基於稀疏特性的無相角未定性相位回復之研究:演算法及效能分析

Project Details

Description

Phase retrieval is concerned with the reconstruction of a signal from magnitude-only linear measurements. This problem arises in many engineering and scientific applications, such as optical imaging, X-ray crystallography, astronomy, etc., mainly because sensing devices in optical systems can only capture the intensity of light waves. It also arises in wireless communications for the purpose of removing unreliable phase information. Inspired by the emerging compressive sensing theory, sparse phase retrieval, which aims to recover sparse signals from sub-Nyquist amplitude samples, has received considerable attention in recent years. Many sparsity-promoting algorithms for efficient signal reconstruction have also been developed in the literature. However, all existing works ensure signal recovery only up to a global phase ambiguity, and almost all the proposed recovery schemes are centralized protocols. Therefore, this three-year research project aims at making contributions to this challenging topic. In the first two years, we will do in-depth study of ambiguity-free/robust sparse phase retrieval in the cases with sparse noise (or outliers) and non-sparse bounded noise. We aim at developing new sparse phase retrieval schemes that are both exact (i.e., free from the phase ambiguity) and computationally efficient. The related rigorous performance guarantees will also be addressed. In the third year, we will examine the cooperative sparse phase retrieval problem among distributed clients in networks taking into account the heterogeneity of local tasks among clients. We will propose new distributed sparse phase retrieval schemes that enable collaborative reconstruction of a common sparse signal by local clients without sharing of the local datasets with the server, while also recovering personalized sparse vector to address the heterogeneity of local tasks in distributed clients. Afterwards, we will study the theoretical performance guarantees of our proposed schemes.
StatusFinished
Effective start/end date1/07/2231/07/23

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 5 - Gender Equality
  • SDG 8 - Decent Work and Economic Growth
  • SDG 12 - Responsible Consumption and Production
  • SDG 13 - Climate Action
  • SDG 17 - Partnerships for the Goals

Keywords

  • Phase retrieval
  • Sparse signals
  • Affine sampling
  • Ambiguity-free signal recovery
  • Distributed learning
  • Energy efficiency

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