A DOA Estimation Method With Kronecker Subspace for Coherent Signals

Inoue, Masatoshi; Hayashi, Kazunori; Mori, Hiroki; Nabetani, Toshihisa

doi:10.1109/lcomm.2018.2870824

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…In the field of AOA positioning, reference [ 23 ] proposed an arrival direction estimation method based on the Kronecker subspace. This method employs a new formula using Khatri–Rao product array processing and can handle up to N − 1 coherent signals using an array with N sensors.…”

Section: Related Workmentioning

confidence: 99%

5G Indoor Positioning Error Correction Based on 5G-PECNN

Yang,

Zhang,

et al. 2024

Sensors

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With the development of the mobile network communication industry, 5G has been widely used in the consumer market, and the application of 5G technology for indoor positioning has emerged. Like most indoor positioning techniques, the propagation of 5G signals in indoor spaces is affected by noise, multipath propagation interference, installation errors, and other factors, leading to errors in 5G indoor positioning. This paper aims to address these issues by first constructing a 5G indoor positioning dataset and analyzing the characteristics of 5G positioning errors. Subsequently, we propose a 5G Positioning Error Correction Neural Network (5G-PECNN) based on neural networks. This network employs a multi-level fusion network structure designed to adapt to the error characteristics of 5G through adaptive gradient descent. Experimental validation demonstrates that the algorithm proposed in this paper achieves superior error correction within the error region, significantly outperforming traditional neural networks.

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Section: Related Workmentioning

confidence: 99%

5G Indoor Positioning Error Correction Based on 5G-PECNN

Yang,

Zhang,

et al. 2024

Sensors

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“…Vectorising R yields [8]

boldr = normalv normale normalc (boldR) = \bar{B} normalv normale normalc ({boldR}_{s}) + normalv normale normalc (boldQ)

where

\bar{B} = {boldA}^{*} \otimes boldA \in {double-struckC}^{M^{2} \times L^{2}}

is the virtual steering matrix. vec(•) and ⊗ denotes the column‐by‐column vectorisation and Kronecker product, respectively.…”

Section: Signal Modelmentioning

confidence: 99%

“…To alleviate the negative impacts of coherent signals, emerging techniques include those based on sparse reconstructions [5][6][7][8]. In [5] a ℓ 1 -SVD method combining singular value decomposition (SVD) and mixed norm constraint is proposed to exploit group sparsity.…”

Section: Introductionmentioning

confidence: 99%

Sparse representation based direction‐of‐arrival estimation in nonuniform noise via tail minimisation

Zeng

Fang

et al. 2021

IET Radar Sonar & Navi

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DOA estimation based on sparse representation in a non-uniform noise environment is proposed using a tail minimisation technique. The noise-free covariance matrix is modelled as diagonal and off-diagonal components relating to incoming signals. In order to combat coherent signals, the auto-and cross-correlation entries of the incident signal are considered separately by using the least square (LS) criterion. Subsequently, from the perspective of difference co-array a vectorised covariance matrix is obtained to emulate the received signal similar to the output of a single snapshot. The effect of non-uniform noise is significantly mitigated via a removal operation. A tail minimisation scheme that is capable of deriving sharper spectra peaks is then applied to detect the DOAs. The proposed approach has the advantages of a larger virtual array aperture beneficial to DOA estimations of uncorrelated and/or coherent signals. The technique can suppress false spectral peaks to a certain extent and is also computationally effective. Numerical simulation verifies the effectiveness of the proposed method.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…In [12], an MSE-based regularization method is proposed to detect the number of coherent signals and estimate the DOAs. Based on the KR subspace approach, Masaaki et al proposed the Kronecker-MUSIC method [13], which can cope with N − 1 coherent source signals by means of N sensors. However, the source signals are restricted to being quasistationary, thus limiting the widespread application of this method.…”

Section: Introductionmentioning

confidence: 99%

A Novel ULA-Difference-Coarray-Based DOA Estimation Method for General Coherent Signals

Chen

Liu

2020

Mathematical Problems in Engineering

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In this article, a difference-coarray-based direction of arrival (DOA) method is introduced, which utilizes the uniform linear array (ULA) in a novel fashion to address the problem of DOA estimation for coherent signals. Inspired by the coarray-based estimators employed in cases of sparse arrays, we convert the sample covariance matrix of the observed signals into the difference coarray domain and process the signals using a spatial smoothing technique. The proposed method exhibits good accuracy and robustness in both the uncorrelated and coherent cases. Numerical simulations verify that the ULA difference coarray- (UDC-) based method can achieve good DOA estimation accuracy even when the SNR is very low. In addition, the UDC-based method is insensitive to the number of snapshots. Under extremely challenging conditions, the proposed UDC-ES-DOA method is preferred because of its outstanding robustness, while the UDC-MUSIC method is suitable for most moderate cases of lower complexity. Due to its demonstrated advantages, the proposed method is a promising and competitive solution for practical DOA estimation, especially for low-SNR or snapshot-limited applications.

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A DOA Estimation Method With Kronecker Subspace for Coherent Signals

Cited by 8 publications

References 8 publications

5G Indoor Positioning Error Correction Based on 5G-PECNN

5G Indoor Positioning Error Correction Based on 5G-PECNN

Sparse representation based direction‐of‐arrival estimation in nonuniform noise via tail minimisation

A Novel ULA-Difference-Coarray-Based DOA Estimation Method for General Coherent Signals

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