Subspace-based method for joint range and DOA estimation of multiple near-field sources

2006 ◽  
Vol 86 (8) ◽  
pp. 2129-2133 ◽  
Author(s):  
Yuntao Wu ◽  
Lin Ma ◽  
Chaohuan Hou ◽  
Guangbin Zhang ◽  
Jun Li
2015 ◽  
Vol 23 (04) ◽  
pp. 1540007 ◽  
Author(s):  
Guolong Liang ◽  
Wenbin Zhao ◽  
Zhan Fan

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.


2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Oluwole John Famoriji ◽  
Thokozani Shongwe

To obtain an antenna array with isotropic radiation, spherical antenna array (SAA) is the right array configuration. The challenges of locating signals transmitted within the proximity of antenna array have been investigated considerably in the literature. However, near-field (NF) source localization of signals has hitherto not been investigated effectively using SAA in the presence of mutual coupling (MC). MC is another critical problem in antenna arrays. This paper presents an NF range and direction-of-arrival (DoA) estimation technique via the direction-independent and signal invariant spherical harmonics (SH) characteristics in the presence of mutual coupling. The energy of electromagnetic (EM) signal on the surface of SAA is captured successfully using a proposed pressure interpolation approach. The DoA estimation within the NF region is then calculated via the distribution of pressure. The direction-independent and signal invariant characteristics, which are SH features, are obtained using the DoA estimates in the NF region. We equally proposed a learning scheme that uses the source activity detection and convolutional neural network (CNN) to estimate the range of the NF source via the direction-independent and signal invariant features. Considering the MC problem and using the DoA estimates, an accurate spectrum peak in the multipath situation in conjunction with MC and a sharper spectrum peak from a unique MC structure and smoothing algorithms are obtained. For ground truth performance evaluation of the SH features within the context of NF localization, a numerical experiment is conducted and measured data were used for analysis to incorporate the MC and consequently computed the root mean square error (RMSE) of the source range and NF DoA estimate. The results obtained from numerical experiments and measured data indicate the validity and effectiveness of the proposed approach. In addition, these results are motivating enough for the deployment of the proposed method in practical applications.


2017 ◽  
Vol 2 (3) ◽  
pp. 11-16
Author(s):  
Yawar Ali Sheikh ◽  
Zhongfu Ye ◽  
Kashif Shabir ◽  
Tarek Hasan Al Mahmud ◽  
Rizwan Ullah

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.


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