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NLOS signal detection and correction for smartphone using convolutional neural network and variational mode decomposition in urban environment

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Abstract

With the rapid development of mobile communications, location-based service for smartphones has received extensive attention, becoming an indispensable part of smart cities. However, the positioning effect of smartphones in urban environments is not ideal, and the non-line-of-sight (NLOS) signal is one of the main limiting factors. To weaken NLOS signal errors, we present a method to detect and correct NLOS signals. An NLOS signal detection model based on a convolutional neural network is constructed using the original observations of smartphones. In an experimental environment, the detection accuracy of the model reaches more than 95%. The detected NLOS signals are decomposed using the variational mode decomposition method to eliminate the NLOS part and improve the data quality. To evaluate the effect of the proposed method, we conduct static and dynamic experiments in an urban environment. In the static experiment, the positioning accuracy of the processed data is improved by an average of 15% compared with the unprocessed data, and the stability of the plane results is also significantly enhanced. In the dynamic test, the dataset processed using the proposed method achieved a positioning accuracy of 3 m in an environment with severe signal occlusion, and the accuracy is improved by more than 20%. Although the accuracy in heavily occluded areas is still far from that in an open environment while only relying on the global navigation satellite system signal of smartphones, this method can still provide new ideas for smart city research and construction.

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Data availability

The datasets supporting this research are collected by the experimental device. If needy, please contact us by email, lq_0328@seu.edu.cn.

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Acknowledgements

This work was supported by the Fundamental Research Funds for the Central Universities (Grant No. 2242021R41134), the National Natural Science Foundation of China (Grant No. 41974030). The authors gratefully thank the editors and reviewers for their comments and suggestions.

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Authors and Affiliations

  1. School of Transportation, Southeast University, Nanjing, 211189, China

    Qi Liu, Chengfa Gao, Rui Shang, Zihan Peng, Ruicheng Zhang & Lu Gan

  2. School of Instrument Science and Engineering, Southeast University, Nanjing, 211189, China

    Wang Gao

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  1. Qi Liu
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  2. Chengfa Gao
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  3. Rui Shang
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  4. Zihan Peng
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Correspondence to Chengfa Gao.

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Liu, Q., Gao, C., Shang, R. et al. NLOS signal detection and correction for smartphone using convolutional neural network and variational mode decomposition in urban environment. GPS Solut 27, 31 (2023). https://doi.org/10.1007/s10291-022-01369-2

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