A Low Complexity DOA Estimation Method of CD Sources in Impulsive Noise

Direction of arrival (DOA) estimation, as the main technology of passive radio monitoring and positioning, has been deeply investigated. However, the DOA for distributed sources is challenging to estimate in environments with impulsive noise. Although many methods have been proposed for DOA estimati...

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Bibliographic Details
Published in:IEEE access Vol. 9; pp. 142857 - 142868
Main Authors: Cai, Ruiyan, Tian, Quan, Qiu, Tianshuang
Format: Journal Article
Language:English
Published: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
alpha-stable distribution
Approximation algorithms
Arrays
Complexity
Computational complexity
Direction of arrival
Direction-of-arrival estimation
distributed sources
DOA estimation
Estimation
impulsive noise
Kernel functions
Manifolds
Multiple signal classification
Noise
Optimization
Random noise
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:Direction of arrival (DOA) estimation, as the main technology of passive radio monitoring and positioning, has been deeply investigated. However, the DOA for distributed sources is challenging to estimate in environments with impulsive noise. Although many methods have been proposed for DOA estimation, most of them assume that array output signals contain Gaussian noise. Therefore, the performance of these methods is often poor for alpha-stable distributed impulsive noise. Furthermore, subspace-based DOA estimation methods for distributed sources require a two-dimensional (2D) peak search, which increases the consumption of system computing resources. In this paper, a Q-function-based kernel function is proposed, and its properties are derived. On this basis, a novel DOA estimation method is proposed for coherently distributed (CD) sources in impulsive noise. To reduce computational complexity, a Lagrangian quadratic optimization function is derived by approximating the generalized array manifold of the CD source. By solving this optimization function, a 2D peak search can be reduced to several one-dimensional (1D) peak searches. The simulation results illustrate that the accuracy and robustness of the proposed method outperform those of existing methods.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3121391