C-AVDI: Compressive Measurement-Based Acoustic Vehicle Detection and Identification

Billy Dawton, Shigemi Ishida, Yutaka Arakawa: C-AVDI: Compressive Measurement-Based Acoustic Vehicle Detection and Identification. In: IEEE Access, 9 , pp. 159457-159474, 2021, ISSN: 2169-3536.

Abstract

As society grows ever more interconnected, the need for sophisticated signal processing and data analysis techniques becomes increasingly apparent. This is particularly true in the field of intelligent transportation systems (ITSs), where various sensing applications generate data at an exponential rate. In this paper, we present C-AVDI, a compressive measurement-based acoustic vehicle detection and identification architecture capable of extracting information from vehicle audio signals while sampling at sub-Nyquist rates. In addition, we further reduce the overall complexity by performing any necessary signal filtering during the acquisition process, removing the need for a separate filtering stage in the system’s front-end. Our results obtained from data collected under a range of weather conditions present an accuracy of 80% with a back-end analog-to-digital converter (ADC) sample rate of 3kHz, with initial results from a microcontroller (MCU) implementation of our proposed system presenting an accuracy of 72%.

BibTeX (Download)

@article{9632588,
title = {C-AVDI: Compressive Measurement-Based Acoustic Vehicle Detection and Identification},
author = {Billy Dawton and Shigemi Ishida and Yutaka Arakawa},
doi = {10.1109/ACCESS.2021.3132061},
issn = {2169-3536},
year  = {2021},
date = {2021-12-01},
urldate = {2021-01-01},
journal = {IEEE Access},
volume = {9},
pages = {159457-159474},
abstract = {As society grows ever more interconnected, the need for sophisticated signal processing and data analysis techniques becomes increasingly apparent. This is particularly true in the field of intelligent transportation systems (ITSs), where various sensing applications generate data at an exponential rate. In this paper, we present C-AVDI, a compressive measurement-based acoustic vehicle detection and identification architecture capable of extracting information from vehicle audio signals while sampling at sub-Nyquist rates. In addition, we further reduce the overall complexity by performing any necessary signal filtering during the acquisition process, removing the need for a separate filtering stage in the system’s front-end. Our results obtained from data collected under a range of weather conditions present an accuracy of 80% with a back-end analog-to-digital converter (ADC) sample rate of 3kHz, with initial results from a microcontroller (MCU) implementation of our proposed system presenting an accuracy of 72%.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}