scholarly journals Polarised HRRP scattering centre estimation via atomic norm minimisation

2019 ◽  
Vol 2019 (21) ◽  
pp. 7847-7850
Author(s):  
Haibo Liu ◽  
Ying Xi ◽  
Yanhua Wang ◽  
Yang Li ◽  
Tong Zhao
Keyword(s):  
Atomic Norm ◽  
Scattering Centre

DYANOM—Dykstra’s projection based atomic norm solver

2021 ◽  
Vol 182 ◽  
pp. 107958
Author(s):  
R. Jyothi ◽  
P. Babu ◽  
Y. Wang ◽  
Z. Tian
Keyword(s):  
Atomic Norm

scholarly journals Linear Time-Periodic System Identification with Grouped Atomic Norm Regularization

2020 ◽  
Vol 53 (2) ◽  
pp. 1237-1242
Author(s):  
Mingzhou Yin ◽  
Andrea Iannelli ◽  
Mohammad Khosravi ◽  
Anilkumar Parsi ◽  
Roy S. Smith
Keyword(s):  
System Identification ◽  
Periodic System ◽  
Linear Time ◽  
Atomic Norm ◽  
Time Periodic

On the Role of the Bounded Lemma in the SDP Formulation of Atomic Norm Problems

2017 ◽  
Vol 24 (7) ◽  
pp. 972-976 ◽  
Author(s):  
Oguzhan Teke ◽  
Palghat P. Vaidyanathan
Keyword(s):  
Atomic Norm

scholarly journals Superresolution 2D DOA Estimation for a Rectangular Array via Reweighted Decoupled Atomic Norm Minimization

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Ming-Ming Liu ◽  
Chun-Xi Dong ◽  
Yang-Yang Dong ◽  
Guo-Qing Zhao
Keyword(s):  
Computational Complexity ◽  
Optimization Problem ◽  
Toeplitz Matrices ◽  
Doa Estimation ◽  
Rank Minimization ◽  
Rectangular Array ◽  
Angle Estimation ◽  
Norm Minimization ◽  
Atomic Norm ◽  
Surrogate Function

This paper proposes a superresolution two-dimensional (2D) direction of arrival (DOA) estimation algorithm for a rectangular array based on the optimization of the atomic l0 norm and a series of relaxation formulations. The atomic l0 norm of the array response describes the minimum number of sources, which is derived from the atomic norm minimization (ANM) problem. However, the resolution is restricted and high computational complexity is incurred by using ANM for 2D angle estimation. Although an improved algorithm named decoupled atomic norm minimization (DAM) has a reduced computational burden, the resolution is still relatively low in terms of angle estimation. To overcome these limitations, we propose the direct minimization of the atomic l0 norm, which is demonstrated to be equivalent to a decoupled rank optimization problem in the positive semidefinite (PSD) form. Our goal is to solve this rank minimization problem and recover two decoupled Toeplitz matrices in which the azimuth-elevation angles of interest are encoded. Since rank minimization is an NP-hard problem, a novel sparse surrogate function is further proposed to effectively approximate the two decoupled rank functions. Then, the new optimization problem obtained through the above relaxation can be implemented via the majorization-minimization (MM) method. The proposed algorithm offers greatly improved resolution while maintaining the same computational complexity as the DAM algorithm. Moreover, it is possible to use a single snapshot for angle estimation without prior information on the number of sources, and the algorithm is robust to noise due to its iterative nature. In addition, the proposed surrogate function can achieve local convergence faster than existing functions.

scholarly journals Incorporation of prior knowledge into sparse time dispersive OFDM channel estimation via weighted atomic norm minimisation

2020 ◽  
Vol 14 (11) ◽  
pp. 1704-1708
Author(s):  
Hoomaan Hezaveh ◽  
Iman Valiulahi ◽  
Mohammad Hossein Kahaei
Keyword(s):  
Channel Estimation ◽  
Prior Knowledge ◽  
Atomic Norm
Sign in / Sign up

Export Citation Format

Share Document