Passive Detection with a Multi-Rank Beamformer of a Random Signal Common to Two Sensors

Motivated by passive source localization, we derive a generalized likelihood ratio for detecting a Gaussian signal common to two passive sensor arrays, measured in white Gaussian noises of unknown variances. The resulting detector is compared with a related detector that makes no such Gaussian signal assumption. These two detectors are called, respectively, second-order and first-order detectors. In the case where each passive sensor employs a known beamformer, performance is nearly identical. But for more general channel or beamformer models where the received signal is assumed only to lie in a low-dimensional subspace, the second-order detector can outperform the first-order detector.

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Publication Details

Published
2024-10-27
DOI
https://doi.org/10.1109/ieeeconf60004.2024.10942635
Citations
1
Primary Topic
Target Tracking and Data Fusion in Sensor Networks
Type
article
Field-Weighted Citation Impact
0.35

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article

Passive Detection with a Multi-Rank Beamformer of a Random Signal Common to Two Sensors

David Ramírez, Ignacio Santamarı́a, Louis L. Scharf
1 citations
Target Tracking and Data Fusion in Sensor Networks
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article

Passive Detection with a Multi-Rank Beamformer of a Random Signal Common to Two Sensors

David Ramírez, Ignacio Santamarı́a, Louis L. Scharf
article en
1 citations

Abstract

Motivated by passive source localization, we derive a generalized likelihood ratio for detecting a Gaussian signal common to two passive sensor arrays, measured in white Gaussian noises of unknown variances. The resulting detector is compared with a related detector that makes no such Gaussian signal assumption. These two detectors are called, respectively, second-order and first-order detectors. In the case where each passive sensor employs a known beamformer, performance is nearly identical. But for more general channel or beamformer models where the received signal is assumed only to lie in a low-dimensional subspace, the second-order detector can outperform the first-order detector.

Universidad de Cantabria (ES), Universidad Carlos III de Madrid (ES), Colorado State University (US)
European Commission, Instituto de Investigación Sanitaria Gregorio Marañón, Agencia Estatal de Investigación, Office of Naval Research, Air Force Office of Scientific Research
Affordable and clean energy
Openalex Percentile: Top 29%
Target Tracking and Data Fusion in Sensor Networks
0.35
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