Efficient Robust Adaptive Beamforming Based on Spatial Sampling with Virtual Sensors
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866916647253573632 |
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| author | Mohammedzadeh, S. de Lamare, R. |
| author_facet | Mohammedzadeh, S. de Lamare, R. |
| contents | Robust adaptive beamforming (RAB) based on interference-plus-noise covariance (IPNC) matrix reconstruction can experience serious performance degradation in the presence of look direction and array geometry mismatches, particularly when the input signal-to-noise ratio (SNR) is large. In this work, we present a RAB technique to address covariance matrix reconstruction problems. The proposed method involves IPNC matrix reconstruction using a low-complexity spatial sampling process (LCSSP) and employs a virtual received array vector. In particular, we devise a power spectrum sampling strategy based on a projection matrix computed in a higher dimension. A key feature of the proposed LCSSP technique is to avoid reconstruction of the IPNC matrix by integrating over the angular sector of the interference-plus-noise region. Simulation results are shown and discussed to verify the effectiveness of the proposed LCSSP method against existing approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06540 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Robust Adaptive Beamforming Based on Spatial Sampling with Virtual Sensors Mohammedzadeh, S. de Lamare, R. Information Theory Signal Processing Robust adaptive beamforming (RAB) based on interference-plus-noise covariance (IPNC) matrix reconstruction can experience serious performance degradation in the presence of look direction and array geometry mismatches, particularly when the input signal-to-noise ratio (SNR) is large. In this work, we present a RAB technique to address covariance matrix reconstruction problems. The proposed method involves IPNC matrix reconstruction using a low-complexity spatial sampling process (LCSSP) and employs a virtual received array vector. In particular, we devise a power spectrum sampling strategy based on a projection matrix computed in a higher dimension. A key feature of the proposed LCSSP technique is to avoid reconstruction of the IPNC matrix by integrating over the angular sector of the interference-plus-noise region. Simulation results are shown and discussed to verify the effectiveness of the proposed LCSSP method against existing approaches. |
| title | Efficient Robust Adaptive Beamforming Based on Spatial Sampling with Virtual Sensors |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2503.06540 |