Open AccessA Systematic Literature Review of Source Number Estimation in Multi-Sensor Array Signal Processing
Author(s) -
Ge Shengguo,
Fei Xiaotao
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3573071
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Source number estimation is a key challenge in multi-sensor array signal processing, focused on accurately determining the number of signal sources based on observed data. This problem is vital for applications in radar, sonar, wireless communication, and astronomy. Despite the variety of methods devel-oped for source number estimation, a comprehensive systematic literature review (SLR) is lacking. This re-view begins by outlining traditional methods for source number estimation and assessing their performance through simulations. It then delves into the latest advancements made between 2017 and 2025, aiming to provide a thorough overview of the factors influencing source number estimation. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, seven digital databases were searched, and 23 key papers were selected after applying predefined inclusion and exclusion criteria. The results show that the performance of source number estimation algorithms is heavily impacted by varia-bles such as noise background, signal-to-noise ratio (SNR), the number of snapshots, array manifold, and array element number. Finally, this review points to future research opportunities, particularly the potential of deep learning techniques, and discusses unresolved challenges and gaps in current studies, offering rec-ommendations to steer future research in this area.