Vibrational Radar Backscatter Communication using Resonant Transponding Surfaces

Vibrational radar backscatter communication (VRBC) performs millimeter-wave radar vibrometry to receive message signals from digitally modulated vibrating surfaces. For anything-to-vehicle communication, VRBC is a scalable, low-latency approach which leverages existing automotive radars. The number...

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Bibliographic Details
Main Authors: Centers, Jessica, Krolik, Jeffrey
Format: Conference Proceeding
Language:English
Subjects:
backscatter communication
Codes
intersymbol interference
Maximum likelihood detection
Millimeter wave radar
Symbols
Transponders
vibrometry
Visualization
Viterbi algorithm
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Summary:Vibrational radar backscatter communication (VRBC) performs millimeter-wave radar vibrometry to receive message signals from digitally modulated vibrating surfaces. For anything-to-vehicle communication, VRBC is a scalable, low-latency approach which leverages existing automotive radars. The number of VRBC channels for a given vehicle depends only on the ability of the radar to spatially isolate the various modulating surfaces as determined by its range and bearing resolution. For an increased signal-to-noise ratio (SNR), larger displacement caused by resonant vibrating surfaces is desirable. However, resonances also induce intersymbol interference (ISI), which introduces more errors during message decoding. This paper addresses the problem of ISI in VRBC by modeling effects of this resonant behavior and performing Viterbi sequence estimation. We model displacements as a linear time-invariant (LTI) filter, dependent on the material properties and boundary conditions of the surface, acting on the vibrational excitation. Vibrational displacement is then mapped to the slow-time phase trajectory of the radar return at the range and bearing of the surface. To mitigate ISI, we implement sequence detection via the Viterbi algorithm for M -ary signaling, which can account for the use of line codes that limit the number of potential interfering symbols. Results demonstrating the benefits of sequence detection are compared to symbol-by-symbol maximum likelihood detection.
ISSN:2151-870X
DOI:10.1109/SAM53842.2022.9827869