ArticleScientific reports2024
Application of a long short-term memory neural network algorithm fused with Kalman filter in UWB indoor positioning.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Robust Kalman filter for heavy-tailed process and measurement noises.Scientific reports · 2026Article
- Domain-Adversarial Neural Network for UWB NLOS Identification in Multiple Environments.Sensors (Basel, Switzerland) · 2026Article
- Application of a temporal convolutional network algorithm fused with channel attention module for UWB indoor positioning.Scientific reports · 2026Article
- Disturbance observer based multiloop proportional feedback system for output voltage regulation in three phase AC to DC converters.Scientific reports · 2025Article
- Machine Learning Models for Indoor Positioning Using Bluetooth RSSI and Video Data: A Case Study.Sensors (Basel, Switzerland) · 2025Article
- Research on Multi-Sensor Fusion Localization for Forklift AGV Based on Adaptive Weight Extended Kalman Filter.Sensors (Basel, Switzerland) · 2025Article
- A Localization Enhancement Method Based on Direct-Path Identification and Tracking for Future Networks.Sensors (Basel, Switzerland) · 2025Article
- On Fusing Wireless Fingerprints with Pedestrian Dead Reckoning to Improve Indoor Localization Accuracy.Sensors (Basel, Switzerland) · 2025Article
- Proportional-type robust current controller under variable bandwidth technique for permanent magnet synchronous motors.Scientific reports · 2024Article
- Improved Kalman Filtering Algorithm Based on Levenberg-Marquart Algorithm in Ultra-Wideband Indoor Positioning.Sensors (Basel, Switzerland) · 2024Article
- Design a novel algorithm for enhancing UWB positioning accuracy in GPS denied environments.Scientific reports · 2024Article
- Application of density clustering with noise combined with particle swarm optimization in UWB indoor positioning.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Ultra-wideband technology has good anti-interference capabilities and development prospects in indoor positioning. Since ultra-wideband will be affected by random errors in indoor positioning, to exploit the advantages of the Kalman filter (KF) and the long short-term memory (LSTM) network, this paper proposes a long short-term memory neural network algorithm fused with the Kalman filter (KF-LSTM) to improve UWB positioning. First, the ultra-wideband data is processed through KF to weaken the noise in the data, and then the data is fed into the LSTM network for training, and the capability of the LSTM network to process time series features is employed to obtain more accurate label positions. Finally, simulation and measurement results show that the KF-LSTM algorithm achieves 71.31%, 37.28%, and 49.31% higher average positioning accuracy than the back propagation (BP) network, (back propagation network fused with the Kalman filter (KF-BP), and LSTM network algorithms, respectively, and the KF-LSTM algorithm performs more stably. Meanwhile, the more noise the data contains, the more obvious the stability contrast between the four algorithms.
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Registered trials
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