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Circuits, Systems, and Signal Processing
Published in:

29-06-2024

Adaptive Robust Subspace Detection Based on GLRT, Rao, Wald, Gradient, and Durbin Tests

Authors: Gaoqing Xiong, Hui Cao, Weijian Liu, Jun Liu, Chongying Qi, Daikun Zheng

Published in: Circuits, Systems, and Signal Processing | Issue 10/2024

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Abstract

The current paper investigates the issue of designing adaptive robust subspace detectors in Gaussian noise whose covariance matrix is unknown. The original problem is revised by importing a fictitious signal with a given structure within the signal-plus-noise hypothesis to collect leakage signals around the subspace, thus increasing the credibility of this hypothesis in situations involving mismatch. To solve the issue described above, we utilize the generalized likelihood ratio test, Rao, Wald, Gradient, and Durbin tests to derive five adaptive subspace detectors. Both theoretical proofs and Monte Carlo simulation results suggest that these proposed detectors possess the constant false alarm rate properties. Numerical examples reveal the effectiveness of these proposed detectors and show their varying degrees of robustness under mismatch scenarios.
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Appendix
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Metadata
Title
Adaptive Robust Subspace Detection Based on GLRT, Rao, Wald, Gradient, and Durbin Tests
Authors
Gaoqing Xiong
Hui Cao
Weijian Liu
Jun Liu
Chongying Qi
Daikun Zheng
Publication date
29-06-2024
Publisher
Springer US
Published in
Circuits, Systems, and Signal Processing / Issue 10/2024
Print ISSN: 0278-081X
Electronic ISSN: 1531-5878
DOI
https://doi.org/10.1007/s00034-024-02740-z

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