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Parametric localisation in frequency diverse array

IET RADAR SONAR AND NAVIGATION 2020年第5期14卷 716-727页

作者： Mahata, Kaushik Hyder, Md Mashud Univ Newcastle Elect Engn Callaghan NSW 2308 Australia

The authors present a non-traditional, parametric method for velocity, angle, and range estimation in a frequency diverse array radar. Unlike the traditional beamforming techniques, the proposed scheme transmits an omni-directional sinusoid regardless of the target locations. They propose a simple sampling strategy, which eliminates the need for employing a bank of bandpass filters at the receiver. Under the proposed sampling scheme the received data follows a convenient low rank model. They exploit this model to design a fast and accurate parametric estimation algorithm. Their velocity and range estimation steps employ known spectral analysis techniques. For angle estimation, they propose a new grid-less sparse recovery algorithm. The resulting methods are applicable to any arbitrary array geometry. Furthermore, they propose an efficient method to mitigate jamming. They also provide necessary guidelines to avoid ambiguity and achieve the desired resolution performance. The Cramer-Rao lower bound for the estimation problem is derived. The utility of the proposed method is demonstrated via numerical simulation results.

关键词： spectral analysis array signal processing parameter estimation radar signal processing jamming antenna arrays signal sampling target locations omni-directional sinusoid traditional beamforming techniques frequency diverse array radar parametric method parametric localisation estimation problem arbitrary array geometry resulting methods grid-less sparse recovery algorithm angle estimation spectral analysis techniques range estimation steps accurate parametric estimation algorithm fast estimation algorithm convenient low rank model received data sampling scheme bandpass filters simple sampling strategy

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Single Snapshot Super-Resolution DOA Estimation for arbitrary array Geometries

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IEEE SIGNAL PROCESSING LETTERS 2019年第1期26卷 119-123页

作者： Raj, Anupama Govinda McClellan, James H. Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

We address the problem of search-free direction of arrival (DOA) estimation for sensor arrays of arbitrary geometry under the challenging conditions of a single snapshot and coherent sources. We extend a method of search-free super-resolution beamforming, originally applicable only for uniform linear arrays, to arrays of arbitrary geometry. The infinite dimensional primal atomic norm minimization problem in continuous angle domain is converted to a dual problem. By exploiting periodicity, the dual function is then represented with a trigonometric polynomial using a truncated Fourier series. A linear rule of thumb is derived for selecting the minimum number of Fourier coefficients required for accurate polynomial representation, based on the distance of the farthest sensor from a reference point. The dual problem is then expressed as a semidefinite program and solved efficiently. Finally, the search-free DOA estimates are obtained through polynomial rooting and source amplitudes are recovered through least squares. Simulations using circular and random planar arrays show perfect DOA estimation in noise-free cases.

关键词： Super-resolution arbitrary array geometry sparse DOA estimation off-grid problem atomic norm compressive beamforming coherent sources limited snapshots

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SUPER-RESOLUTION DOA ESTIMATION FOR arbitrary array GEOMETRIES USING A SINGLE NOISY SNAPSHOT 44

SUPER-RESOLUTION DOA ESTIMATION FOR ARBITRARY ARRAY GEOMETRI...

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Raj, Anupama Govinda McClellan, James H. Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

ISBN: (纸本)9781479981311

We address the problem of search-free DOA estimation from a single noisy snapshot for sensor arrays of arbitrary geometry, by extending a method of gridless super-resolution beam forming to arbitrary arrays with noisy measurements. The primal atomic norm minimization problem is converted to a dual problem in which the periodic dual function is represented with a trigonometric polynomial using truncated Fourier series. The number of terms required for accurate representation depends linearly on the distance of the farthest sensor from a reference. The dual problem is then expressed as a semidefinite program and solved in polynomial time. DOA estimates are obtained via polynomial rooting followed by a LASSO based approach to remove extraneous roots arising in root finding from noisy data, and then source amplitudes are recovered by least squares. Simulations using circular and random planar arrays show high resolution DOA estimation in white and colored noise scenarios.

关键词： Super-resolution off-grid problem sparse DOA estimation arbitrary array geometry single snapshot

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Gridless DOA Estimation Method for arbitrary array Geometries Based on Complex-Valued Deep Neural Networks

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REMOTE SENSING 2024年第19期16卷 3752页

作者： Cao, Yuan Zhou, Tianjun Zhang, Qunfei Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China

Gridless direction of arrival (DOA) estimation methods have garnered significant attention due to their ability to avoid grid mismatch errors, which can adversely affect the performance of high-resolution DOA estimation algorithms. However, most existing gridless methods are primarily restricted to applications involving uniform linear arrays or sparse linear arrays. In this paper, we derive the relationship between the element-domain covariance matrix and the angular-domain covariance matrix for arbitrary array geometries by expanding the steering vector using a Fourier series. Then, a deep neural network is designed to reconstruct the angular-domain covariance matrix from the sample covariance matrix and the gridless DOA estimation can be obtained by Root-MUSIC. Simulation results on arbitrary array geometries demonstrate that the proposed method outperforms existing methods like MUSIC, SPICE, and SBL in terms of resolution probability and DOA estimation accuracy, especially when the angular separation between targets is small. Additionally, the proposed method does not require any hyperparameter tuning, is robust to varying snapshot numbers, and has a lower computational complexity. Finally, real hydrophone data from the SWellEx-96 ocean experiment validates the effectiveness of the proposed method in practical underwater acoustic environments.

关键词： gridless direction of arrival (DOA) estimation complex-valued deep neural network arbitrary array geometry high resolution

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