TY - JOUR
T1 - Micro-Doppler Signal Features of Idling Vehicle Vibrations
T2 - Dependence on Gear Engagements and Occupancy
AU - Narayanan, Ram M.
AU - Simone, Benjamin D.
AU - Watson, Daniel K.
AU - Reichard, Karl M.
AU - Gallagher, Kyle A.
N1 - Publisher Copyright:
© 2025 by the authors.
PY - 2025/9
Y1 - 2025/9
N2 - This study investigates the use of a custom-built 10 GHz continuous wave micro-Doppler radar system to analyze external vibrations of idling vehicles under various conditions. Scenarios included different gear engagements with one occupant and parked gear with up to four occupants. Motivated by security concerns, such as the threat posed by idling vehicles with multiple occupants, the research explores how micro-Doppler signatures can indicate vehicle readiness to move. Experiments focused on a mid-size SUV, with similar trends seen in other vehicles. Radar data were compared to in situ accelerometer measurements, confirming that the radar system can detect subtle frequency changes, especially during gear shifts. The system’s sensitivity enables it to distinguish variations tied to gear state and passenger load. Extracted features like frequency and magnitude show strong potential for use in machine learning models, offering a non-invasive, remote sensing method for reliably identifying vehicle operational states and occupancy levels in security or monitoring contexts. Spectrogram and PSD analyses reveal consistent tonal vibrations around 30 Hz, tied to engine activity, with harmonics at 60 Hz and 90 Hz. Gear shifts produce impulse signatures primarily below 20 Hz, and transient data show distinct peaks at 50, 80, and 100 Hz. Key features at 23 Hz and 45 Hz effectively indicate engine and gear states. Radar and accelerometer data align well, supporting the potential for remote sensing and machine learning-based classification.
AB - This study investigates the use of a custom-built 10 GHz continuous wave micro-Doppler radar system to analyze external vibrations of idling vehicles under various conditions. Scenarios included different gear engagements with one occupant and parked gear with up to four occupants. Motivated by security concerns, such as the threat posed by idling vehicles with multiple occupants, the research explores how micro-Doppler signatures can indicate vehicle readiness to move. Experiments focused on a mid-size SUV, with similar trends seen in other vehicles. Radar data were compared to in situ accelerometer measurements, confirming that the radar system can detect subtle frequency changes, especially during gear shifts. The system’s sensitivity enables it to distinguish variations tied to gear state and passenger load. Extracted features like frequency and magnitude show strong potential for use in machine learning models, offering a non-invasive, remote sensing method for reliably identifying vehicle operational states and occupancy levels in security or monitoring contexts. Spectrogram and PSD analyses reveal consistent tonal vibrations around 30 Hz, tied to engine activity, with harmonics at 60 Hz and 90 Hz. Gear shifts produce impulse signatures primarily below 20 Hz, and transient data show distinct peaks at 50, 80, and 100 Hz. Key features at 23 Hz and 45 Hz effectively indicate engine and gear states. Radar and accelerometer data align well, supporting the potential for remote sensing and machine learning-based classification.
UR - https://www.scopus.com/pages/publications/105017402139
UR - https://www.scopus.com/pages/publications/105017402139#tab=citedBy
U2 - 10.3390/signals6030035
DO - 10.3390/signals6030035
M3 - Article
AN - SCOPUS:105017402139
SN - 2624-6120
VL - 6
JO - Signals
JF - Signals
IS - 3
M1 - 35
ER -