Alternative navigation technology to global navigation satellite systems (GNSSs) is required for unmanned ground vehicles (UGVs) in multipath environments (such as urban areas). In urban areas, long-term evolution (LTE) signals can be received ubiquitously at high power without any additional infrastructure. We present a machine learning approach to estimate the range between the LTE base station and UGV based on the LTE channel impulse response (CIR). The CIR, which includes information of signal attenuation from the channel, was extracted from the LTE physical layer using a software-defined radio (SDR). We designed a convolutional neural network (CNN) that estimates ranges with the CIR as input. The proposed method demonstrated better ranging performance than a received signal strength indicator (RSSI)-based method during our field test.
|Title of host publication||2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2020 Nov 1|
|Event||2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020 - Seoul, Korea, Republic of|
Duration: 2020 Nov 1 → 2020 Nov 3
|Name||2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020|
|Conference||2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020|
|Country/Territory||Korea, Republic of|
|Period||20/11/1 → 20/11/3|
Bibliographical noteFunding Information:
This research was supported by the Ministry of Science and ICT (MSIT), Korea, under the High- Potential Individuals Global Training Program (2020- 0-01531) supervised by the Institute for Information & Communications Technology Planning & Evaluation (IITP).
© 2020 IEEE.
Copyright 2020 Elsevier B.V., All rights reserved.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Electrical and Electronic Engineering
- Media Technology