Coherent Source Enumeration With Compact ULAs

Source enumeration typically relies on subspace-based techniques that require accurate separation of signal and noise subspaces. However, prior works do not address coherent sources in small uniform linear arrays, where ambiguities arise in the spatial spectrum. We address this by decomposing the fo...

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Published in:	IEEE signal processing letters Vol. 32; pp. 3077 - 3081
Main Authors:	Sil, Dibakar, Krishnan, Sunder Ram, Mishra, Kumar Vijay
Format:	Journal Article
Language:	English
Published:	New York IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Convexity Covariance matrices Covariance matrix Direction-of-arrival estimation Elliptopes Enumeration Estimation Linear arrays Matrix decomposition Noise Optimization rank constraint Sensor arrays Sensors smoothing Smoothing methods source enumeration Subspaces ULA Vectors
ISSN:	1070-9908, 1558-2361
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Abstract	Source enumeration typically relies on subspace-based techniques that require accurate separation of signal and noise subspaces. However, prior works do not address coherent sources in small uniform linear arrays, where ambiguities arise in the spatial spectrum. We address this by decomposing the forward-backward smoothed covariance matrix into a sum of a rank-constrained Toeplitz matrix and a diagonal matrix with non-negative entries representing the signal and noise subspaces, respectively. The resulting non-convex optimization problem is solved by proposing T oeplitz a pproach for r ank-based tar g et e s t imation (TARgEt) that employs the alternating direction method of multipliers. Numerical results on both synthetic and real-world datasets demonstrate the effectiveness and robustness of TARgEt over the state-of-the-art.
AbstractList	Source enumeration typically relies on subspace-based techniques that require accurate separation of signal and noise subspaces. However, prior works do not address coherent sources in small uniform linear arrays, where ambiguities arise in the spatial spectrum. We address this by decomposing the forward-backward smoothed covariance matrix into a sum of a rank-constrained Toeplitz matrix and a diagonal matrix with non-negative entries representing the signal and noise subspaces, respectively. The resulting non-convex optimization problem is solved by proposing Toeplitz approach for rank-based target estimation (TARgEt) that employs the alternating direction method of multipliers. Numerical results on both synthetic and real-world datasets demonstrate the effectiveness and robustness of TARgEt over the state-of-the-art. Source enumeration typically relies on subspace-based techniques that require accurate separation of signal and noise subspaces. However, prior works do not address coherent sources in small uniform linear arrays, where ambiguities arise in the spatial spectrum. We address this by decomposing the forward-backward smoothed covariance matrix into a sum of a rank-constrained Toeplitz matrix and a diagonal matrix with non-negative entries representing the signal and noise subspaces, respectively. The resulting non-convex optimization problem is solved by proposing T oeplitz a pproach for r ank-based tar g et e s t imation (TARgEt) that employs the alternating direction method of multipliers. Numerical results on both synthetic and real-world datasets demonstrate the effectiveness and robustness of TARgEt over the state-of-the-art.
Author	Krishnan, Sunder Ram Mishra, Kumar Vijay Sil, Dibakar
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SubjectTerms	Convexity Covariance matrices Covariance matrix Direction-of-arrival estimation Elliptopes Enumeration Estimation Linear arrays Matrix decomposition Noise Optimization rank constraint Sensor arrays Sensors smoothing Smoothing methods source enumeration Subspaces ULA Vectors
Title	Coherent Source Enumeration With Compact ULAs
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