Ultrasensitive Open-Ended Coaxial Cable-Based Microwave Resonator Learns to Sense Impacts


This article presents an ultrasensitive open-ended hollow coaxial cable resonator (OE-HCCR) that was trained to detect impact events that occurred in the surrounding space through machine learning (ML) techniques. First, inspired by the well-known mass-based accelerometer, for the first time, we propose an OE-HCCR-based accelerometer with the addition of an inertial mass block as the element responsive to acceleration. The OE-HCCR is formed by a metal shorting post welded at the RF input end and the open end of the cable. The instantaneous displacements of the mass block mounted at the endface of the cable due to accelerations lead to variations of the gap distance between the mass block and the endface of the coaxial line, which can be accurately metered by the coaxial resonating structure. Impact events that occur in the surrounding space of the OE-HCCR result in vibrations transmitted to the OE-HCCR system and thereby measured by the OE-HCCR. Second, we demonstrated that employing ML algorithms to analyze the transient responses from the OE-HCCR makes unambiguous mappings between the transient responses of the OE-HCCR and the sources of vibrations possible, due to the ultrahigh sensitivity of the OE-HCCR and the spatial anisotropy of matter in the surrounding space. The preliminary results demonstrate the feasibility of using ML-assisted OE-HCCR system for locating impact events and recognizing different impactor implements that caused the impact events.


Electrical and Computer Engineering

Research Center/Lab(s)

Intelligent Systems Center


Army Research Laboratory, Grant W911NF-14-2-0034

Keywords and Phrases

Accelerometer; Coaxial Cable Device; Displacement; Machine Learning (ML); Microwave Resonator; Open-Ended Hollow Coaxial Cable Resonator (OE-HCCR); Support Vector Machine (SVM)

International Standard Serial Number (ISSN)

0018-9456; 1557-9662

Document Type

Article - Journal

Document Version


File Type





© 2020 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

21 Sep 2020