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AoA Estimation Using Sufficient Statistic-Based MUSIC Algorithms for Low SNR
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DOI：10.1109/TVT.2025.3604610.png)
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Abstract
En 中文
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.
Keywords:
Sufficient statistic
multiple signal classification
angular estimation
low-SNR
small-sample
Journal
IF:
7.1
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1.8W
Citations:
6.6W
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