Passive Detection of a Random Signal Common to Multi-Sensor Reference and Surveillance Arrays

This paper addresses the passive detection of a common rank-one subspace signal received in two multi-sensor arrays. We consider the case of a one-antenna transmitter sending a common Gaussian signal, independent Gaussian noises with arbitrary spatial covariance, and known channel subspaces. The det...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology Vol. 73; no. 7; pp. 10106 - 10117
Main Authors: Ramirez, David, Santamaria, Ignacio, Scharf, Louis L.
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Antenna arrays
Approximation
Beamforming
Closed form solutions
Coherence
Covariance matrices
Detectors
Doppler effect
Exact solutions
generalized likelihood ratio (GLR)
hypothesis test
Likelihood ratio
Maximum likelihood estimation
Maximum likelihood estimators
multi-sensor array
Multisensor systems
Parameter estimation
Passive radar
Quadratic forms
Random signals
Sensor arrays
Sensors
Subspaces
Surveillance
Transmitters
ISSN:0018-9545, 1939-9359
Online Access:Get full text
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Summary:This paper addresses the passive detection of a common rank-one subspace signal received in two multi-sensor arrays. We consider the case of a one-antenna transmitter sending a common Gaussian signal, independent Gaussian noises with arbitrary spatial covariance, and known channel subspaces. The detector derived in this paper is a generalized likelihood ratio (GLR) test. For all but one of the unknown parameters, it is possible to find closed-form maximum likelihood (ML) estimator functions. We can further compress the likelihood to only an unknown vector whose ML estimate requires maximizing a product of ratios in quadratic forms, which is carried out using a trust-region algorithm. We propose two approximations of the GLR that do not require any numerical optimization: one based on a sample-based estimator of the unknown parameter whose ML estimate cannot be obtained in closed-form, and one derived under low-SNR conditions. Notably, all the detectors are scale-invariant, and the approximations are functions of beamformed data. However, they are not GLRTs for data that has been pre-processed with a beamformer, a point that is elaborated in the paper. These detectors outperform previously published correlation detectors on simulated data, in many cases quite significantly. Moreover, performance results quantify the performance gains over detectors that assume only the dimension of the subspace to be.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2024.3366757