Synthetic Augmented L-shaped Array design and 2D Cramér-Rao bound analysis based on Vertical-Horizontal Moving Scheme

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Digital Signal Processing Pub Date : 2024-11-15 DOI:10.1016/j.dsp.2024.104867

Danni Feng , Guiyu Wang , Xiangnan Li , Weijiang Wang , Shiwei Ren

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Abstract

This paper introduces an innovative approach to enhance the effectiveness of the L-shaped array using synthetic aperture processing. Through the implementation of the Vertical-Horizontal Moving Scheme (VHMS), a novel 2D array structure called the Synthetic Augmented L-shaped Array (SALA) is developed, featuring varied element spacing along the x and y axes of a 1D linear array. Comparative analyses demonstrate that the SALA achieves a larger difference coarray and higher degrees of freedom compared to arrays with an equivalent number of physical elements. The paper also provides a comprehensive derivation of the Cramér-Rao Bound (CRB) for 2D moving arrays, with detailed analyses of its conditions and properties. Simulation results highlight the SALA's superior capabilities in detecting multiple sources and providing precise 2D direction of arrival estimation, along with a lower CRB, indicating its potential for practical implementation in diverse scenarios.

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基于垂直-水平移动方案的合成增强 L 型阵列设计和二维克拉梅尔-拉奥约束分析

本文介绍了一种利用合成孔径处理增强 L 形阵列有效性的创新方法。通过实施垂直-水平移动方案（VHMS），开发了一种名为合成增强 L 形阵列（SALA）的新型二维阵列结构，其特点是沿一维线性阵列的 x 轴和 y 轴改变元素间距。对比分析表明，与物理元素数量相当的阵列相比，SALA 阵列具有更大的共阵列差和更高的自由度。论文还全面推导了二维移动阵列的克拉梅尔-拉奥约束 (CRB)，并对其条件和属性进行了详细分析。仿真结果凸显了 SALA 在检测多个信号源和提供精确的二维到达方向估计方面的卓越能力，以及较低的 CRB，这表明它具有在各种场景中实际应用的潜力。

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来源期刊

Digital Signal Processing 工程技术-工程：电子与电气

CiteScore

5.30

自引率

17.20%

发文量

435

审稿时长

66 days

期刊介绍： Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,