Controlling AoA Estimation Errors in Rician Fading via Measurement Quality Classification
Angle of Arrival (AoA) estimation plays a crucial role in modern positioning systems but is often affected by errors in multipath channels. Existing methods typically lack a direct mechanism to assess the quality of the estimates, while full channel estimation using channel sounders is computational...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11037416/ |
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| Summary: | Angle of Arrival (AoA) estimation plays a crucial role in modern positioning systems but is often affected by errors in multipath channels. Existing methods typically lack a direct mechanism to assess the quality of the estimates, while full channel estimation using channel sounders is computationally expensive and impractical for many applications. To address this challenge, we propose a classification method that leverages frequency smoothing and the estimated Rician K-factor to evaluate AoA measurements. The algorithm discards estimates with low precision, thereby reducing the mean AoA error and achieving the stringent accuracy required for indoor positioning. Simulation results demonstrate that the proposed method significantly improves the AoA error statistics and effectively controls the maximum estimation error. |
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| ISSN: | 2169-3536 |