超宽带(Ultra Wide Band, UWB)技术因其高精度和强抗干扰能力,在室内定位领域中有着广泛的应用。然而,在复杂的室内环境中,UWB信号易受多径效应和非视距条件的影响,使得定位精度下降。为此,文章提出了一种基于深度学习的UWB室内定位方...超宽带(Ultra Wide Band, UWB)技术因其高精度和强抗干扰能力,在室内定位领域中有着广泛的应用。然而,在复杂的室内环境中,UWB信号易受多径效应和非视距条件的影响,使得定位精度下降。为此,文章提出了一种基于深度学习的UWB室内定位方法。通过引入双向门控循环单元(Bidirectional Gated Recurrent Unit, BiGRU)与Bahdanau注意力机制的结合模型,充分挖掘UWB信号的时序特征和关键信息。BiGRU利用其在时序数据处理中的优势,有效捕捉UWB信号的动态特征,而Bahdanau注意力机制通过动态权重分配,增强模型对关键特征的关注,从而提高定位精度。实验结果表明,文章提出的模型平均定位误差为6.9 cm,相较于传统的循环神经网络(Recurrent Neural Network, RNN)、长短时记忆(Long Short-Term Memory, LSTM)网络和门控循环单元(Gated Recurrent Unit, GRU),误差减少了约29.59%至42.98%。研究结果表明,结合BiGRU与Bahdanau注意力机制的深度学习模型在复杂环境下具有更高的鲁棒性和定位精度。Ultra Wide Band (UWB) technology is widely used in indoor positioning due to its high accuracy and strong anti-interference capability. However, in complex indoor environments, UWB signals are susceptible to multipath effects and non-line-of-sight conditions, which degrade positioning accuracy. To address this issue, this paper proposes a deep learning-based UWB indoor positioning method. By introducing a combined model of the Bidirectional Gated Recurrent Unit and Bahdanau attention mechanism, the method effectively exploits the temporal features and key information of UWB signals. BiGRU leverages its advantages in handling sequential data to capture the dynamic characteristics of UWB signals, while the Bahdanau attention mechanism enhances the model’s focus on critical features through dynamic weight allocation, thus improving positioning accuracy. Experimental results show that the average positioning error of the proposed model is 6.9 cm, which represents a reduction of approximately 29.59% to 42.98% compared to traditional Recurrent Neural Network, Long Short-Term Memory Network, and Gated Recurrent Unit. The results demonstrate that the deep learning model combining BiGRU and the Bahdanau attention mechanism offers higher robustness and positioning accuracy in complex environments.展开更多
文摘超宽带(Ultra Wide Band, UWB)技术因其高精度和强抗干扰能力,在室内定位领域中有着广泛的应用。然而,在复杂的室内环境中,UWB信号易受多径效应和非视距条件的影响,使得定位精度下降。为此,文章提出了一种基于深度学习的UWB室内定位方法。通过引入双向门控循环单元(Bidirectional Gated Recurrent Unit, BiGRU)与Bahdanau注意力机制的结合模型,充分挖掘UWB信号的时序特征和关键信息。BiGRU利用其在时序数据处理中的优势,有效捕捉UWB信号的动态特征,而Bahdanau注意力机制通过动态权重分配,增强模型对关键特征的关注,从而提高定位精度。实验结果表明,文章提出的模型平均定位误差为6.9 cm,相较于传统的循环神经网络(Recurrent Neural Network, RNN)、长短时记忆(Long Short-Term Memory, LSTM)网络和门控循环单元(Gated Recurrent Unit, GRU),误差减少了约29.59%至42.98%。研究结果表明,结合BiGRU与Bahdanau注意力机制的深度学习模型在复杂环境下具有更高的鲁棒性和定位精度。Ultra Wide Band (UWB) technology is widely used in indoor positioning due to its high accuracy and strong anti-interference capability. However, in complex indoor environments, UWB signals are susceptible to multipath effects and non-line-of-sight conditions, which degrade positioning accuracy. To address this issue, this paper proposes a deep learning-based UWB indoor positioning method. By introducing a combined model of the Bidirectional Gated Recurrent Unit and Bahdanau attention mechanism, the method effectively exploits the temporal features and key information of UWB signals. BiGRU leverages its advantages in handling sequential data to capture the dynamic characteristics of UWB signals, while the Bahdanau attention mechanism enhances the model’s focus on critical features through dynamic weight allocation, thus improving positioning accuracy. Experimental results show that the average positioning error of the proposed model is 6.9 cm, which represents a reduction of approximately 29.59% to 42.98% compared to traditional Recurrent Neural Network, Long Short-Term Memory Network, and Gated Recurrent Unit. The results demonstrate that the deep learning model combining BiGRU and the Bahdanau attention mechanism offers higher robustness and positioning accuracy in complex environments.
