测量相关信噪比条件下稀疏阵列的cram - rao边界和分辨率优势

IF 3.2 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Signal Processing Letters Pub Date : 2025-01-03 DOI:10.1109/LSP.2024.3525400

Sina Shahsavari;Piya Pal

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引用次数: 0

摘要

本文给出了在低信噪比条件下，任意稀疏阵列的cram r- rao界（CRB）作为两个远场窄带源间角间距函数的非渐近刻画。主要贡献是在特定测量依赖的信噪比（MD-SNR）制度下匹配CRB的上界和下界的推导，其中可以随着传感器数量的增加而逐渐放大到较低的信噪比。这种严格的表征有助于建立稀疏阵列，如嵌套阵列和协素数阵列，在特定的信噪比下，与均匀线性阵列（ULAs）相比，可证明具有更低的CRB。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Cramér-Rao Bounds and Resolution Benefits of Sparse Arrays in Measurement-Dependent SNR Regimes

This paper derives new non-asymptotic characterization of the Cramér-Rao Bound (CRB) of any sparse array as a function of the angular separation between two far-field narrowband sources in certain regimes characterized by a low Signal-to-Noise Ratio (SNR). The primary contribution is the derivation of matching upper and lower bounds on the CRB in a certain measurement-dependent SNR (MD-SNR) regime, where one can zoom into progressively lower SNR as the number of sensors increases. This tight characterization helps to establish that sparse arrays such as nested and coprime arrays provably exhibit lower CRB compared to Uniform Linear Arrays (ULAs) in the specified SNR regime.

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来源期刊

IEEE Signal Processing Letters 工程技术-工程：电子与电气

CiteScore

7.40

自引率

12.80%

发文量

339

审稿时长

2.8 months

期刊介绍： The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.