AoA Estimation Using Sufficient Statistic-Based MUSIC Algorithms for Low SNR
This letter primarily focuses on efficient angular estimation in low signal-to-noise ratio (SNR) conditions, as well as small-sample scenarios, aiming to facilitate the application of Integrated Sensing and Communication (ISAC) techniques. We propose a novel algorithm derived from multiple signal classification (MUSIC), called SS-MUSIC, which incorporates the sufficient statistic theorem. Simulation results demonstrate that SS-MUSIC achieves lower estimation errors in low-SNR conditions compared with existing MUSIC-based approaches, without increased complexity, and also exhibits superior performance in multi-target and small-sample scenarios.
Authors
- Wanqing Guan (ORCID: https://orcid.org/0000-0002-5205-7936)
- Linpei Li (ORCID: https://orcid.org/0000-0002-4674-1723)
- Xuqing Liu (ORCID: https://orcid.org/0000-0001-5998-6546)
- Haijun Zhang (ORCID: https://orcid.org/0000-0002-0236-6482)
- Yang Lu
- Chunlei Sun (ORCID: https://orcid.org/0000-0003-1676-9220)
Institutions
- Beijing University of Posts and Telecommunications (CN)
- North China Electric Power University (CN)
- China Electric Power Research Institute
- University of Science and Technology Beijing (CN)
Publication Details
- Journal
- IEEE Transactions on Vehicular Technology
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1109/tvt.2025.3604610
- Primary Topic
- Direction-of-Arrival Estimation Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Basic and Applied Basic Research Foundation of Guangdong Province