A Deep Learning Assisted Ground Penetrating Radar Localization Method

NI Zhikang; YE Shengbo; SHI Cheng; PAN Jun; ZHENG Zhijie; FANG Guangyou

doi:10.11999/JEIT211072

Volume 44 Issue 4

Apr. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(4): 1265-1273

NI Zhikang, YE Shengbo, SHI Cheng, PAN Jun, ZHENG Zhijie, FANG Guangyou. A Deep Learning Assisted Ground Penetrating Radar Localization Method[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1265-1273. doi: 10.11999/JEIT211072

Citation:

NI Zhikang, YE Shengbo, SHI Cheng, PAN Jun, ZHENG Zhijie, FANG Guangyou. A Deep Learning Assisted Ground Penetrating Radar Localization Method[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1265-1273. doi: 10.11999/JEIT211072

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A Deep Learning Assisted Ground Penetrating Radar Localization Method

doi: 10.11999/JEIT211072

NI Zhikang^{1, 2, 3},
YE Shengbo^{1, 2},
SHI Cheng^{1, 2, 3},
PAN Jun^{1, 2, 3},
ZHENG Zhijie^{1, 2, 3},
FANG Guangyou^{1, 2, 3
,
,}

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2.
Key Laboratory of Electromagnetic Radiation and Sensing Technology, Chinese Academy of Sciences, Beijing 100094, China
3.
School of Electronic, Electrical and Communication Engineering, University of the Chinese Academy of Sciences, Beijing 100049, China

Funds: The National Natural Science Foundation of China (61827803)

Received Date: 2021-09-30
Accepted Date: 2021-12-28
Rev Recd Date: 2021-12-14

Available Online: 2022-01-13

Publish Date: 2022-04-18

Abstract

Abstract

Under harsh conditions, such as rain, snow, dust, strong light, and dark night, the vision and laser sensors commonly used in autonomous driving solutions may fail because they can not accurately sense the external environment. Therefore, a method for vehicle localization using underground target features sensed by deep learning assisted ground penetrating radar is proposed in this paper. The proposed method is divided into two phases: offline mapping phase and online localization phase. In the offline mapping phase, the ground penetrating radar is used to collect the echo data from the underground targets first, then the Deep Convolutional Neural Network (DCNN) is utilized to extract the target features from the collected echo data, and the extracted target features are saved with the current geographic location information to form a fingerprint map of underground target features. In the localization phase, the DCNN is used to extract the target features from the current echo data collected by the ground penetrating radar first, and then the target feature most similar to the current extracted target feature in the fingerprint map of underground target features is retrieved based on the particle swarm optimization method, and the geographic location information of the retrieved feature is marked as the vehicle localization result by the ground penetrating radar. Finally, the Kalman filter is used to fuse the ground penetrating radar localization result and the mileage information measured by the ranging wheel to obtain a high-precision localization result. The localization performance of the proposed localization method is tested on the experimental scenario with rich underground targets and the actual urban road scenario. The experimental results show that, compared with the single raw data-based ground penetrating radar localization method, the deep learning assisted ground penetrating radar localization method can avoid directly calculating the similarity between the raw radar data, reduce the amount of data computation and data transmission, and has the real-time localization capability. At the same time, the fingerprint map of underground target features is robust to the changes of the raw radar data, so the average localization error of the proposed method is reduced by about 70%. The deep learning assisted ground penetrating radar localization method can be used as a supplement to the detection and localization method of autonomous vehicles in harsh environments in the future.
- Ground penetrating radar,
- Convolutional Neural Network (CNN),
- Map-based localization,
- Kalman filter,
- Particle swarm optimization

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References(18)

References

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