Low-Rank Tensor Completion by Sum of Tensor Nuclear Norm Minimization
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2940664
Abstract: In this paper, we study the problem of low-rank tensor completion with the purpose of recovering a low-rank tensor from a tensor with partial observed items. To date, there are several different definitions of tensor… read more here.
Keywords: tensor; tensor nuclear; norm minimization; rank tensor ... See more keywords
Closed-Loop Subspace Identification for Stable/ Unstable Systems Using Data Compression and Nuclear Norm Minimization
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3154017
Abstract: This paper provides a subspace method for closed-loop identification, which clearly specifies the model order from noisy measurement data. The method can handle long I/O data of the target system to be noise-tolerant and determine… read more here.
Keywords: norm minimization; closed loop; nuclear norm; data compression ... See more keywords
Robust Localization based on Mixed-Norm Minimization Criterion
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3177838
Abstract: This paper presents robust positioning methods that use range measurements to estimate location parameters. The existing maximum correntropy criterion-based localization algorithm uses only the l2 norm minimization. Therefore, the localization performance may not be satisfying… read more here.
Keywords: norm minimization; mixed norm; localization; criterion ... See more keywords
An Efficient Reconstruction Approach Based on Atomic Norm Minimization for Coprime Tomographic SAR
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2022.3143662
Abstract: Recently, we have proposed the coprime tomographic synthetic aperture radar (TomoSAR) technique, whose baseline configuration conforms to the coprime array geometry. This technique is devoted to reducing the required number of acquisitions in the practical… read more here.
Keywords: coprime tomographic; coprime; atomic norm; reconstruction ... See more keywords
Gridless Sparse ISAR Imaging via 2-D Fast Reweighted Atomic Norm Minimization
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2022.3191394
Abstract: Aiming at acquiring high-resolution ISAR image effectively and quickly, a new fast gridless imaging method with a sound two-dimensional (2-D) reweighting strategy is proposed in this letter. First, the received echo is characterized as a… read more here.
Keywords: isar; isar imaging; atomic norm; gridless ... See more keywords
RFI Source Detection Based on Reweighted ℓ1-Norm Minimization for Microwave Interferometric Radiometry
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2021.3096318
Abstract: Radio frequency interference (RFI) seriously deteriorates the quality of the retrieval of geophysical parameters, e.g., Earth surface moisture and ocean salinity, measured in microwave interferometric radiometry (MIR). The accurate detection of RFI sources is crucial… read more here.
Keywords: norm minimization; rfi; detection; interferometric radiometry ... See more keywords
Tomographic SAR Inversion by Atomic-Norm Minimization—The Gridless Compressive Sensing Approach
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3223524
Abstract: Synthetic aperture radar (SAR) tomography (TomoSAR) extends the synthetic aperture principle into the elevation direction for 3-D imaging. Due to the sparsity of the elevation signal, compressive sensing (CS) methods have been introduced for tomographic… read more here.
Keywords: minimization gridless; atomic norm; gridless; norm minimization ... See more keywords
DOD and DOA Estimation From Incomplete Data Based on PARAFAC and Atomic Norm Minimization Method
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2023.3234576
Abstract: In this article, we propose an efficient direction of departure (DOD) and direction of arrival (DOA) estimation method for bistatic multiple-input multiple-output (MIMO) radar with faulty arrays. A third-order tensor model is built, and the… read more here.
Keywords: doa estimation; norm minimization; atomic norm; method ... See more keywords
Robust DLPP With Nongreedy $\ell _1$ -Norm Minimization and Maximization
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2016.2636130
Abstract: Recently, discriminant locality preserving projection based on L1-norm (DLPP-L1) was developed for robust subspace learning and image classification. It obtains projection vectors by greedy strategy, i.e., all projection vectors are optimized individually through maximizing the… read more here.
Keywords: minimization maximization; nongreedy ell; robust dlpp; ell norm ... See more keywords
Exclusive Sparsity Norm Minimization With Random Groups via Cone Projection
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2018.2819958
Abstract: Many practical applications such as gene expression analysis, multitask learning, image recognition, signal processing, and medical data analysis pursue a sparse solution for the feature selection purpose and particularly favor the nonzeros evenly distributed in… read more here.
Keywords: norm minimization; sparsity norm; exclusive sparsity;
A Compact Formulation for the $\ell _{2,1}$ Mixed-Norm Minimization Problem
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2017.2788431
Abstract: Parameter estimation from multiple measurement vectors (MMVs) is a fundamental problem in many signal processing applications, e.g., spectral analysis and direction-of-arrival estimation. Recently, this problem has been addressed using prior information in form of a… read more here.
Keywords: sparse signal; mixed norm; formulation; norm minimization ... See more keywords