Locate UWB Smart Keys

Park, JiWoong; Choi, Hong-Beom; Ko, Young-Bae; Lim, Kyung Soo

doi:10.1145/3446382.3448884

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…However, the task is challenging and computationally complex, and a first approach is described in (Jiang et al 2020). While our feature set has proven robust in practice, more complex features for multi-path environments, and without direct NLOS determination, are listed by Park et al (2021). For a broader overview of UWB-based (indoor) localization, we refer the reader to the work by Shi and Ming (2016).…”

Section: Uwb Key Localizationmentioning

confidence: 99%

Domain Reconstruction for UWB Car Key Localization Using Generative Adversarial Networks

Kuvshinov

Knobloch

Kulzer

et al. 2022

AAAI

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We consider the car key localization task using ultra-wideband (UWB) signal measurements. Given labeled data for a certain car, we train a deep classifier to make the prediction about the new points. However, due to the differences in car models and possible environmental effects that might alter the signal propagation, data collection requires considerable effort for each car. In particular, we consider a situation where the data for the new car is collected only in one environment, so we have to utilize the measurements in other environments from a different car. We propose a framework based on generative adversarial networks (GANs) to generate missing parts of the data and train the classifier on it, mitigating the necessity to collect the real data. We show that the model trained on the synthetic data performs better than the baseline trained on the collected measurements only. Furthermore, our model closes the gap to the level of performance achieved when we would have the information about the new car in multiple environments by 35%.

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Section: Uwb Key Localizationmentioning

confidence: 99%

Domain Reconstruction for UWB Car Key Localization Using Generative Adversarial Networks

Kuvshinov

Knobloch

Kulzer

et al. 2022

AAAI

View full text Add to dashboard Cite

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NN-LCS: Neural Network and Linear Coordinate Solver Fusion Method for UWB Localization in Car Keyless Entry System

Zheng

Yan

Sun

et al. 2023

Sensors

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Nowadays, ultra-wideband (UWB) technology is becoming a new approach to localize keyfobs in the car keyless entry system (KES), because it provides precise localization and secure communication. However, for vehicles the distance ranging suffers from great errors because of none-line-of-sight (NLOS) which is raised by the car. Regarding the NLOS problem, efforts have been made to mitigate the point-to-point ranging error or to estimate the tag coordinate by neural networks. However, it still suffers from some problems such as low accuracy, overfitting, or a large number of parameters. In order to address these problems, we propose a fusion method of a neural network and linear coordinate solver (NN-LCS). We use two FC layers to extract the distance feature and received signal strength (RSS) feature, respectively, and a multi-layer perceptron (MLP) to estimate the distances with the fusion of these two features. We prove that the least square method which supports error loss backpropagation in the neural network is feasible for distance correcting learning. Therefore, our model is end-to-end and directly outputs the localization results. The results show that the proposed method is high-accuracy and with small model size which could be easily deployed on embedded devices with low computing ability.

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Locate UWB Smart Keys

Cited by 2 publications

References 5 publications

Domain Reconstruction for UWB Car Key Localization Using Generative Adversarial Networks

Domain Reconstruction for UWB Car Key Localization Using Generative Adversarial Networks

NN-LCS: Neural Network and Linear Coordinate Solver Fusion Method for UWB Localization in Car Keyless Entry System

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