Infrared and Laser Engineering, Volume. 52, Issue 12, 20230183(2023)
Deep learning-based impact mitigation method for UWB NLOS propagation
Fig. 5. Two basic types of modules for NLO-ResNet: NLRBU-Iden (a) and NLRBU-Conv (b)
Fig. 7. Typical schematic of CIR data under LOS and NLOS propagation conditions
Fig. 8. Schematic of the variation of the network performance with the dimensionality of the input CIR data
Fig. 9. Comparison of the basic modules of CNN (a), ResNet (b), and NLO-ResNet (c)
Fig. 11. Comparison chart of predicted range error and actual range error
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Wanqing Liu, Guo Wei, Chunfeng Gao, Xudong Yu, Zhongqi Tan, Chengzhong Zhang, Chengzhi Hou, Xu Zhu. Deep learning-based impact mitigation method for UWB NLOS propagation[J]. Infrared and Laser Engineering, 2023, 52(12): 20230183
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Received: Apr. 10, 2023
Accepted: --
Published Online: Feb. 23, 2024
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