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

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 54
Author(s):  
Jianzhong Li ◽  
Yide Wang ◽  
Cédric Le Bastard ◽  
Zongze Wu ◽  
Shaoyang Men
Keyword(s):  
Hermitian Matrix ◽  
Near Field ◽  
Low Complexity ◽  
High Order ◽  
Field Source ◽  
Propagator Method ◽  
Lower Complexity ◽  
Simulation Results ◽  
Two Parameters ◽  
High Order Statistics

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.

scholarly journals Mixed-Field Source Localization Based on the Non-Hermitian Matrix

Information ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 56
Author(s):  
Minggang Mo ◽  
Zhaowei Sun
Keyword(s):  
Source Localization ◽  
Hermitian Matrix ◽  
High Order ◽  
Signal Classification ◽  
Range Estimation ◽  
Field Source ◽  
Multiple Signal Classification ◽  
Schmidt Orthogonalization ◽  
Simulation Results ◽  
Mixed Field

In this paper, an efficient high-order multiple signal classification (MUSIC)-like method is proposed for mixed-field source localization. Firstly, a non-Hermitian matrix is designed based on a high-order cumulant. One of the steering matrices, that is related only with the directions of arrival (DOA), is proved to be orthogonal with the eigenvectors corresponding to the zero eigenvalues. The other steering matrix that contains the information of both the DOA and range is proved to span the same column subspace with the eigenvectors corresponding to the non-zero eigenvalues. By applying the Gram–Schmidt orthogonalization, the range estimation can be achieved one by one after substituting each estimated DOA. The analysis shows that the computational complexity of the proposed method is lower than other methods, and the effectiveness of the proposed method is shown with some simulation results.

2-D Near Field Source Localization by Evolutionary Technique Exploiting the L-Type Geometry of Sensor Array

2017 ◽  
Vol 2 (3) ◽  
pp. 11-16
Author(s):  
Yawar Ali Sheikh ◽  
Zhongfu Ye ◽  
Kashif Shabir ◽  
Tarek Hasan Al Mahmud ◽  
Rizwan Ullah
Keyword(s):  
Near Field ◽  
Doa Estimation ◽  
Low Complexity ◽  
Joint Estimation ◽  
Field Source ◽  
Fitness Evaluation ◽  
Estimated Parameters ◽  
Planar Arrays ◽  
Evolutionary Technique ◽  
Array Structure

The efficiency of two dimensional (2-D) Direction of Arrival (DOA) estimation relies on the geometry of array. Among many array geometries, L-type array structure is becoming more popular among researchers because it can be decoupled into two uniform linear arrays (ULAs) and require less number of array elements as compared to other planar arrays. This paper propose a novel, fast and low complexity method for joint estimation of range and 2-D DOAs (elevation and azimuth) of near field sources. The main focus of this paper is to present the efficacy of performance and ease in implementation of L-type array structure when it is integrated with Differential Evolution (DE), a global evolutionary optimizer. To avoid the pair matching of estimated parameters, mean square error is used as fitness evaluation function as it only requires a single snapshot of array output to achieve optimal convergence. The robustness of proposed method is tested by a large number of computer simulations and statistical performance analysis is compared with other techniques.

Statistical analysis of achievable resolution limit in the near field source localization context

2012 ◽  
Vol 92 (2) ◽  
pp. 547-552 ◽  
Author(s):  
Mohammed Nabil El Korso ◽  
Rémy Boyer ◽  
Alexandre Renaux ◽  
Sylvie Marcos
Keyword(s):  
Statistical Analysis ◽  
Source Localization ◽  
Near Field ◽  
Resolution Limit ◽  
Field Source

Direction of Arrival Estimation Under Near-Field Interference Using Matrix Filter

2015 ◽  
Vol 23 (04) ◽  
pp. 1540007 ◽  
Author(s):  
Guolong Liang ◽  
Wenbin Zhao ◽  
Zhan Fan
Keyword(s):  
Near Field ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Direction Of Arrival Estimation ◽  
Far Field ◽  
Data Simulation ◽  
Estimation Algorithms ◽  
Simulation Results

Direction of arrival (DOA) estimation is of great interest due to its wide applications in sonar, radar and many other areas. However, the near-field interference is always presented in the received data, which may result in degradation of DOA estimation. An approach which can suppress the near-field interference and preserve the far-field signal desired by using a spatial matrix filter is proposed in this paper and some typical DOA estimation algorithms are adjusted to match the filtered data. Simulation results show that the approach can improve capability of DOA estimation under near-field inference efficiently.

The Costs of Ignoring High-Order Correlations in Populations of Model Neurons

2006 ◽  
Vol 18 (3) ◽  
pp. 660-682 ◽  
Author(s):  
Melchi M. Michel ◽  
Robert A. Jacobs
Keyword(s):  
Model Neuron ◽  
Joint Probability ◽  
High Order ◽  
Neural Population ◽  
Neural Responses ◽  
Significant Information ◽  
Statistical Correlations ◽  
Neural Populations ◽  
Simulation Results ◽  
Dependence Tree

Investigators debate the extent to which neural populations use pairwise and higher-order statistical dependencies among neural responses to represent information about a visual stimulus. To study this issue, three statistical decoders were used to extract the information in the responses of model neurons about the binocular disparities present in simulated pairs of left-eye and right-eye images: (1) the full joint probability decoder considered all possible statistical relations among neural responses as potentially important; (2) the dependence tree decoder also considered all possible relations as potentially important, but it approximated high-order statistical correlations using a computationally tractable procedure; and (3) the independent response decoder, which assumed that neural responses are statistically independent, meaning that all correlations should be zero and thus can be ignored. Simulation results indicate that high-order correlations among model neuron responses contain significant information about binocular disparities and that the amount of this high-order information increases rapidly as a function of neural population size. Furthermore, the results highlight the potential importance of the dependence tree decoder to neuroscientists as a powerful but still practical way of approximating high-order correlations among neural responses.

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