DOA Estimation of Noncircular Signals Under Impulsive Noise Using a Novel Empirical Characteristic Function-Based MUSIC

Asghari, Mohsen; Zareinejad, Mohammad; Rezaei, Seyed Mehdi; Amindavar, Hamidreza

doi:10.1007/s00034-022-02289-9

Cited by 4 publications

(1 citation statement)

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“…However, most studies on DOA estimation have been performed assuming white Gaussian conditions of ideal additive noise. As a result, many algorithms have been developed, including super-resolution DOA estimation algorithms such as Multiple Signal Classification (MUSIC) [4][5][6] and Signal Parameter Estimation Using Rotational Invariance Techniques (ESPRIT) [7][8][9]. In practical environments, many signals and noises do not exactly follow Gaussian distributions.…”

Section: Introductionmentioning

confidence: 99%

A Novel DOA Estimation Algorithm Based on Robust Mixed Fractional Lower-Order Correntropy in Impulsive Noise

Lan,

Hu,

Zhang

et al. 2024

Electronics

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The estimation of direction of arrival (DOA) is paramount in the realm of practical array signal processing systems. Nevertheless, traditional estimation methods often rely heavily on the Gaussian noise assumption, rendering them ineffective in achieving high-precision estimates in environments plagued by strong impulsive noise. To address this challenge, this paper introduces a novel DOA estimation algorithm that leverages mixed fractional lower-order correntropy (MFLOCR) in the context of Alpha-stable distributed impulsive noise. Correntropy is used as a measure of the similarity of the signals, using a Gaussian function to smooth extreme values and provide greater robustness against impulsive noise. By utilizing diverse kernel lengths to jointly regulate the kernel function, the concept of correntropy is expanded and implemented in the fractional lower-order moment (FLOM) algorithm for received signals. Subsequently, the MFLOCR is derived by adjusting the resulting form of correntropy. Finally, an enhanced DOA estimation algorithm is proposed that combines the MFLOCR operator with the MUSIC algorithm, specifically tailored for impulsive noise environments. Furthermore, a proof of boundedness is provided to validate the effectiveness of the proposed approach in such noisy conditions. Simulation experiments confirmed that the proposed method outperforms existing DOA estimation methods in the context of intense impulsive noise, a low generalized signal-to-noise ratio (GSNR), and a smaller number of snapshots.

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