Sensing Method for Two-Target Detection in Time-Constrained Vector Gaussian Channel

Fuente: arXiv
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Main Authors: Fahad, Muhammad, Fuhrmann, Daniel R.
Format: Preprint
Published: 2022
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author Fahad, Muhammad
Fuhrmann, Daniel R.
author_facet Fahad, Muhammad
Fuhrmann, Daniel R.
contents This paper considers a vector Gaussian channel of fixed identity covariance matrix and binary input signalling as the mean of it. A linear transformation is performed on the vector input signal. The objective is to find the optimal scaling matrix, under the total time constraint, that would: i) maximize the mutual information between the input and output random vectors, ii) maximize the MAP detection. It was found that the two metrics lead to different optimal solutions for our experimental design problem. We have used the Monte Carlo method for our computational work.
format Preprint
id arxiv_https___arxiv_org_abs_2202_02478
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Sensing Method for Two-Target Detection in Time-Constrained Vector Gaussian Channel
Fahad, Muhammad
Fuhrmann, Daniel R.
Information Theory
Signal Processing
This paper considers a vector Gaussian channel of fixed identity covariance matrix and binary input signalling as the mean of it. A linear transformation is performed on the vector input signal. The objective is to find the optimal scaling matrix, under the total time constraint, that would: i) maximize the mutual information between the input and output random vectors, ii) maximize the MAP detection. It was found that the two metrics lead to different optimal solutions for our experimental design problem. We have used the Monte Carlo method for our computational work.
title Sensing Method for Two-Target Detection in Time-Constrained Vector Gaussian Channel
topic Information Theory
Signal Processing
url https://arxiv.org/abs/2202.02478