flight (ToF) of radio burst signals with a set of synchronized antennas from which they trilaterate accurate position estimates of mobile tags. However, in industrial environments where multipath propagation is predominant it is difficult to extract the correct ToF of the signal. This article shows how deep learning (DL) can be used to estimate the position of mobile objects directly from the raw channel impulse responses (CIR) extracted at the receivers. Our experiments show that our DL-based position estimation not only works well under harsh multipath propagation but also outperforms state-of-the-art approaches in line-of-sight situations.
Indexed as
Identifiers
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.