APRD-MUSIC Algorithm DOA Estimation for Reactance Based Uniform Circular Array

This paper proposes a low complexity multiple-signal-classifier (MUSIC)-based direction-of-arrival (DOA) detection algorithm for frequency-modulated continuous-wave (FMCW) vital radars. In order to reduce redundant complexity, the proposed algorithm employs characteristics of distance between adjacent arrays having trade-offs between field of view (FOV) and resolution performance. First, the proposed algorithm performs coarse DOA estimation using fast Fourier transform. On the basis of the coarse DOA estimation, the number of channels as input of the MUSIC algorithm are selected. If the estimated DOA is smaller than 30°, it implies that there is an FOV margin. Therefore, the proposed algorithm employs only half of the channels, that is, it is the same as doubling the spacing between arrays. By doing so, the proposed algorithm achieves more than 40% complexity reduction compared to the conventional MUSIC algorithm while achieving similar performance. By experiments, it is shown that the proposed algorithm despite the low complexity is enable to distinguish the adjacent DOA in a practical environment.

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“…In order to perform the decomposition of the subspaces between signal and noise, singular decomposition (SVD) is employed as follows [29]:…”

Section: Music Algorithmmentioning

confidence: 99%

Low-Complexity MUSIC-Based Direction-of-Arrival Detection Algorithm for Frequency-Modulated Continuous-Wave Vital Radar

Kim

Jin

Lee

et al. 2020

Sensors

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“…In WSNs with directional antennas, it is difficult for each node to obtain the location knowledge of other neighbors due to the directional beamforming [ 34 ]. To solve the problem of directional neighbor discovery, the complicated schemes, such as using direction-of-arrival estimation, swiveling the beam from 0 to

, were proposed [ 37 ]. Random beamforming schemes have relatively lower communication overhead than the neighbor-discovering schemes.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, we consider two kinds of typical directional antennas: Uniform Circular Array (UCA) antennas [ 37 , 42 , 45 , 46 ] and Uniform Linear Array (ULA) antennas [ 46 , 47 ], which are introduced in detail as follows.…”

Section: Antenna Modelsmentioning

confidence: 99%

On Connectivity of Wireless Sensor Networks with Directional Antennas

Wang

Dai

Zheng

et al. 2017

Sensors

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In this paper, we investigate the network connectivity of wireless sensor networks with directional antennas. In particular, we establish a general framework to analyze the network connectivity while considering various antenna models and the channel randomness. Since existing directional antenna models have their pros and cons in the accuracy of reflecting realistic antennas and the computational complexity, we propose a new analytical directional antenna model called the iris model to balance the accuracy against the complexity. We conduct extensive simulations to evaluate the analytical framework. Our results show that our proposed analytical model on the network connectivity is accurate, and our iris antenna model can provide a better approximation to realistic directional antennas than other existing antenna models.

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“…The traditional direction of arrival (DOA) estimation originates from 1960s; it is usually used in radar [1][2][3][4][5], underwater detection [6][7][8], and mobile communication [9][10][11][12][13][14][15]. Generally speaking, most of the direction finding algorithms need to know the accurate array manifold, and they are very sensitive to the errors in the sensor channels.…”

Section: Introductionmentioning

confidence: 99%

DOA estimation for far-field sources in mixed signals with mutual coupling and gain-phase error array

Zhen

Guo

2018

J Wireless Com Network

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Mutual coupling and gain-phase errors are very common in sensor channels for array signal processing, and they have serious impacts on the performance of most algorithms, especially in practical applications. Therefore, a new approach for direction of arrival (DOA) estimation of far-field sources in mixed far-field and near-field signals in the presence of mutual coupling and gain-phase imperfections is addressed. First, the model of received data with two kinds of array errors is founded. Then matrix transformation is used for simplifying the spectrum function according to the structure of the uniform linear array (ULA). At last, DOA of far-field signals can be obtained through searching the peaks of the modified spatial spectrum. The usefulness and behavior of the presented approach are illustrated by simulated experiments.

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APRD-MUSIC Algorithm DOA Estimation for Reactance Based Uniform Circular Array

Cited by 27 publications

References 17 publications

Low-Complexity MUSIC-Based Direction-of-Arrival Detection Algorithm for Frequency-Modulated Continuous-Wave Vital Radar

Low-Complexity MUSIC-Based Direction-of-Arrival Detection Algorithm for Frequency-Modulated Continuous-Wave Vital Radar

On Connectivity of Wireless Sensor Networks with Directional Antennas

DOA estimation for far-field sources in mixed signals with mutual coupling and gain-phase error array

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