A Large-Dimensional Analysis of ESPRIT DoA Estimation: Inconsistency and a Correction via RMT
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914474611441664 |
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| author | Wang, Zhengyu Yang, Wei Mai, Xiaoyi Ling, Zenan Liao, Zhenyu Qiu, Robert C. |
| author_facet | Wang, Zhengyu Yang, Wei Mai, Xiaoyi Ling, Zenan Liao, Zhenyu Qiu, Robert C. |
| contents | In this paper, we perform asymptotic analyses of the widely used ESPRIT direction-of-arrival (DoA) estimator for large arrays, where the array size $N$ and the number of snapshots $T$ grow to infinity at the same pace. In this large-dimensional regime, the sample covariance matrix (SCM) is known to be a poor eigenspectral estimator of the population covariance. We show that the classical ESPRIT algorithm, that relies on the SCM, and as a consequence of the large-dimensional inconsistency of the SCM, produces inconsistent DoA estimates as $N,T \to \infty$ with $N/T \to c \in (0,\infty)$, for both widely-~and~closely-spaced DoAs. Leveraging tools from random matrix theory (RMT), we propose an improved G-ESPRIT method and prove its consistency in the same large-dimensional setting. From a technical perspective, we derive a novel bound on the eigenvalue differences between two potentially non-Hermitian matrices, which may be of independent interest. Numerical simulations are provided to corroborate our theoretical findings. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_02746 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Large-Dimensional Analysis of ESPRIT DoA Estimation: Inconsistency and a Correction via RMT Wang, Zhengyu Yang, Wei Mai, Xiaoyi Ling, Zenan Liao, Zhenyu Qiu, Robert C. Signal Processing Probability Spectral Theory Statistics Theory In this paper, we perform asymptotic analyses of the widely used ESPRIT direction-of-arrival (DoA) estimator for large arrays, where the array size $N$ and the number of snapshots $T$ grow to infinity at the same pace. In this large-dimensional regime, the sample covariance matrix (SCM) is known to be a poor eigenspectral estimator of the population covariance. We show that the classical ESPRIT algorithm, that relies on the SCM, and as a consequence of the large-dimensional inconsistency of the SCM, produces inconsistent DoA estimates as $N,T \to \infty$ with $N/T \to c \in (0,\infty)$, for both widely-~and~closely-spaced DoAs. Leveraging tools from random matrix theory (RMT), we propose an improved G-ESPRIT method and prove its consistency in the same large-dimensional setting. From a technical perspective, we derive a novel bound on the eigenvalue differences between two potentially non-Hermitian matrices, which may be of independent interest. Numerical simulations are provided to corroborate our theoretical findings. |
| title | A Large-Dimensional Analysis of ESPRIT DoA Estimation: Inconsistency and a Correction via RMT |
| topic | Signal Processing Probability Spectral Theory Statistics Theory |
| url | https://arxiv.org/abs/2501.02746 |