Fast Direction-of-arrival Estimation of Multiple Targets Using Deep Learning and Sparse Arrays

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this work, we focus on improving the Direction-of-Arrival (DoA) estimation of multiple targets/sources from a small number of snapshots. Estimation via the sample covariance matrix is known to perform poorly, since the true manifold structure is not revealed for a small number of samples. First, we explicitly model the sample covariance matrix that is used for the DoA estimation as a noisy version of the true one. Next, we employ a stacked denoising autoencoder (DAE) that predicts a statistically richer version of the sampled matrix that is subsequently used for the DoA estimation. Moreover, we consider a limited number of sensors (comparable to the number of sources) in a non-uniform linear configuration and introduce an end-to-end hybrid DoA prediction-estimation scheme. Results demonstrate significant improvement compared to the conventional approach.

Original languageEnglish
Title of host publicationICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
Pages4632-4636
Number of pages5
ISBN (Electronic)978-1-5090-6631-5
DOIs
Publication statusPublished - 14 May 2020
Event45th IEEE International Conference on Acoustics, Speech and Signal Processing 2020 - Barcelona, Spain
Duration: 4 May 20208 May 2020
https://2020.ieeeicassp.org/

Publication series

NameIEEE International Conference on Acoustics, Speech and Signal Processing
ISSN (Electronic)2379-190X

Conference

Conference45th IEEE International Conference on Acoustics, Speech and Signal Processing 2020
Abbreviated titleICASSP 2020
CountrySpain
CityBarcelona
Period4/05/208/05/20
Internet address

Keywords

  • Direction-of-arrival DoA estimation
  • deep learning
  • denoising autoencoder DAE
  • maximum interelement spacing constraint MISC arrays
  • sparse arrays

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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