SVM-Based DOA Estimation with Classification Optimization

Faye, Andre; Ndaw, J. D.; Sène, Moustapha

doi:10.1109/telfor.2018.8611827

Cited by 8 publications

(3 citation statements)

References 4 publications

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“…Therefore, different from previous approaches, a high-resolution SSE algorithm is used here. In recent years, numerous SSE algorithms have been proposed, including the multiple signal classification (MUSIC) algorithm, estimation of signal parameters via rotational invariant technique (ESPRIT) algorithm and their variants, principle of maximum entropy power spectrum, sparse Bayesian learning, support vector machine, and discrete Fourier transform-based methods [26][27][28][29][30]. This subsection presents the MUSIC algorithm, which is classical and facile to implement.…”

Section: Ssementioning

confidence: 99%

Covariance Matrix Reconstruction via a Subspace Method and Spatial Spectral Estimation for Robust Adaptive Beamforming

Cao

et al. 2022

Wireless Communications and Mobile Computing

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Several robust adaptive beamforming (RAB) algorithms based on interference plus noise covariance matrix (INCM) reconstruction have been recently proposed, which can enhance the robustness of beamforming algorithms when certain mismatches occur in the model. However, some approaches ignore the resolution of the Capon spectral estimator (CSE), leading to reconstruction errors. This paper proposes a novel RAB algorithm formulated using the subspace projection method and spatial spectral estimation (SSE), which is named INCM-SSE. First, without using the CSE, the subspace projection matrix (SPM) is obtained through the integral of the angular sector where the signal of interest (SOI) is located. Subsequently, after estimating the direction of arrival (DOA) of incident signals using the multiple signal classification (MUSIC) algorithm, we project the sample covariance matrix (SCM) onto the SPM to eliminate the SOI influence. Then, the estimation method of interference powers is derived. Moreover, the INCM is reconstructed based on the estimated powers and steering vector (SV) of interferences. The SV of the SOI is optimized by solving a quadratic convex optimization problem. The INCM-SSE algorithm not only employs SSE to improve the angular resolution but also reduces the influence of the SOI component by using SPM. Simulation results indicate that the proposed method is robust against various types of mismatches, thus achieving superior overall performance.

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Section: Ssementioning

confidence: 99%

Covariance Matrix Reconstruction via a Subspace Method and Spatial Spectral Estimation for Robust Adaptive Beamforming

Cao

et al. 2022

Wireless Communications and Mobile Computing

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“…For example, the methods introduced in [11]- [13], are merely applicable in particular antenna array patterns and may not be able to achieve good performance in other cases and correct the errors caused by unsuitable antenna array patterns. To overcome these problems, some scholars have proposed some methods that do not need to correct array errors [14], [15].…”

Section: Introductionmentioning

confidence: 99%

DOA Robust Estimation of Echo Signals Based on Deep Learning Networks With Multiple Type Illuminators of Opportunity

Liu

et al. 2020

IEEE Access

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Traditional DOA estimation algorithms have poor adaptability to antenna errors. To enhance the direction of arrival (DOA) estimation performance for moving target echo signals in the environment of multiple type illuminators of opportunity, a DOA estimation framework leveraging deep learning networks (DLN) is proposed. In the proposed framework, the DLN is divided into two main components, including linear classification networks (LCN) and convolutional neural networks (CCN). The LCN is utilized to identify the spatial subregion of received signals and divide the signals from each subregion into corresponding output modules. Then, the output of the LCN after matrix transformations will be input into multiple parallel CNNs, where DOA estimations are carried out. Extensive simulation studies are conducted, demonstrating that our proposed method has excellent estimation performance and strong universality with high estimation accuracy even under large antenna defects. INDEX TERMS Convolutional neural networks, deep learning networks, direction of arrival estimation, illuminator of opportunity, linear classification networks.

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“…In order to overcome the computational complexity of conventional methods, neural networks have been developed to DOA estimation. References [15][16][17][18] using the support vector machine (SVM) [19] and references [20][21][22][23] using the multi-layer perceptron (MLP) [24] formulate DOA estimation as a classification problem, and they lead to the discrete outputs of neural networks. References [25][26][27][28] use a radial basis function (RBF) [29] to formulate DOA estimation as a regression problem, and they lead to the continuous outputs of neural networks.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to 3D-DOA Estimation of Stationary EM Signals Using Convolutional Neural Networks

Chen

Joo

2020

Sensors

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This paper proposes a novel three-dimensional direction-of-arrival (3D-DOA) estimation method for electromagnetic (EM) signals using convolutional neural networks (CNN) in a Gaussian or non-Gaussian noise environment. First of all, in the presence of Gaussian noise, four output covariance matrices of the uniform triangular array (UTA) are normalized and then fed into four neural networks for 1D-DOA estimation with identical parameters in parallel; then four 1D-DOA estimations of the UTA can be obtained, and finally, the 3D-DOA estimation could be obtained through post-processing. Secondly, in the presence of non-Gaussian noise, the array output covariance matrices are normalized by the infinity-norm and then processed in Gaussian noise environment; the infinity-norm normalization could effectively suppress impulsive outliers and then provide appropriate input features for the neural network. In addition, the outputs of the neural network are controlled by a signal monitoring network to avoid misjudgments. Comprehensive simulations demonstrate that in Gaussian or non-Gaussian noise environment, the proposed method is superior and effective in computation speed and accuracy in 1D-DOA and 3D-DOA estimations, and the signal monitoring network could also effectively control the neural network outputs. Consequently, we can conclude that CNN has better generalization ability in DOA estimation.

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SVM-Based DOA Estimation with Classification Optimization

Cited by 8 publications

References 4 publications

Covariance Matrix Reconstruction via a Subspace Method and Spatial Spectral Estimation for Robust Adaptive Beamforming

Covariance Matrix Reconstruction via a Subspace Method and Spatial Spectral Estimation for Robust Adaptive Beamforming

DOA Robust Estimation of Echo Signals Based on Deep Learning Networks With Multiple Type Illuminators of Opportunity

A Novel Approach to 3D-DOA Estimation of Stationary EM Signals Using Convolutional Neural Networks

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