A Partial Rotation Formulation of the Circular Near-Field-to-Far-Field Transformation (CNFFFT)

Rice, S.A.; LaHaie, Ivan J.

doi:10.1109/map.2007.4293976

Cited by 21 publications

(11 citation statements)

References 2 publications

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“…In the implementation of the framework, the test angle θ is from −6° to 6° and has 121 sampling points. It takes into account the cost of data acquisition and the limitations of “image-based” NFFFT on the test angle [ 10 ]. The radius R of the target is limited to 3

to 8

.…”

Section: Numerical Analysismentioning

confidence: 99%

“…In order to compare with “image-based” NFFFT in [ 10 ], a three-point scattering target that meets the requirements in Figure 5 a is used to define the wideband monostatic scattering field at the same angle. The center frequency of them is the same as the NN-NFFFT framework, while the relative bandwidth is 40%.…”

Section: Numerical Analysismentioning

confidence: 99%

“…As mentioned in [ 10 ], the accuracy of “Image-Based” NFFFT is relative to the sample points in the frequency domain and the truncation number in Equation (15). The truncation number

is related to the minimum diameter of the target D (D = 2R) and is restricted to be greater than

[ 9 ], where k is the wavenumber.…”

Section: Numerical Analysismentioning

confidence: 99%

“…The measured errors lead to corresponding errors in the reconstructed far-field RCS [ 3 ]. The second kind, based on the Hankel function, calculates RCS without the image reconstruction process but still needs a broadband measurement, as in [ 7 , 8 , 9 , 10 ]. However, this method introduces fluctuations in the angle domain [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the antennas’ radiation patterns and the bistatic measurement can be completely incorporated in the training process with no additional complication. The sampling limitation introduced in [ 10 ] can also be ignored due to the training samples. The experiment result shows that, for the target, which has a small RCS, the method maintains good accuracy with less operation time.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Radar Cross Section Near-Field to Far-Field Prediction for Isotropic-Point Scattering Target Based on Regression Estimation

Yang

Zhang

et al. 2020

Sensors

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Radar cross section near-field to far-field transformation (NFFFT) is a well-established methodology. Due to the testing range constraints, the measured data are mostly near-field. Existing methods employ electromagnetic theory to transform near-field data into the far-field radar cross section, which is time-consuming in data processing. This paper proposes a flexible framework, named Neural Networks Near-Field to Far-Filed Transformation (NN-NFFFT). Unlike the conventional fixed-parameter model, the near-field RCS to far-field RCS transformation process is viewed as a nonlinear regression problem that can be solved by our fast and flexible neural network. The framework includes three stages: Near-Field and Far-field dataset generation, regression estimator training, and far-field data prediction. In our framework, the Radar cross section prior information is incorporated in the Near-Field and Far-field dataset generated by a group of point-scattering targets. A lightweight neural network is then used as a regression estimator to predict the far-field RCS from the near-field RCS observation. For the target with a small RCS, the proposed method also has less data acquisition time. Numerical examples and extensive experiments demonstrate that the proposed method can take less processing time to achieve comparable accuracy. Besides, the proposed framework can employ prior information about the real scenario to improve performance further.

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to 8

.…”

Section: Numerical Analysismentioning

confidence: 99%

Section: Numerical Analysismentioning

confidence: 99%

“…As mentioned in [ 10 ], the accuracy of “Image-Based” NFFFT is relative to the sample points in the frequency domain and the truncation number in Equation (15). The truncation number

is related to the minimum diameter of the target D (D = 2R) and is restricted to be greater than

[ 9 ], where k is the wavenumber.…”

Section: Numerical Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Radar Cross Section Near-Field to Far-Field Prediction for Isotropic-Point Scattering Target Based on Regression Estimation

Yang

Zhang

et al. 2020

Sensors

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show abstract

Error Comparison of CNFFFT and Hankel Method in RCS Near-Field to Far-Field Transformation

Zhao

Liu

et al. 2023

J. Phys.: Conf. Ser.

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At present, there are mainly two kinds of image-based near-field to far-field transformation (NFFFT) algorithms: Hankel method and circular near-field-to-far-field transformation (CNFFFT). Due to the approximation in the measurement distance, the accuracy of Hankel method is lower than that of CNFFFT. However, the implementation of CNFFFT is complex and time-consuming. It is necessary to determine the detailed relationship between the transformation error and the near-field test distance for both algorithms to provide a basis for the choice of which algorithm to use. In this paper, the effect of the near-field test distance approximation of the two algorithms is obtained by simulating 4, 5, and 9 scattering point models. Meanwhile, an accurate comparison method for two NFFFT algorithms under different test distances is proposed.

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RCS Prediction Method from One-Dimensional Intensity Data in Near-Field

Inasawa

Miyashita

Konishi

2008

IEICE Transactions on Electronics

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A Partial Rotation Formulation of the Circular Near-Field-to-Far-Field Transformation (CNFFFT)

Cited by 21 publications

References 2 publications

Radar Cross Section Near-Field to Far-Field Prediction for Isotropic-Point Scattering Target Based on Regression Estimation

Radar Cross Section Near-Field to Far-Field Prediction for Isotropic-Point Scattering Target Based on Regression Estimation

Error Comparison of CNFFFT and Hankel Method in RCS Near-Field to Far-Field Transformation

RCS Prediction Method from One-Dimensional Intensity Data in Near-Field

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