Sparse Reconstruction Based Robust Near-Field Source Localization Algorithm

Li, Sen; Li, Bing; Lin, Bin; Tang, Xiaofang; He, Rongxi

doi:10.3390/s18041066

Cited by 9 publications

(5 citation statements)

References 30 publications

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“…According to (20) and 22, the developed virtual steering matrix B u can be considered as a steering matrix of K mixed sources which impinge on a linear array consisting of L − u + 1 sensors. So, similar to TS-MUSIC algorithm [29], the proposed method alleviates the array aperture loss.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, sparse signal representation (SSR) framework has been employed in array signal processing. In this regard, several algorithms have been presented to localise NF sources [20,21], FF sources [22][23][24][25], and mixed sources [26][27][28]. Similar to subspace-based methods, SSR-based ones exhibit some advantages such as high resolution, and robustness to the noise.…”

Section: Introductionmentioning

confidence: 99%

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Generalised two stage cumulants‐based MUSIC algorithm for passive mixed sources localisation

Ebrahimi

Abutalebi

Karimi³

2019

IET signal process.

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In this study, the authors address the problem of passive mixed near-field and far-field sources localisation using a uniform linear array (ULA) in which the signals received by the array may come from mixed sources. This study presents a new two stage cumulant-based multiple signal classification (MUSIC) algorithm for passive source localisation using fourth-order cumulants of a ULA data. The significant characteristic of the proposed algorithm is that it constructs a new special cumulant matrix to acquire more information of signals received by a ULA. Consequently, the proposed algorithm gives high direction of arrival (DOA) and range estimation accuracy, and alleviates the array aperture loss. Monte Carlo simulations are established to verify the effectiveness of the proposed method in increasing direction of arrival and range estimation accuracies.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Generalised two stage cumulants‐based MUSIC algorithm for passive mixed sources localisation

Ebrahimi

Abutalebi

Karimi³

2019

IET signal process.

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“…Based on the rank reduction (RARE) principle [220,221], in [222], a localization algorithm called two-stage RARE (TSRARE) is presented for mixed FFSs and NFSs in the presence of unknown MC. The TSRARE algorithm only requires SOS and 1-D spectral search.…”

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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“…In the traditional numerical calculations of the near field target's azimuth‐range parameters is studied on the basis of the TESCL [ 21‐25 ] .…”

Section: Implementation Of the Proposed Algorithmmentioning

confidence: 99%

An Accurate Near‐Field Distance Estimation Differential Algorithm

Yan

Tao

Chang

2022

Chinese J of Electronics

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The triangular geometry is the basis of near field array accurate distance estimation algorithms. The Fisher expression of traditional distance estimation is derived by utilizing the Taylor series. To improve convergence rate and estimation accuracy, a novel iterative distance estimation algorithm is proposed with differential equations based on the triangular geometry. Firstly, its convergence performance is analysed in detail. Secondly, the selection of the initial value and the number of iterations are respectively studied. Thirdly, compared with the traditional estimation algorithms by utilizing the Fisher approximation, the proposed algorithm has a higher convergence rate and estimation accuracy. Moreover, its pseudocode is presented. Finally, the experiment results and performance analysis are provided to verify the effectiveness of the proposed algorithm.

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Sparse Reconstruction Based Robust Near-Field Source Localization Algorithm

Cited by 9 publications

References 30 publications

Generalised two stage cumulants‐based MUSIC algorithm for passive mixed sources localisation

Generalised two stage cumulants‐based MUSIC algorithm for passive mixed sources localisation

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

An Accurate Near‐Field Distance Estimation Differential Algorithm

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