Prof. Li Hao Profile

Prof. Li Hao

at Southwest Jiaotong Univ.

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Publications (2)

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Proceedings Article | 11 October 2023 Paper

Dataset collecting for UWB NLOS identification based on machine learning

Yifang Zhang, Yiming Liu, Li Hao

Proceedings Volume 12800, 1280030 (2023) https://doi.org/10.1117/12.3003998

KEYWORDS: Education and training, Non-line-of-sight propagation, Machine learning, Data acquisition, Ranging, Telecommunications, Lawrencium, Signal detection, Lithium, Binary data

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The Non-Line-of-Sight (NLOS) environment significantly reduces the accuracy of Ultra-Wideband (UWB) ranging and positioning in the UWB positioning system based on time of arrival. The mainstream method is to identify the Line-of-Sight (LOS) and NLOS environment through machine learning and make corresponding corrections to improve the accuracy of the positioning system. However, the existing research and application of machine learning methods do not fully consider the situation that the test set exceeds the training set, which is inconsistent with the actual scenario. Firstly, in this paper, we show through experimental results that when the test set and the training set are independent, different training set acquisition methods will directly affect the accuracy of machine learning. The denser the training set, the higher the precision and the greater the workload. Therefore, aiming at how to choose the training set collection scheme, we put forward an evaluation index to determine the best training set collection scheme. Commonly used machine learning algorithms, such as Random Forest (RF), and XGBoost, are used here to explore the parameter configuration of this index. The research results show that when using machine learning method to identify ultra-wideband NLOS scenes, the acquisition step size of training set is 1.6 meters, which can make the identification accuracy high and the acquisition workload small.

Proceedings Article | 9 May 2022 Paper

Radio frequency fingerprint recognition based on deep transfer learning

Yuanhui Wu, Li Hao

Proceedings Volume 12252, 122520K (2022) https://doi.org/10.1117/12.2640233

KEYWORDS: Neural networks, Fingerprint recognition, Transmitters, Convolutional neural networks, Convolution, Network architectures

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Radio frequency (RF) fingerprint refers to the inevitable subtle defects in the hardware circuit of radio transmitter, which are reflected in the signal waveform transmitted by it. Therefore, the wireless device can be uniquely identified by analyzing the received signal waveform. Due to the advantages of multidimensional mapping of convolutional neural network (CNN), it can automatically extract signal features and transmitter nonlinear features, which can not be extracted by traditional low-dimensional algorithms. Therefore, it is very important to design a deep neural network with reasonable structure and high recognition accuracy. Taking the in phase and quadrature (I & Q) time domain signals of eight radiation sources of the same model as the input samples, the fine features of IQ time domain signals are extracted by designing six deep learning radiation source individual recognition algorithms with different structures to realize the identification of RF fingerprint. Besides, the change of wireless channel probably weakens the identification robustness of RF fingerprint. Thus, a deep transfer learning algorithm is proposed to cross train and test the I & Q data collected at different times, which solves the problem of poor robustness.

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