DOA estimation for far-field sources in mixed signals with mutual coupling and gain-phase error array

Zhen, Jiaqi; Guo, Baoyu

doi:10.1186/s13638-018-1324-x

Cited by 4 publications

(2 citation statements)

References 33 publications

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“…The simulation results in this work show that STCA can achieve better performance than other symmetric nested arrays under the same array sensors and MC effects. In [227], an effective approach to resolve FFSs in mixed signals (FM) using SULA is presented, which in addition to considering MC effects, gain/phase imperfections are also addressed. The issue of array gain/phase uncertainties for mixed sources is also addressed in [228].…”

Section: E the Effect Of Antenna Mutual Couplingmentioning

confidence: 99%

A Comprehensive Review of Direction-of-Arrival Estimation and Localization Approaches in Mixed-Field Sources Scenario

Molaei,

Zakeri,

Andargoli

et al. 2024

IEEE Access

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Direction-of-arrival (DOA) estimation plays a crucial role in array signal processing across various domains, including radar, sonar, wireless communications, and seismic exploration. However, traditional DOA techniques often assume either far-field (FF) or near-field (NF) propagation, limiting their applicability in scenarios involving mixed-field sources. DOA estimation and localization in scenarios involving mixed NF and FF sources is a complex and dynamic field that has garnered significant research attention in recent years. This multifaceted and evolving area holds promise for addressing challenges in radar, wireless communications, and acoustic sensing applications. This review paper provides a comprehensive overview of the methodologies, techniques, and advancements in this domain. We categorize existing methodologies, discussing their advantages and limitations. Furthermore, we delve into the mathematical modeling of mixed-field sources and essential signal processing techniques for parameter estimation. Special attention is given to technical issues such as aperture loss, computational complexity, and hardware considerations. The paper discusses the various sources of noise in the mentioned scenario and highlights the importance of modeling noise accurately for effective estimation. It also explores different scenarios and assumptions considered in the literature, ranging from non-Gaussian and nonstationary noise environments to scenarios involving multipath propagation and unknown mutual coupling effects. A detailed examination of the statistical approaches used in DOA estimation and localization reveals a diverse range of methods, including higher-order statistics and second-order statistics, each with its own advantages and applications. A comparative evaluation of various approaches highlights their performance in terms of estimation accuracy, resolution, aperture loss and computational efficiency. This provides insights into the trade-offs involved in choosing between different approaches. The review also identifies promising future research directions, such as the exploration of advanced signal processing techniques like compressive sensing and deep learning, exact NF modeling, estimation based on one-bit measurements, the integration of polarization diversity, employing metasurface antennas, tracking parameters, and the utilization of full-wave or experimental data for a more realistic representation of the challenges. By reviewing advances in methodologies and techniques, as well as outlining future research directions aimed at addressing the complexities of mixed-field scenarios, this paper paves the way for the development of more robust and reliable localization systems capable of handling real-world complexities.

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Section: E the Effect Of Antenna Mutual Couplingmentioning

confidence: 99%

A Comprehensive Review of Direction-of-Arrival Estimation and Localization Approaches in Mixed-Field Sources Scenario

Molaei,

Zakeri,

Andargoli

et al. 2024

IEEE Access

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“…Generally, conventional calibration methods lack adaptability and are challenging to model accurately [35][36][37]. CNN-based methods are data-driven, and therefore they do not require prior assumptions about array imperfections.…”

Section: Array Imperfections and Transfermentioning

confidence: 99%

Novel Approach to 2D DOA Estimation for Uniform Circular Arrays Using Convolutional Neural Networks

Chen

Joo

2021

International Journal of Antennas and Propagation

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This paper presents a novel efficient high-resolution two-dimensional direction-of-arrival (2D DOA) estimation method for uniform circular arrays (UCA) using convolutional neural networks. The proposed 2D DOA neural network in the single source scenario consists of two levels. At the first level, a classification network is used to classify the observation region into two subregions (0°, 180°) and (180°, 360°) according to the azimuth angle degree. The second level consists of two parallel DOA networks, which correspond to the two subregions, respectively. The input of the 2D DOA neural network is the preprocessed UCA covariance matrix, and its outputs are the estimated elevation angle to be modified by postprocessing and the estimated azimuth angle. The purpose of the postprocessing is to enhance the proposed method’s robustness to the incident signal frequency. Moreover, in the inevitable array imperfections scenario, we also achieve 2D DOA estimation via transfer learning. Besides, although the proposed 2D DOA neural network can only process one source at a time, we adopt a simple strategy that enables the proposed method to estimate the 2D DOA of multiple sources in turn. Finally, comprehensive simulations demonstrate that the proposed method is effective in computation speed, accuracy, and robustness to the incident signal frequency and that transfer learning could significantly reduce the amount of required training data in the case of array imperfections.

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An auxiliary source-based near field source localization method with sensor position error

Zhang

Yang

et al. 2023

Signal Processing

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DOA estimation for far-field sources in mixed signals with mutual coupling and gain-phase error array

Cited by 4 publications

References 33 publications

A Comprehensive Review of Direction-of-Arrival Estimation and Localization Approaches in Mixed-Field Sources Scenario

A Comprehensive Review of Direction-of-Arrival Estimation and Localization Approaches in Mixed-Field Sources Scenario

Novel Approach to 2D DOA Estimation for Uniform Circular Arrays Using Convolutional Neural Networks

An auxiliary source-based near field source localization method with sensor position error

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