Decoupled Location Parameter Estimation of 3-D Near-Field Sources in a Uniform Circular Array using the Rank Reduction Algorithm

In classification and localization of mixed far-field and near-field sources, the unknown mutual coupling degrades the performance of most high-resolution algorithms. In practice, the assumption of an ideal receiving sensor array is rarely satisfied. This paper proposes an effective algorithm of mixed sources identification using uniform circular array under unknown mutual coupling. Firstly, according to rank reduction and joint space–time processing, the directions of arrival of far-field sources is estimated directly without mutual coupling elimination. Addition, the joint space–time processing can improve the estimation results in the case of low signal noise ratio of incoming signal sources and small number of snapshots. Then, these estimates are adopted to reconstruct the mutual coupling matrix. Finally, both direction and range parameters of near-field sources are obtained through spatial search after mutual coupling effects and far-field components elimination. The proposed algorithm is described in detail, and its behavior is illustrated by numerical examples.

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Missile-borne Near-field Radar Coincidence Imaging Using a Uniform Circular Array

2020 IEEE 3rd International Conference on Electronic Information and Communication Technology (ICEICT) ◽

10.1109/iceict51264.2020.9334349 ◽

2020 ◽

Author(s):

Zhidong Liu ◽

Kaiming Li ◽

Rui Li ◽

Guojian Jiang ◽

Zilong Song

Keyword(s):

Near Field ◽

Circular Array ◽

Coincidence Imaging

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Efficient Localization Algorithm for Near-Field Noncircular Sources via Dual-Polarization Sensor Array

Mathematical Problems in Engineering ◽

10.1155/2019/5759427 ◽

2019 ◽

Vol 2019 ◽

pp. 1-8

Author(s):

Jiaqi Song ◽

Haihong Tao

Keyword(s):

Source Localization ◽

Near Field ◽

Nuisance Parameter ◽

Estimation Accuracy ◽

Localization Algorithm ◽

Dual Polarization ◽

Rank Reduction ◽

Localization Accuracy ◽

Noncircular Signals ◽

Polarization Sensor

Noncircular signals are widely used in the area of radar, sonar, and wireless communication array systems, which can offer more accurate estimates and detect more sources. In this paper, the noncircular signals are employed to improve source localization accuracy and identifiability. Firstly, an extended real-valued covariance matrix is constructed to transform complex-valued computation into real-valued computation. Based on the property of noncircular signals and symmetric uniform linear array (SULA) which consist of dual-polarization sensors, the array steering vectors can be separated into the source position parameters and the nuisance parameter. Therefore, the rank reduction (RARE) estimators are adopted to estimate the source localization parameters in sequence. By utilizing polarization information of sources and real-valued computation, the maximum number of resolvable sources, estimation accuracy, and resolution can be improved. Numerical simulations demonstrate that the proposed method outperforms the existing methods in both resolution and estimation accuracy.

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