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Resilient Sparse Array Radar with the Aid of Deep Learning

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Aya Mostafa Ahmed, Udaya S.K.P. Miriya Thanthrige, Aydin Sezgin, Fulvio Gini

Machine Learning · Submitted 21 Jun 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Numerical results show that both approaches can significantly improve the performance of MRA with two failed sensors.

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arXiv ID2306.12285 (version 1)
Pith IDLXUDNW2X, Pith's permanent record of this paper and its review
CitationAya Mostafa Ahmed, Aydin Sezgin, Fulvio Gini, Udaya S.K.P. Miriya Thanthrige. "Resilient Sparse Array Radar with the Aid of Deep Learning." arXiv:2306.12285 (2023). https://pith.science/paper/LXUDNW2X
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 6,195 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.31 · human participants 0.01 · controlled comparison 0.84 · randomized assignment 0.01 · states quantitative result 0.80 · compares with prior results 0.87 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

In this paper, we address the problem of direction of arrival (DOA) estimation for multiple targets in the presence of sensor failures in a sparse array. Generally, sparse arrays are known with very high-resolution capabilities, where N physical sensors can resolve up to $\mathcal{O}(N^2)$ uncorrelated sources. However, among the many configurations introduced in the literature, the arrays that provide the largest hole-free co-array are the most susceptible to sensor failures. We propose here two machine learning (ML) methods to mitigate the effect of sensor failures and maintain the DOA estimation performance and resolution. The first method enhances the conventional spatial smoothing using deep neural network (DNN), while the second one is an end-to-end data-driven method. Numerical results show that both approaches can significantly improve the performance of MRA with two failed sensors. The data-driven method can maintain the performance of the array with no failures at high signal-tonoise ratio (SNR). Moreover, both approaches can even perform better than the original array at low SNR thanks to the denoising effect of the proposed DNN

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#sentencerolethe authors' own workstates the main conclusionstrength
s0 In this paper, we address the problem of direction of arrival (DOA) estimation for multiple targets in the presence of sensor failures in a sparse array. background 0.91 0.31 0.04 asserted 0.68
s1 Generally, sparse arrays are known with very high-resolution capabilities, where N physical sensors can resolve up to $\mathcal{O}(N^2)$ uncorrelated sources. background 1.00 0.05 0.03 asserted 0.95
s2 However, among the many configurations introduced in the literature, the arrays that provide the largest hole-free co-array are the most susceptible to sensor failures. background 0.84 0.08 0.03 asserted 0.99
s3 We propose here two machine learning (ML) methods to mitigate the effect of sensor failures and maintain the DOA estimation performance and resolution. contribution 0.99 0.92 0.14 asserted 0.97
s4 The first method enhances the conventional spatial smoothing using deep neural network (DNN), while the second one is an end-to-end data-driven method. method 0.90 0.84 0.05 asserted 0.98
s5 Numerical results show that both approaches can significantly improve the performance of MRA with two failed sensors. result 1.00 0.95 0.36 asserted 1.00
s6 The data-driven method can maintain the performance of the array with no failures at high signal-tonoise ratio (SNR). result 1.00 0.92 0.14 asserted 0.95
s7 Moreover, both approaches can even perform better than the original array at low SNR thanks to the denoising effect of the proposed DNN result 0.94 0.93 0.22 asserted 0.95

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 1.00.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

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Original abstract

In this paper, we address the problem of direction of arrival (DOA) estimation for multiple targets in the presence of sensor failures in a sparse array. Generally, sparse arrays are known with very high-resolution capabilities, where N physical sensors can resolve up to $\mathcal{O}(N^2)$ uncorrelated sources. However, among the many configurations introduced in the literature, the arrays that provide the largest hole-free co-array are the most susceptible to sensor failures. We propose here two machine learning (ML) methods to mitigate the effect of sensor failures and maintain the DOA estimation performance and resolution. The first method enhances the conventional spatial smoothing using deep neural network (DNN), while the second one is an end-to-end data-driven method. Numerical results show that both approaches can significantly improve the performance of MRA with two failed sensors. The data-driven method can maintain the performance of the array with no failures at high signal-tonoise ratio (SNR). Moreover, both approaches can even perform better than the original array at low SNR thanks to the denoising effect of the proposed DNN

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