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A Novel Demixing Algorithm for Joint Target Detection and Impulsive Noise Suppression
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Micro and Nanosystems.ORCID iD: 0000-0002-3050-7705
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.ORCID iD: 0000-0003-4519-9204
EURECOM, Commun Syst Dept, Biot, F-06904 Sophia Antipolis, France..ORCID iD: 0000-0001-6866-6595
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Micro and Nanosystems.ORCID iD: 0000-0003-3339-9137
2022 (English)In: IEEE Communications Letters, ISSN 1089-7798, E-ISSN 1558-2558, Vol. 26, no 11, p. 2750-2754Article in journal (Refereed) Published
Abstract [en]

This work considers a collocated radar scenario where a probing signal is emitted toward the targets of interest and records the received echoes. Estimating the relative delay-Doppler shifts of the targets allows determining their relative locations and velocities. However, the received radar measurements are often affected by impulsive non-Gaussian noise which makes a few measurements partially corrupted. While demixing radar signal and impulsive noise is challenging in general by traditional subspace-based methods, atomic norm minimization (ANM) has been recently developed to perform this task in a much more efficient manner. Nonetheless, the ANM cannot identify close delay-Doppler pairs and also requires many measurements. Here, we propose a smoothed l(0) atomic optimization problem encouraging both the sparse features of the targets and the impulsive noise. We design a majorization-minimization algorithm that converges to the solution of the proposed non-convex problem using alternating direction method of multipliers (ADMM). Simulations results verify the superior accuracy of our proposed algorithm even for very close delay-Doppler pairs in comparison to ANM with around 40 dB improvement.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2022. Vol. 26, no 11, p. 2750-2754
Keywords [en]
Radar, impulsive noise cancellation, compressed sensing, l(0) function, non-convex optimization, atomic norm minimization
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-322150DOI: 10.1109/LCOMM.2022.3199460ISI: 000881981500051Scopus ID: 2-s2.0-85136894654OAI: oai:DiVA.org:kth-322150DiVA, id: diva2:1715643
Note

QC 20221202

Available from: 2022-12-02 Created: 2022-12-02 Last updated: 2023-11-01Bibliographically approved

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Seidi, MohammadrezaRazavikia, SaeedDaei, SajadOberhammer, Joachim

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