Loading...
Send to Friend
 Bookmark
 Review List
  1. Simple search
  2. Search result
  3. Three-Dimensional Decoupled Atomic Norm Minimization

Three-Dimensional Decoupled Atomic Norm Minimization

Jazi, M.B ; Sharif University of Technology | 2024

431 Viewed
  1. Type of Document: Article
  2. DOI: 10.1109/ICASSP48485.2024.10448278
  3. Publisher: 2024
  4. Abstract:
  5. This paper focuses on the problem of three-dimensional (3D) frequency retrieval from a set of time samples. The assumption made in this study is that the signal being analyzed consists of K continuous-valued 3D complex sinusoidal signals. The main objective is to identify all frequency components when only one snapshot of regularly-spaced time samples is available. The vectorized version of atomic norm minimization (ANM) method could accurately solve the problem. Nonetheless, this technique demands significant computational resources. In this paper, we propose a computationally-efficient method based on atomic norm minimization known as three-dimensional decoupled ANM (3D-DANM). This approach transforms the estimation problem into a semi-definite programming (SDP) problem. By performing Vandermonde decomposition on the Toeplitz matrices resulted from the SDP problem, the frequencies of all sources will be estimated. The extracted parameters are then paired using a specific approach. Simulation results demonstrate that the proposed method closely approaches the Cramér-Rao lower bound (CRLB) for sufficient signal-to-noise ratio (SNR) values. Additionally, it offers significant improvements in computational cost compared to other existing approaches. © 2024 IEEE
  6. Keywords:
  7. Computational complexity ; Frequency estimation ; Atomic norm minimization ; Cramer Rao lower bound ; Frequency retrieval ; Minimisation ; Semi-definite programming ; Three-dimensional decoupled atomic norm minimization ; Three-dimensional frequency retrieval ; Time samples ; Signal to noise ratio
  8. Source: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings ; 2024 , Pages 8716-8720 ; 15206149 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/10448278