Cooperative Indoor Localization System Based UWB and Random Forest Algorithm In Complicated Underground NLOS Scenario

Yang, Yaodong; Zhang, Li; Xu, Jingao; Li, Danyang; Bao, Jinhui; Tan, Jieqing

doi:10.1109/icdh57206.2022.00049

Cited by 8 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Reference [15], an NLOS identification method with fuzzy credibility-based support vector machines (FC-SVM) and dynamic threshold comparison (DTC) is proposed, this is done in two steps, starting with a coarse-grained NLOS classification using the DTC approach, then moving on to a fine-grained result using FC-SVM. A UWB positioning system based on RF is proposed in Reference [16], the RF algorithm is innovatively applied to Kalman filter measurement update, and the Taylor algorithm is adopted to improve the estimation accuracy. To address the performance degradation caused by the disproportionate number of LOS and NLOS signals, Che et al [17] proposed the Weighted Naive Bayes algorithm to reduce the impact of the limited number of NLOS components on the train the model.…”

Section: Related Work a Nlos Identificationmentioning

confidence: 99%

NLOS Identification for UWB Positioning Based on IDBO and Convolutional Neural Networks

Kong

2023

IEEE Access

View full text Add to dashboard Cite

Ultra-wideband (UWB) is regarded as the technology with the most potential for precise indoor location due to its centimeter-level ranging capabilities, good time resolution, and low power consumption. However, Because of the presence of non-line-of-sight (NLOS) error, the accuracy of UWB localization deteriorates significantly in harsh and volatile indoor conditions. Therefore, identifying NLOS conditions is crucial to enhancing the accuracy of UWB location. This paper proposes a convolutional neural network (CNN) classification method based on an improved Dung Beetle Optimizer (DBO). Firstly, based on the standard DBO, the Circle chaotic mapping, non-uniform Gaussian variational strategy, and multi-stage perturbation strategy are used to optimize the exploration capability and enhance the performance of original DBO method, the superiority-seeking ability of IDBO is demonstrated by testing 23 benchmark functions. In addition, based on the IDBO algorithm, we propose an IDBO-CNN classification model, with the help of IDBO, the accuracy of NLOS identification is improved by adjusting the hyperparameters of CNN to be closer to the optimal solution. Experiments conducted on the open-source dataset demonstrate that IDBO-CNN is capable of achieve the desired effect. In comparison to the conventional CNN approach, the F1-score achieved by IDBO-CNN is enhanced by 3.31%, which demonstrates that IDBO-CNN has superior identification accuracy.

show abstract

Section: Related Work a Nlos Identificationmentioning

confidence: 99%

NLOS Identification for UWB Positioning Based on IDBO and Convolutional Neural Networks

Kong

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

UWB Wireless Positioning Method Based on LightGBM

Cui,

Li,

et al. 2024

Wireless Pers Commun

View full text Add to dashboard Cite

Hybrid Quantum Convolutional Neural Networks for UWB Signal Classification

Jeong,

Do,

Hwang

et al. 2023

IEEE Access

View full text Add to dashboard Cite

With the increasing requirements for location-based services for Internet of things (IoT) applications, ultrawideband (UWB) technology provides accurate indoor positioning capabilities. However, indoor environments contain various obstacles leading to significant signal propagation effects. This results in errors in the time-of-arrival-based UWB positioning system. Specifically, a non-line-of-sight (NLOS) signal induces additional distance and position errors owing to the path delay compared to a line-ofsight (LOS) signal. Therefore, UWB signal classification is essential for improving positioning accuracy. Recently, various approaches have successfully classified UWB signals, including machine-learning-based methods such as convolutional neural networks (CNNs) and long short-term memory (LSTM). This study proposes a hybrid quantum CNN (HQCNN) inspired by a CNN for UWB signal classification. HQCNN employs a classical layer before a quantum embedding circuit and variational quantum circuits for the convolutional filter. These structures enable efficient training and implementation. We used UWB channel impulse response data to demonstrate the performance of the proposed algorithm and compared the benchmarks with HQCNN using the evaluation metrics. The results showed that the HQCNN outperformed the others.

show abstract

Cooperative Indoor Localization System Based UWB and Random Forest Algorithm In Complicated Underground NLOS Scenario

Cited by 8 publications

References 17 publications

NLOS Identification for UWB Positioning Based on IDBO and Convolutional Neural Networks

NLOS Identification for UWB Positioning Based on IDBO and Convolutional Neural Networks

UWB Wireless Positioning Method Based on LightGBM

Hybrid Quantum Convolutional Neural Networks for UWB Signal Classification

Contact Info

Product

Resources

About