Learned Integrated Sensing Pipeline: Reconfigurable Metasurface Transceivers as Trainable Physical Layer in an Artificial Neural Network.

Published

Journal Article

The rapid proliferation of intelligent systems (e.g., fully autonomous vehicles) in today's society relies on sensors with low latency and computational effort. Yet current sensing systems ignore most available a priori knowledge, notably in the design of the hardware level, such that they fail to extract as much task-relevant information per measurement as possible. Here, a "learned integrated sensing pipeline" (LISP), including in an end-to-end fashion both physical and processing layers, is shown to enable joint learning of optimal measurement strategies and a matching processing algorithm, making use of a priori knowledge on task, scene, and measurement constraints. Numerical results demonstrate accuracy improvements around 15% for object recognition tasks with limited numbers of measurements, using dynamic metasurface apertures capable of transceiving programmable microwave patterns. Moreover, it is concluded that the optimal learned microwave patterns are nonintuitive, underlining the importance of the LISP paradigm in current sensorization trends.

Full Text

Duke Authors

Cited Authors

  • Del Hougne, P; Imani, MF; Diebold, AV; Horstmeyer, R; Smith, DR

Published Date

  • February 2020

Published In

Volume / Issue

  • 7 / 3

Start / End Page

  • 1901913 -

PubMed ID

  • 32042558

Pubmed Central ID

  • 32042558

Electronic International Standard Serial Number (EISSN)

  • 2198-3844

International Standard Serial Number (ISSN)

  • 2198-3844

Digital Object Identifier (DOI)

  • 10.1002/advs.201901913

Language

  • eng