Detection with multimodal dependent data using low-dimensional random projections

Thakshila Wimalajeewa, Pramod Kumar Varshney

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

3 Scopus citations

Abstract

Performing likelihood ratio based detection with high dimensional multimodal data is a challenging problem since the computation of the joint probability density functions (pdfs) in the presence of intermodal dependence is difficult. While some computationally expensive approaches have been proposed for dependent multimodal data fusion (e.g., based on copula theory), a commonly used tractable approach is to compute the joint pdf as the product of marginal pdfs ignoring dependence. However, this method leads to poor performance when the data is strongly dependent. In this paper, we consider the problem of detection when dependence among multimodal data is modeled in a compressed domain where compression is obtained using low dimensional random projections. We employ a Gaussian approximation while modeling inter-modal dependence in the compressed domain which is computationally more efficient. We show that, under certain conditions, detection with multimodal dependent data in the compressed domain with a small number of compressed measurements yields enhanced performance compared to detection with high dimensional data via either the product approach or other suboptimal fusion approaches proposed in the literature.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4621-4625
Number of pages5
ISBN (Electronic)9781509041176
DOIs
StatePublished - Jun 16 2017
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: Mar 5 2017Mar 9 2017

Other

Other2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
CountryUnited States
CityNew Orleans
Period3/5/173/9/17

Keywords

  • Compressive sensing
  • copula theory
  • intermodal dependence
  • likelihood ratio based detection
  • multimodal data

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Wimalajeewa, T., & Varshney, P. K. (2017). Detection with multimodal dependent data using low-dimensional random projections. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings (pp. 4621-4625). [7953032] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2017.7953032