Median-Difference Correntropy for DOA under the Impulsive Noise Environment

Abstract

The source localization using direction of arrival (DOA) of target is an important research in the field of Internet of Things (IoTs). However, correntropy suffers the performance degradation for direction of arrival when the two signals contain the similar impulsive noise, which cannot be detected by the difference between two signals. This paper proposes a new correntropy, called the median-difference correntropy, which combines the generalized correntropy and the median difference. The median difference is defined as the deviation between the sampling value and the median of the signal, and it intuitively reflects the abnormality of impulsive noise. Then, the median difference is combined with the generalized correntropy to form a new weighting factor that can effectively suppress the amplitude level of impulsive noise. To improve the robustness of the algorithm, an adaptive kernel size is also integrated into the weighting factor to obtain the optimal local feature. The influence of adaptive kernel sizes on the proposed algorithm is simulated, and the comparison between three typical direction-of-arrival estimation algorithms is conducted. The results show that the accuracy of the median-difference correntropy is significantly superior to the correntropy-based correlation and the phased fractional lower-order moment for a wide range of alpha-stable distribution noise environments.

An efficient direction‐of‐arrival estimation of multipath signals with impulsive noise using satin bowerbird optimization‐based deep learning neural network

“…The weight is fixed in the conventional beam‐forming method, so it could not adjust to disparate noise along with interference (Qiang et al, 2019). The basic subspace algorithms are (1) the multiple signal classification (MUSIC) algorithms and (2) the estimation method of signal parameters via rotational invariance techniques (ESPRIT); in this, MUSIC is the noise subspace algorithm's representative, and ESPRIT is the signal subspace algorithm's representative (Ma et al, 2019). For assessing the uncorrelated signal's DOAs accurately, both the MUSIC and ESPRIT algorithms are utilized.…”

“…It is a 2‐Dimensional (2D) DOA (2D DOAs) (i.e., elevation and azimuth angles) that people are usually most concerned about in practical applications. In 1‐Dimensional (1D) circumstances, estimators like MUSIC and estimation of signal parameter through rotation invariance technique (ESPRIT) have been studied; moreover, extended to 2D circumstances (Ma et al, 2019; Qiang et al, 2019). The complexity of the DOA estimation process is severely affected by the array geometry owing to the rise in the dimensionality of the 2D DOA estimation issue (Gan & Luo, 2013); in addition, the pair matching (i.e., association or alignment) of the estimated elevation and azimuth angles is generally essential.…”

A Robust DOA Tracking Method Using a Nested Array in Impulse Noise

“…e above methods are derived in Gaussian noise, whereas in real world, there exists some impulse noises, including the lightning and the low-frequency atmospheric noise, which can be described well as symmetric α-stable (SαS) distribution [9], and these forms of impulse noise exist a long tail, which deteriorates the performance of the existing methods. Many methods, including fractional lower-order moment [10], correntropy technique [11,12], in nite norm [13], and kernel method [14,15], have been proposed to achieve accurate estimates in impulse noise. DOA tracking in impulse noise has also made great progress in recent years.…”

A Robust Multi Sample Compressive Sensing Technique for DOA Estimation Using Sparse Antenna Array

“…It can be defined by alpha stable distribution [6]. To solve the impulsive noise median difference correntropy (MDCO) algorithm is suggested in [7]. The MDCO derives the weighting factor from the correntropy criterion, which suppress the impulsive noise.…”