Two‐Dimensional DOA Estimation Using One‐Dimensional Search for Spherical Arrays

Huang, Qinghua; Feng, Jiajun; Fang, Yong

doi:10.1049/cje.2019.08.009

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…Spherical sensor arrays have been extensively investigated within the phased array antenna and the acoustic array community for several decades, spanning a diverse range of applications. These applications cover super-resolution direction finding [1][2][3], source localization [4,5], mobile communications, satellite communications [6][7][8], radar [9,10], and numerous others [11]. When dealing with targets distributed in a broad area of threedimensional space, a spherical sensor array emerges as the optimal choice due to its superior performance and the deployment efficiency of sensors.…”

Section: Introductionmentioning

confidence: 99%

On Difference Pattern Synthesis for Spherical Sensor Arrays

Huang,

Chen,

et al. 2024

Sensors

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An innovative method for synthesizing optimum difference patterns of the spherical sensor array is introduced, along with a sidelobe tapering technique. Firstly, we suggest employing the spherical harmonics of degree ±1 to synthesize the spherical array difference pattern; secondly, we study the mapping relationship between the difference pattern of the spherical sensor array and the difference pattern of the uniformly spaced linear array (ULA) with odd-numbered elements; finally, we enhance the Zolotarev difference pattern, which is a counterpart to the Dolph–Chebyshev sum pattern that traditionally allows synthesis only for ULA with even-numbered elements. Our modification extends its applicability to synthesize difference patterns for ULA with odd-numbered elements. Leveraging the optimal difference pattern, a generalized Bayliss difference pattern synthesis method designed for the ULA with odd-numbered elements is further proposed. To illustrate the effectiveness of our approach, we present several design examples through experimental simulation.

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Section: Introductionmentioning

confidence: 99%

On Difference Pattern Synthesis for Spherical Sensor Arrays

Huang,

Chen,

et al. 2024

Sensors

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“…[1−3] . Generally speaking, different sorts of measurements including time difference of arrival (TDOA) [4] , frequency difference of arrival (FDOA) [4] , angle of arrival (AOA) [5] , or their combinations, can be used to conduct source localization in a passive and uncooperative way. Each measurement defines a geometrical curve of possible source locations.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Algebraic Solution for Moving Source Localization from Quadruple Hybrid Measurements

Ding

Zhao

2022

Chinese J of Electronics

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This paper deals with the 3-D moving source localization using time difference of arrival (TDOA), frequency difference of arrival (FDOA), angle of arrival (AOA) and AOA rate measurements, gathered from a set of spatially distributed receivers. The TDOA, FDOA, AOA and AOA rate measurement equations were firstly established according to the space geometric relationship of the source relative to the receivers. Then an efficient closed-form algorithm for source position and velocity estimation from the quadruple hybrid measurements was proposed. The proposed algorithm converts the nonlinear measurement equations into a linear set of equations, which can then be used to estimate the source position and velocity applying weighted least square (WLS) minimization. In contrast to existing two-stage WLS algorithms, the proposed algorithm does not introduce any nuisance parameters and requires merely onestage, which enables for source localization with the fewest receivers necessary. Theoretical accuracy analysis shows that the proposed algorithm reaches the Cramer-Rao lower bound, and simulation studies corroborate the efficiency and superiority of the proposed algorithm over other algorithms.

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Research on the Development Strategy of Guangfu Traditional Music Culture Based on Big Data

Yang

2020

2020 2nd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)

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Two‐Dimensional DOA Estimation Using One‐Dimensional Search for Spherical Arrays

Cited by 3 publications

References 19 publications

On Difference Pattern Synthesis for Spherical Sensor Arrays

On Difference Pattern Synthesis for Spherical Sensor Arrays

An Efficient Algebraic Solution for Moving Source Localization from Quadruple Hybrid Measurements

Research on the Development Strategy of Guangfu Traditional Music Culture Based on Big Data

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