A One-Snapshot Localization Algorithm for Mixed Far-Field and Near-Field Sources

Huang, Zhikai; Wang, Wei; Dong, Fuwang; Wang, Di

doi:10.1109/lcomm.2020.2977002

Cited by 14 publications

(11 citation statements)

References 28 publications

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“…Different from existing methods, we present a low-complexity one-bit DOA estimation method based on discrete Fourier transform (DFT) in this paper. DFT has been widely used in single-snapshot DOA estimation of MULA in recent years [16,36,37]. Theoretical study and simulation confirmation demonstrates that even after one-bit quantization of the single snapshot data received by MULA, the initial DOA estimation can be obtained from the DFT spectrum's peak location.…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity One-Bit DOA Estimation for Massive ULA with a Single Snapshot

Fan

Wang

et al. 2022

Remote Sensing

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Existing one-bit direction of arrival (DOA) estimate methods based on sparse recovery or subspace have issues when used for massive uniform linear arrays (MULAs), such as high computing cost, estimation accuracy depending on grid size, or high snapshot-number requirements. This paper considers the low-complexity one-bit DOA estimation problems for MULA with a single snapshot. Theoretical study and simulation results demonstrate that discrete Fourier transform (DFT) can be applied to MULA for reliable initial DOA estimation even when the received data are quantized by one-bit methods. A precise estimate is then obtained by searching within a tiny area. The resulting method is called one-bit DFT. This method is straightforward and simple to implement. High-precision DOA estimates of MULA can be obtained with a single snapshot, and the computational complexity is significantly less than that of existing one-bit DOA estimation methods. Moreover, the suggested method is easily extensible to multiple snapshot scenarios, and increasing the number of snapshots can further improve estimation precision. Simulation results show the effectiveness of the one-bit DFT method.

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Section: Introductionmentioning

confidence: 99%

Low-Complexity One-Bit DOA Estimation for Massive ULA with a Single Snapshot

Fan

Wang

et al. 2022

Remote Sensing

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show abstract

Section: Introductionmentioning

confidence: 99%

“…To solve the drawback of the MUSIC-based methods, the NFSLs without the knowledge of the number of sources were proposed [13][14][15][16]. In [13], the discrete fractional Fourier transform (DFrFT) based SL was proposed. The DFrFT was designed for one snapshot, which was unsuitable for low signal-to-noise power ratio (SNR) scenarios.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Near-Field Source Localization Without a Priori Knowledge of the Number of Sources

et al. 2022

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In this paper, we propose a high resolution grid-based deep learning source localization that precisely estimates the locations of near-field sources without a priori knowledge of the number of sources. The proposed method consists of a principal component analysis network (PCAnet) and a spatial spectrum network (Sp2net). The proposed PCAnet calculates the noise spaces of the received signals by convolutional layers without a priori knowledge or the estimation of the number of sources and has the lower complexity than eigenvalue decomposition (EVD). The proposed Sp2net calculates the spatial spectrum with a linear layer from the output of the PCAnet and classifies dense location grids with a convolutional neural network (CNN). From the spatial spectrum, this paper also proposes an activation function to enlarge the values at the grid points where the near-field sources exist, which are differentiable for all input values. Then, the direction of arrivals (DOAs) and the ranges of the near-field sources are estimated with high resolution. Computer simulations demonstrated that the proposed method had better DOA and range estimation performances than those of the conventional methods.

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“…This method shows robustness at low SNR and does not demand any matrix decomposition. Furthermore, a onesnapshot mixed far-field and near-field localization algorithm based on discrete fractional Fourier transform is developed [17,18]. This method simultaneously estimates and classifies mixed sources.…”

Section: Introductionmentioning

confidence: 99%

“…There are existing methods reported in the literature for the estimation of DoA such as estimation of subspace rotational in variance technology (ESPRIT) algorithm, multiple signal classification (MUSIC), MUSIC group delay [11,18,19], the steered response power with phase transform (SRP-PHAT) [30][31][32][33][34][35], generalized cross-correlation (GCC) [36], adaptive eigenvalue decomposition [37], onedimensional MUSIC [38], and order aware method [39]. MUSIC has been applied to the SH structure in NF (MUSIC-SH) [33,[40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Source Localization of EM Waves in the Near‐Field of Spherical Antenna Array in the Presence of Unknown Mutual Coupling

Famoriji

Shongwe

2021

Wireless Communications and Mobile Computing

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To obtain an antenna array with isotropic radiation, spherical antenna array (SAA) is the right array configuration. The challenges of locating signals transmitted within the proximity of antenna array have been investigated considerably in the literature. However, near-field (NF) source localization of signals has hitherto not been investigated effectively using SAA in the presence of mutual coupling (MC). MC is another critical problem in antenna arrays. This paper presents an NF range and direction-of-arrival (DoA) estimation technique via the direction-independent and signal invariant spherical harmonics (SH) characteristics in the presence of mutual coupling. The energy of electromagnetic (EM) signal on the surface of SAA is captured successfully using a proposed pressure interpolation approach. The DoA estimation within the NF region is then calculated via the distribution of pressure. The direction-independent and signal invariant characteristics, which are SH features, are obtained using the DoA estimates in the NF region. We equally proposed a learning scheme that uses the source activity detection and convolutional neural network (CNN) to estimate the range of the NF source via the direction-independent and signal invariant features. Considering the MC problem and using the DoA estimates, an accurate spectrum peak in the multipath situation in conjunction with MC and a sharper spectrum peak from a unique MC structure and smoothing algorithms are obtained. For ground truth performance evaluation of the SH features within the context of NF localization, a numerical experiment is conducted and measured data were used for analysis to incorporate the MC and consequently computed the root mean square error (RMSE) of the source range and NF DoA estimate. The results obtained from numerical experiments and measured data indicate the validity and effectiveness of the proposed approach. In addition, these results are motivating enough for the deployment of the proposed method in practical applications.

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A One-Snapshot Localization Algorithm for Mixed Far-Field and Near-Field Sources

Cited by 14 publications

References 28 publications

Low-Complexity One-Bit DOA Estimation for Massive ULA with a Single Snapshot

Low-Complexity One-Bit DOA Estimation for Massive ULA with a Single Snapshot

Deep Learning-Based Near-Field Source Localization Without a Priori Knowledge of the Number of Sources

Source Localization of EM Waves in the Near‐Field of Spherical Antenna Array in the Presence of Unknown Mutual Coupling

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