Gradient of error probability of m-ary hypothesis testing problems under multivariate gaussian noise

Authors

Minoh Jeong, College of Science and Engineering
Alex Dytso, New Jersey Institute of Technology
Martina Cardone, College of Science and Engineering

Document Type

Article

Publication Date

1-1-2020

Abstract

This letter considers an M-ary hypothesis testing problem on an n-dimensional random vector perturbed by the addition of Gaussian noise. A novel expression for the gradient of the error probability, with respect to the covariance matrix of the noise, is derived and shown to be a function of the cross-covariance matrix between the noise matrix (i.e., the matrix obtained by multiplying the noise vector by its transpose) and Bernoulli random variables associated with the correctness event.

Identifier

85105584206 (Scopus)

Publication Title

IEEE Signal Processing Letters

External Full Text Location

https://doi.org/10.1109/LSP.2020.3031487

e-ISSN

15582361

ISSN

10709908

First Page

1909

Last Page

1913

Volume

27

Grant

1849757

Fund Ref

National Science Foundation

Recommended Citation

Jeong, Minoh; Dytso, Alex; and Cardone, Martina, "Gradient of error probability of m-ary hypothesis testing problems under multivariate gaussian noise" (2020). Faculty Publications. 5669.
https://digitalcommons.njit.edu/fac_pubs/5669