English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In the signal reception a under complex electromagnetic environment, there always exist time–frequency overlaps among multiple sources, and the separation and recovery of sources in the received hybrid signals are very important and necessary for subsequent information acquisition. In this paper, for the dual-channel underdetermined time–frequency overlapped signals, a method based on submatrix transformation (SMT) and multi-source point compensation (MSPC) is proposed to solve the problem of source recovery. SMT aims to extract time–frequency single-source point of the targeted sources successively to complete the elementary recovery. When the degree of time–frequency overlap increases, the multi-source part has more impact on the time–frequency distribution of the sources and even seriously affects the recovery performance. Therefore, MSPC is proposed to compensate for the missing time–frequency coefficients of the elementary recovered sources, which effectively improves the signal integrity and recovery performance under severe time–frequency overlap. In the experiments, in order to demonstrate the feasibility and superiority of the proposed method, different types of signals are used to test, and the performance comparison with other mainstream source recovery methods is made.

ieee access

Novel Source Recovery Method of Underdetermined Time-Frequency Overlapped Signals Based on Submatrix Transformation and Multi-Source Point Compensation