A Simplified DOA Estimation Method Based on Correntropy in the Presence of Impulsive Noise

Tian, Quan; Qiu, Tianshuang; Ma, Jitong; Li, Jingchun; Li, Rong

doi:10.1109/access.2018.2879386

Cited by 14 publications

(13 citation statements)

References 49 publications

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“…and: However, many signals and noises encountered in practice are decidedly non-Gaussian, for example, low-frequency atmospheric noise, underwater acoustic signals, and many types of human-made noises. An important class of impulse noise encountered in DOA estimation can be modelled by α-stable distribution [14], which is an extremely flexible modelling tool. It is pity that there exists no closed-form expression for the PDF of α-stable distribution except for Gaussian and Cauchy distributions.…”

Section: Noise Modelmentioning

confidence: 99%

“…β (β = 0 in this paper) determines the sign and degree of asymmetry about µ (µ = 0 in this paper), which is similar to the mean of Gaussian distribution. For details about the α-stable distribution, see Tian et al [14] and the references therein. As shown in Figure 3, the time-domain waveform of α-stable distribution with α = 1.0 has more impulsive outliers compared with that in Figure 1.…”

Section: Noise Modelmentioning

confidence: 99%

“…where λ denotes the wavelength of the carrier. The single snapshot M × 1 observation vector can also be expressed as [14]:…”

Section: Problem Formulationmentioning

confidence: 99%

“…Therefore, its computational complexity is extremely high. In the presence of non-Gaussian noise, conventional 1D-DOA estimation methods of stationary EM signals include FLOM [11], PFOM [12], CRCO [13], COBU [14], etc. Besides the spectral peak search, they also need a complex preprocessing stage and require high computational complexity.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Noise Modelmentioning

confidence: 99%

Section: Noise Modelmentioning

confidence: 99%

“…where λ denotes the wavelength of the carrier. The single snapshot M × 1 observation vector can also be expressed as [14]:…”

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Chen

Joo

2020

Sensors

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“…Thus, the characteristic function of the SαS can be expressed as ϕ (t) = exp (−γ |t| α ), and its pdf could be given by f (s) = (1/2π ) e −γ |t| α dt (32) Let g = 2/π 3 +∞ −∞ e −γ |t| α dt; we can then obtain g < +∞. The derivation can be referred to in [24]. The second-order moment of the derf is thus bounded.…”

Section: The Error Function and Its Derivativementioning

confidence: 99%

A Novel DOA Estimation for Distributed Sources in an Impulsive Noise Environment

et al. 2020

Self Cite

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Direction of arrival (DOA) estimation has emerged as a promising technology for many military and civilian applications, such as radio astronomy and wireless communications. However, it remains very challenging to estimate DOA for distributed sources within impulsive noise environments. As is widely known, the utilization of the subspace technology in array signal processing can enable high-resolution DOA estimation. In many instances, most of these algorithms assume that the noise is Gaussian. Considering the impulsive noise environments, the derivative of the error function is derived as the iteratively weighted factor in the objective function of low-rank decomposition. Two iterative algorithms are proposed to compute the signal subspace to implement DOA estimation for coherently distributed sources. To further improve the DOA estimation accuracy and increase the number of estimated sources, the virtual data samples are reconstructed from the extended co-prime array. Simulation results confirm that the two proposed impulsive noise suppression algorithms provide better performance than the existing algorithms. The virtual data samples that are reconstructed from the extended co-prime array can be utilized as the effective inputs for subspace-like algorithms.

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Parameter estimation for coherently distributed noncircular sources under impulsive noise environments

Chao

Qiu

Tian

et al. 2020

SIViP

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A Simplified DOA Estimation Method Based on Correntropy in the Presence of Impulsive Noise

Cited by 14 publications

References 49 publications

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

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

A Novel DOA Estimation for Distributed Sources in an Impulsive Noise Environment

Parameter estimation for coherently distributed noncircular sources under impulsive noise environments

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