Direction-of-arrival estimation of coherent sources with leaky-wave antennas using spatially filtered interpolation

IF 3.6 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing Pub Date : 2025-08-19 DOI:10.1016/j.sigpro.2025.110245

Rida Maydani , Yide Wang , Julien Sarrazin

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引用次数: 0

Abstract

With their frequency-beam scanning behavior, leaky-wave antennas (LWAs) are promising solutions to develop accurate and cost-effective direction-of-arrival (DoA) estimation systems. However, DoA estimators such as MUSIC face challenges with coherent sources due to the non-Vandermonde LWA steering matrix. Leveraging the unique radiation properties of LWAs, this paper first divides the entire field of view into several angular sectors, and then introduces a robust and accurate sectorized spatially-filtered interpolation (SFI) method to transform the LWA steering matrix into a Vandermonde matrix in each sector while minimizing the issue of out-of-sector interference. The proposed method thus allows the estimation of DoAs of coherent sources with LWAs. The simulation results show that the DoAs of multiple coherent sources across the entire field-of-view, regardless of their angular sector, can be correctly estimated. The performance of the proposed method is shown to be close to the Cramér–Rao Bound.

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基于空间滤波插值的漏波天线相干源到达方向估计

泄漏波天线（LWAs）具有频率波束扫描特性，是开发准确且经济高效的到达方向（DoA）估计系统的有前途的解决方案。然而，由于非vandermonde LWA转向矩阵，像MUSIC这样的DoA估计器面临着相干源的挑战。利用LWA独特的辐射特性，首先将整个视场划分为多个角度扇区，然后引入一种鲁棒且精确的扇区空间滤波插值（SFI）方法，将LWA的转向矩阵在每个扇区内转换为Vandermonde矩阵，同时最大限度地减少扇区外干扰问题。因此，该方法可以用LWAs估计相干源的doa。仿真结果表明，该方法可以正确估计整个视场内多个相干源的DoAs，而不考虑其角扇形。结果表明，该方法的性能接近cram - rao边界。

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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来源期刊

Signal Processing 工程技术-工程：电子与电气

CiteScore

9.20

自引率

9.10%

发文量

309

审稿时长

41 days

期刊介绍： Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal processing. Subject areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal Processing Systems; Software Developments; Image Processing; Pattern Recognition; Optical Signal Processing; Digital Signal Processing; Multi-dimensional Signal Processing; Communication Signal Processing; Biomedical Signal Processing; Geophysical and Astrophysical Signal Processing; Earth Resources Signal Processing; Acoustic and Vibration Signal Processing; Data Processing; Remote Sensing; Signal Processing Technology; Radar Signal Processing; Sonar Signal Processing; Industrial Applications; New Applications.