Moment-Matching Array Processing Technique for diffuse source estimation
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914205451419648 |
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| author | Cros, Colin Ferro-Famil, Laurent |
| author_facet | Cros, Colin Ferro-Famil, Laurent |
| contents | Direction of Arrival (DOA) estimation is a fundamental problem in signal processing. Diffuse sources, whose power density cannot be represented with a single angular coordinate, are usually characterized based on prior assumptions, which associate the source angular density with a specific set of functions. However, these assumptions can lead to significant estimation biases when they are incorrect. This paper introduces the Moment-Matching Estimation Technique (MoMET), a low-complexity method for estimating the mean DOA, spread, and power of a narrow diffuse source without requiring prior knowledge on the source distribution. The unknown source density is characterized by its mean DOA and its first central moments, which are estimated through covariance matching techniques which fit the empirical covariance of the measurements to that modeled from the moments. The MoMET parameterization is robust to incorrect model assumptions, and numerically efficient. The asymptotic bias and covariance of the new estimator are derived and its performance is demonstrated through simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15283 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Moment-Matching Array Processing Technique for diffuse source estimation Cros, Colin Ferro-Famil, Laurent Signal Processing Direction of Arrival (DOA) estimation is a fundamental problem in signal processing. Diffuse sources, whose power density cannot be represented with a single angular coordinate, are usually characterized based on prior assumptions, which associate the source angular density with a specific set of functions. However, these assumptions can lead to significant estimation biases when they are incorrect. This paper introduces the Moment-Matching Estimation Technique (MoMET), a low-complexity method for estimating the mean DOA, spread, and power of a narrow diffuse source without requiring prior knowledge on the source distribution. The unknown source density is characterized by its mean DOA and its first central moments, which are estimated through covariance matching techniques which fit the empirical covariance of the measurements to that modeled from the moments. The MoMET parameterization is robust to incorrect model assumptions, and numerically efficient. The asymptotic bias and covariance of the new estimator are derived and its performance is demonstrated through simulations. |
| title | Moment-Matching Array Processing Technique for diffuse source estimation |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2512.15283 |