Near-field far-field transformations using spherical-wave expansions

Near-field to far-field transformation (NFFFT) is a frequently-used method in antenna and radar cross section (RCS) measurements for various applications. For weapon systems, most measurements are captured in the near-field area in an anechoic chamber, considering the security requirements for the design process and high spatial costs of far-field measurements. As the theoretical RCS value is the power ratio of the scattered wave to the incident wave in the far-field region, a scattered wave measured in the near-field region needs to be converted into field values in the far-field region. Therefore, this paper proposes a near-field to far-field transformation algorithm based on spherical wave expansion for application in near-field RCS measurement systems. If the distance and angular coordinates of each measurement point are known, the spherical wave functions in an orthogonal relationship can be calculated. If each weight is assumed to be unknown, a system of linear equations as numerous as the number of samples measured in the near electric field can be generated. In this system of linear equations, each weight value can be calculated using the iterative least squares QR-factorization method. Based on this theory, the validity of the proposed NFFFT is verified for several scatterer types, frequencies and measurement distances.

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Probe-corrected near-field to far-field transformation using multiple spherical wave expansions

International Journal of Microwave and Wireless Technologies ◽

10.1017/s1759078720000136 ◽

2020 ◽

Vol 12 (6) ◽

pp. 447-454

Author(s):

Fernando Rodríguez Varela ◽

Belén Galocha Iragüen ◽

Manuel Sierra Castañer

Keyword(s):

Near Field ◽

Spherical Wave ◽

Irregular Sampling ◽

Field Pattern ◽

Far Field ◽

Transformation Technique ◽

Sampling Schemes ◽

Field Transformation ◽

Simulated Field ◽

Far Field Pattern

AbstractNear-field to far-field transformations constitute a powerful antenna characterization technique for near-field measurement scenarios. In this paper, a near-field to far-field transformation technique based on multiple spherical wave expansions (SWEs) is presented. Thanks to its iterative matrix inversion nature, the approach performs the transformation of fields measured on arbitrary surfaces. Also, irregular sampling schemes can be incorporated. The proposed algorithm is based on modeling the antenna fields with not one, but several SWEs distributed over its geometry. Due to the high number of SWEs, their truncation number can be arbitrarily reduced. Working with expansions of low order allows us to incorporate the probe correction in the transformation in a very simple way, accepting any type of probe and orientation. Only the probe far-field pattern is used, thus working with its full SWE is avoided. The algorithm is validated using simulated field data as well as measurements of real antennas.

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A Sparse Perspective for Direction-of-Arrival Estimation Under Strong Near-Field Interference Environment

Sensors ◽

10.3390/s20010163 ◽

2019 ◽

Vol 20 (1) ◽

pp. 163

Author(s):

Longhao Qiu ◽

Tian Lan ◽

Yilin Wang

Keyword(s):

Near Field ◽

Spherical Wave ◽

Reconstruction Algorithm ◽

Direction Of Arrival ◽

Doa Estimation ◽

Noise Power ◽

Far Field ◽

Music Algorithm ◽

Practical Applications ◽

Minimum Variance Distortionless Response

Direction of arrival (DOA) estimation via sensor array is a crucial component of any passive sonar signal processing technology. In certain practical applications, however, the interested far-field targets are frequently affected by near-field interference, which may result in degradation of DOA estimation. Aiming at the direction estimation problems of far-field targets under strong near-field interference, a unified sparse representation model of far-field and near-field hybrid sources is constructed according to the various correlations in steering vectors between the planar wave and spherical wave in this paper. A high-resolution spatial spectrum reconstruction algorithm based on a sparse Bayesian framework is then exploited to constrain the energy of near-field interference in the preset near-field steering vector over-complete dictionary, thus ensuring the accurate detection and estimation of far-field targets. An expectation-maximization (EM) algorithm approach is introduced to estimate the number of sources and noise power iteratively, which will reduce the dependence of the algorithm on such prior information. Several state-of-art algorithms are mentioned and discussed (Minimum Variance Distortionless Response (MVDR) method, Multiple Signal Classification (MUSIC) algorithm and conventional beamforming (CBF) algorithm) to compare with the one proposed in this manuscript that achieves higher accuracy of estimation and resolution under low SNR level with limited samples, which is verified by simulation and for the results obtained in an experimental case study.

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