Objective  With the continuous development of intelligent technologies and devices, precise positioning technology in the military field is becoming increasingly widespread, and its application scenarios cover both outdoor and indoor environments. Global Navigation Satellite System (GNSS) positioning technology is commonly used for its high positioning accuracy and rich information provision in outdoor environments; However, its positioning accuracy in indoor environments is significantly reduced due to the obstruction of walls and other obstacles. Ultra-Wideband (UWB) technology shows obvious advantages with its high positioning accuracy, firm spatial and temporal resolution, fast transmission rate, and low cost. These advantages make UWB technology particularly suitable for indoor high-precision positioning. In the indoor environment, various obstacles block the propagation channel between the base station and the tag of the UWB system, due to the Non-Line-Of-Sight (NLOS) phenomenon of UWB signals, the positioning accuracy of UWB systems is significantly reduced. Therefore, it is necessary to research the impact mitigation method for UWB NLOS propagation.

  Methods  A deep neural network based on deep learning techniques is proposed for UWB NLOS propagation impact mitigation. This deep neural network takes the initial channel impulse response (CIR) of the UWB device as input and the ranging error of the UWB device as output. The experimental analysis shows that the characteristics of CIR data are significantly different under LOS and NLOS propagation conditions (Fig.7), which provides a solid theoretical basis for establishing the mapping relationship between CIR and ranging error using deep learning methods. Meanwhile, the network performance is related to the dimensionality of the input CIR data. The network performance is best when the input CIR data is 128 dimensions (Fig.8). When the input of the deep neural network is 128-dimensional data, too long input will lead to the structural design of the network becoming difficult. And the number of network layers is too small, the network performance can not meet the requirements to achieve good NLOS propagation impact mitigation effect; After the number of network layers increases to a certain degree, the network performance will decrease with the increase of the number of layers. For this reason, the ResNet network is selected in this paper, which enables the gradient to flow effectively to the early layers near the input layer by introducing residual connections in the deep neural network, thus improving the network performance with the increase of layers. At the same time, CIR data, as a time-series signal, correlates its data points. The global features of CIR data must be considered, while local module such as convolution can only extract local features. For this reason, this paper introduces the Non-local module, which can capture the long-distance dependence between locations and extract global information. In summary, the proposed deep neural network is constructed by inserting the Non-local module into the ResNet network's basic module while considering the CIR data's features, and named the deep neural network as NLO-ResNet.

  Results and Discussions   In order to evaluate the NLOS propagation impact mitigation performance of the proposed deep neural network, four networks were selected for performance comparison. Four networks include two machine learning-based networks, SVM and MLP, and two deep learning-based networks, CNN and ResNet. Experimental results (Tab.1) show that, due to the increase in the number of layers of the network and the change in the input data, the performance of the deep learning-based network is generally better than that of the machine learning-based network; Among the deep learning-based networks, the CNN network has the worst performance, the ResNet network improves with the increase of the number of layers due to the introduction of residual connections, and the NLO-ResNet network has the best performance, which has the most comprehensive feature extraction of the input CIR data. The mean absolute error (MAE) is reduced by 12.2% compared to the CNN-based network and 4.8% compared to the ResNet-based network, and the learning process of this network converges quickly (Fig.10), and the predicted range error of this network is very close to the actual range error (Fig.11).

  Conclusions  To improve the accuracy of UWB systems under NLOS propagation conditions, a deep learning-based NLOS propagation impact mitigation method is proposed, which constitutes a deep neural network by inserting a Non-local module into the basic module of the ResNet network. The method can reduce the MAE of the original data from 0.1242 m to 0.0681 m. The research provides technical support for indoor high-precision positioning in the military field. The related results can be applied in the autonomous takeoff and landing of military UAVs, and indoor positioning of military robots.