Vehicle detection and classification using LTE-CommSense

Sardar, Santu and Mishra, Amit K and Khan, Mohammed Zafar Ali (2019) Vehicle detection and classification using LTE-CommSense. IET Radar, Sonar & Navigation, 13 (5). pp. 850-857. ISSN 1751-8784

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Abstract

The authors demonstrated a vehicle detection and classification method based on long-term evolution (LTE) communication infrastructure-based environment-sensing instrument, termed as LTE-CommSense by the authors. This technology is a novel passive sensing system which focuses on the reference signals embedded in the sub-frames of LTE resource grid. It compares the received signal with the expected reference signal, extracts the evaluated channel state information (CSI) and analyses it to estimate the change in the environment. For vehicle detection and subsequent classification, authors’ setup is similar to a passive radar in forward scattering radar (FSR) mode. Instead of performing the radio frequency (RF) signals directly, the authors take advantage of the processing that happens in a LTE receiver user equipment (UE). The authors tap into the channel estimation and equalisation block and extract the CSI value. CSI value reflects the property of the communication channel between communication base station (eNodeB) and UE. The authors use CSI values for with vehicle and without vehicle case in outdoor open road environment. Being a receiver-only system, there is no need for any transmission and related regulations. Therefore, this system is low cost, power-efficient and difficult to detect. Also, most of its processing will be done by the existing LTE communication receiver (UE). Here, the authors establish authors’ claim by analysing field-collected data. Live LTE downlink (DL) signal is captured using modelled LTE UE using software defined radio (SDR). The detection analysis and classification performance show promising results and ascertain that LTE-CommSense is capable of detection and classification of different types of vehicles in outdoor road environment.

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IITH Creators:
IITH CreatorsORCiD
Khan, Mohammed Zafar AliUNSPECIFIED
Item Type: Article
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: Team Library
Date Deposited: 13 May 2019 07:49
Last Modified: 13 May 2019 07:49
URI: http://raiith.iith.ac.in/id/eprint/5138
Publisher URL: http://doi.org/10.1049/iet-rsn.2018.5231
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