Passive Detection of Rank-One Gaussian Signals for Known Channel Subspaces and Arbitrary Noise

This paper addresses the passive detection of a common signal in two multi-sensor arrays. For this problem, we derive a detector based on likelihood theory for the case of one-antenna transmitters, independent Gaussian noises with arbitrary spatial structure, Gaussian signals, and known channel subs...

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Bibliographic Details
Published in:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 1 - 5
Main Authors: Ramirez, D., Santamaria, I., Scharf, L. L.
Format: Conference Proceeding
Language:English
Published: IEEE 04.06.2023
Subjects:
Channel estimation
Detectors
Generalized likelihood ratio (GLR)
hypothesis test
Maximum likelihood detection
Maximum likelihood estimation
multi-sensor array
passive radar
Radar signal processing
Surveillance
Transmitting antennas
ISSN:2379-190X
Online Access:Get full text
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Summary:This paper addresses the passive detection of a common signal in two multi-sensor arrays. For this problem, we derive a detector based on likelihood theory for the case of one-antenna transmitters, independent Gaussian noises with arbitrary spatial structure, Gaussian signals, and known channel subspaces. The detector uses a likelihood ratio where all but one of the unknown parameters are replaced by their maximum likelihood (ML) estimates. The ML estimation of the remaining parameter requires a numerical search, and it is therefore estimated using a sample-based estimator. The performance of the proposed detector is illustrated by means of Monte Carlo simulations and compared with that of the detector for unknown channels, showing the advantage of this knowledge.
ISSN:2379-190X
DOI:10.1109/ICASSP49357.2023.10094671