Mixed Near-Field and Far-Field Sources Localization Based on Uniform Linear Array Partition

Wang, Kai; Wang, Ling; Shang, Jing-Rui; Qu, Xin

doi:10.1109/jsen.2016.2603182

Cited by 40 publications

(30 citation statements)

References 24 publications

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“…, and that of mixed near-field and far-field source localization based on uniform linear array partition (MULAP) [30] needs to form three 2M × 2Mfourth-order cumulant matrices, decompose a 4M × 4M matrix. Then using the ESPRIT to estimate the DOA with decomposing two 2(M − 1) × 2(M − 1) matrices, so it is nearly 3ð2MÞ 2 B þ 4 3 ð4MÞ 3 þ 8 3 ð2M−1Þ 3 .…”

Section: Transforming Spectrum Functionmentioning

confidence: 99%

“…For the past few years, DOA calculation for mixture far-field and near-field sources (FS and NS) has got more and more attentions and rapid development; Liang developed a two-stage MUSIC algorithm with cumulant which averts pairing parameters and loss of the aperture [29]. In [30], based on FOC and the estimation of signal parameters via rotational invariance techniques (ES-PRIT), K Wang proposed a new localization algorithm for the mixed signals. In [31,32], two localization methods based on sparse signal reconstruction are provided by Ye and B Wang respectively; they can achieve improved accuracy and resolve signals which are close to each other.…”

Section: Introductionmentioning

confidence: 99%

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

Zhen

Guo

2018

J Wireless Com Network

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Mutual coupling and gain-phase errors are very common in sensor channels for array signal processing, and they have serious impacts on the performance of most algorithms, especially in practical applications. Therefore, a new approach for direction of arrival (DOA) estimation of far-field sources in mixed far-field and near-field signals in the presence of mutual coupling and gain-phase imperfections is addressed. First, the model of received data with two kinds of array errors is founded. Then matrix transformation is used for simplifying the spectrum function according to the structure of the uniform linear array (ULA). At last, DOA of far-field signals can be obtained through searching the peaks of the modified spatial spectrum. The usefulness and behavior of the presented approach are illustrated by simulated experiments.

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Section: Transforming Spectrum Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhen

Guo

2018

J Wireless Com Network

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“…Non-data aided channel estimation based on the first-order statistics for ultra wide-band communication is studied in [32]. Source localization using order statistics theory can also be found in [33,34,35,36,37,38] for direct localization problems, and in [39] for indirect localization problems. In all these studies, however, the sensor noise is assumed to have Gaussian distribution.…”

Section: Introductionmentioning

confidence: 99%

Exploring Positive Noise in Estimation Theory

Radnosrati

Hendeby

Gustafsson

2020

IEEE Trans. Signal Process.

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Estimation of the mean of a stochastic variable observed in noise with positive support is considered. It is well known from the literature that order statistics gives one order of magnitude lower estimation variance compared to the best linear unbiased estimator (BLUE). We provide a systematic survey of some common distributions with positive support, and provide derivations of minimum variance unbiased estimators (MVUE) based on order statistics, including BLUE for comparison. The estimators are derived with or without knowledge of the hyperparameters of the underlying noise distribution. Though the uniform, exponential and Rayleigh distributions, respectively, we consider are standard in literature, the problem of estimating the location parameter with additive noise from these distribution seems less studied, and we have not found any explicit expressions for BLUE and MVUE for these cases. In addition to additive noise with positive support, we also consider the mixture of uniform and normal noise distribution for which an order statisticsbased unbiased estimator is derived. Finally, an iterative global navigation satellite system (GNSS) localization algorithm with uncertain pseudorange measurements is proposed which relies on the derived estimators for receiver clock bias estimation. Simulation data for GNSS time estimation and experimental GNSS data for joint clock bias and position estimation are used to evaluate the performance of the proposed methods.

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“…However, this kind of method needs an extra pairing algorithm to match the estimated parameters. The method proposed in [25] improves these ESPRIT-like methods by avoiding the parameter matching procedure. Based on these high-order statistics methods, a further improvement (modified ESPRIT-like) was proposed in [26], which requires only one matrix without pairing steps.…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity High-Order Propagator Method for Near-Field Source Localization

Wang

Bastard

et al. 2018

Sensors

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In this paper, an efficient high-order propagator method is proposed to localize near-field sources. We construct a specific non-Hermitian matrix based on the high-order cumulant of the received signals. With its columns and rows, we can obtain two subspaces orthogonal to all the columns of two steering matrices, respectively, with which the estimation of the directions of arrival (DOA) and ranges of near-field sources can be achieved. Different from other methods, the proposed method needs only one matrix for estimating two parameters separately, therefore leading to a smaller computational burden. Simulation results show that the proposed method achieves the same performance as the other high order statistics-based methods with a lower complexity.

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Mixed Near-Field and Far-Field Sources Localization Based on Uniform Linear Array Partition

Cited by 40 publications

References 24 publications

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

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

Exploring Positive Noise in Estimation Theory

Low-Complexity High-Order Propagator Method for Near-Field Source Localization

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