An Exact Model-Based Method for Near-Field Sources Localization with Bistatic MIMO System

Singh, Parth Raj; Wang, Yide; Chargé, Pascal

doi:10.3390/s17040723

Cited by 9 publications

(4 citation statements)

References 25 publications

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“…Unlike the majority of existing approaches that are based on the approximate model, in [181][182][183], methods for NFSs localization are presented that consider the exact wavefront model. For the mixed sources scenario, there is a significant need for a comprehensive study of the errors caused by employing the approximate NF model, along with a solution to the problem when the exact model is considered [184].…”

Section: Approximate Near-field Modelmentioning

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: Approximate Near-field Modelmentioning

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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show abstract

“…However, to the best of our knowledge, most near-field localization methods do not consider critical effects that are not prominent in the near-field on the signal model. Instead, they use a so-called “ exact model ” that is actually based on the behaviour of far-field electromagnetic waves 16 , 17 , 30 , 31 . The phase of the far-field electromagnetic wave is proportional to the product of the distance and inverse of the wavelength 32 .…”

Section: Introductionmentioning

confidence: 99%

“…A limited number of works, such as 7 , 16 , 17 , 30 , have highlighted algorithms based on the exact model. However, these algorithms are only useful for particular antenna configurations, such as bistatic multiple-input multiple-output (MIMO) structure, and are unsuitable for other antenna arrays.…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic informed data model considerations for near-field DOA and range estimates

Ebadi,

Molaei,

Abbasi

et al. 2024

Sci Rep

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Localizing sources in the near-field is one of the emerging challenges for array signal processing, which has received a great deal of attention in recent years. The development of accurate localization algorithms requires the definition of a reliable model of the received signal that takes into account all wavefront characteristics, such as angle, range, and polarization, as well as electromagnetic effects, such as mutual coupling between antennas and the amplitude and phase behaviour of electromagnetic wavefronts. A system model that considers the electromagnetic-informed wave behaviour effects, independent of the type of receiver antennas, array structure, degree of correlation of sources signals and other electromagnetic effects, is considered an “ exact model ” in the literature. However, due to the mathematical complexity of this modeling approach, simplifications using several approximations are conventionally used. For instance, the phase of the exact model is approximated using the Fresnel approximation, while the magnitude of the exact model is simplified by assuming equal distances between the source and all elements in the array. In this work, we evaluate the accuracy of a localization algorithm, the multiple signal classification (MUSIC), using the exact and approximated models in the near-field region. Through a series of simulations, we demonstrate that the localization algorithm designed based on the electromagnetic-informed exact model outperforms the one designed using the approximated model. We also show that considering electromagnetic factors in the system model through the exact model results in a 13% improvement in the direction of arrival (DOA) root mean square error (RMSE) and a 57.7% improvement in range RMSE at signal-to-noise ratio (SNR) of 15 dB.

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“…In the literature, there are two main approaches dealing with the exact signal model in near-field source localization. One is to directly estimate the DOA and the range with the exact model, including the maximum likelihood estimator (MLE) method [20], the Fourier transform method [21], the parallel factor (PARAFAC) decomposition method [22], and the cumulant based method [17]. However, direct estimation methods generally require multi-dimensional or 1-D search, or multiple decomposition, which brings much computational load.…”

Section: Introductionmentioning

confidence: 99%

A search-free near-field source localization method with exact signal model

Pan

Raj²,

Men

2021

J. of Syst. Eng. Electron.

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Most of the near-field source localization methods are developed with the approximated signal model, because the phases of the received near-field signal are highly non-linear. Nevertheless, the approximated signal model based methods suffer from model mismatch and performance degradation while the exact signal model based estimation methods usually involve parameter searching or multiple decomposition procedures. In this paper, a search-free near-field source localization method is proposed with the exact signal model. Firstly, the approximative estimates of the direction of arrival (DOA) and range are obtained by using the approximated signal model based method through parameter separation and polynomial rooting operations. Then, the approximative estimates are corrected with the exact signal model according to the exact expressions of phase difference in near-field observations. The proposed method avoids spectral searching and parameter pairing and has enhanced estimation performance. Numerical simulations are provided to demonstrate the effectiveness of the proposed method.

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An Exact Model-Based Method for Near-Field Sources Localization with Bistatic MIMO System

Cited by 9 publications

References 25 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

Electromagnetic informed data model considerations for near-field DOA and range estimates

A search-free near-field source localization method with exact signal model

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