Gridless Sparse ISAR Imaging via 2-D Fast Reweighted Atomic Norm Minimization

Lv, Mingjiu; Chen, Wenfeng; Ma, Jian-wei; Yang, Jun; Cheng, Qi; Xiao-yan, MA

doi:10.1109/lgrs.2022.3191394

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…However, this approach has cubic complexity order of the matrix Q, here O(M 3 P 3 N 3 r ), which becomes infeasible for large signal dimensions. In this case, one may employ low-complexity approaches that have been proposed for ANM-based recovery such as reweighting the continuous dictionary [43], accelerating proximal gradient [44], or employing prior knowledge to apply block iterative 1 minimization [45]. In our recent work [10], we solved the 1-D DBD via a low-rank matrix Hankel recovery approach but this technique remains unexamined for multiple variables.…”

Section: Recovery Via Soman Minimizationmentioning

confidence: 99%

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Multi-Dimensional Dual-Blind Deconvolution Approach Toward Joint Radar-Communications

Jácome

Mishra

Vargas

et al. 2022

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)

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We consider a joint multiple-antenna radar-communications system in a co-existence scenario. Contrary to conventional applications, wherein at least the radar waveform and communications channel are known or estimated a priori, we investigate the case when the channels and transmit signals of both systems are unknown. In radar applications, this problem arises in multistatic or passive systems, where transmit signal is not known. Similarly, highly dynamic vehicular or mobile communications may render prior estimates of wireless channel unhelpful. In particular, the radar signal reflected-off multiple targets is overlaid with the multi-carrier communications signal. In order to extract the unknown continuous-valued target parameters (range, Doppler velocity, and direction-of-arrival) and communications messages, we formulate the problem as a sparse dual-blind deconvolution and solve it using atomic norm minimization. Numerical experiments validate our proposed approach and show that precise estimation of continuous-valued channel parameters, radar waveform, and communications messages is possible up to scaling ambiguities.

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Section: Recovery Via Soman Minimizationmentioning

confidence: 99%

“…Proof: Note that f r (r) = Q r w r (r) and f c (c) = Q c w c (c). Using the radar LMI in (43) and Schur complement, we get…”

Section: B Performance Analysesmentioning

confidence: 99%

Multi-Dimensional Dual-Blind Deconvolution Approach Toward Joint Radar-Communications

Jácome

Mishra

Vargas

et al. 2022

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)

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“…In contrast, decoupled ANM (DANM) [18] mitigates computational complexity by constructing matrix-form atomic norm and decoupling 2D information into two independent dimensions. Based on the DANM algorithm, recent researches focus on the case of multiple snapshots [19], and further complexity reduction [20,21]. Nevertheless, all aforementioned methods based on matrix-form atomic norm are limited to signals received from complete uniform rectangular arrays (URA).…”

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confidence: 99%

Efficient gridless 2D DOA estimation based on generalized matrix‐form atomic norm minimization

Gao,

Wang,

Zhang

et al. 2024

Electronics Letters

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Two‐dimensional (2D) direction‐of‐arrival (DOA) estimation is crucial in array signal processing. Compressed sensing (CS) provides a superior alternative to spatial spectrum estimation algorithms by enabling 2D DOA estimation of correlated sources from single snapshot data. However, the grid mismatch effect inherent in grid‐based CS algorithms impacts estimation accuracy. Despite recent advancements, the state‐of‐the‐art gridless CS algorithm, decoupled atomic norm minimization, is limited to specific 2D array geometries, such as uniform rectangular arrays. This letter presents an efficient gridless 2D DOA estimation algorithm for generalized rectangular arrays, including both uniform and sparse arrays. The proposed algorithm achieves high accuracy through a novel approach called generalized matrix‐form atomic norm minimization and provides a fast solution using the alternating direction method of multipliers. Validation through computer simulations and practical experiments underscores its efficacy.

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Multi-antenna dual-blind deconvolution for joint radar-communications via SoMAN minimization

Jacome,

Vargas,

Mishra

et al. 2024

Signal Processing

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Gridless Sparse ISAR Imaging via 2-D Fast Reweighted Atomic Norm Minimization

Cited by 6 publications

References 15 publications

Multi-Dimensional Dual-Blind Deconvolution Approach Toward Joint Radar-Communications

Multi-Dimensional Dual-Blind Deconvolution Approach Toward Joint Radar-Communications

Efficient gridless 2D DOA estimation based on generalized matrix‐form atomic norm minimization

Multi-antenna dual-blind deconvolution for joint radar-communications via SoMAN minimization

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