Reconstruction of Measured Antenna Patterns and Related Time-Varying Aperture Fields

Martí-Canales, J.; Ligthart, L.P.

doi:10.1109/tap.2004.835235

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Reconstruction of antenna patterns from near-field measurements using the radiation centers of the antenna has been reported [3], while [4] reconstructed far-field radiation patterns from nearfield amplitude measurements using the global particle swarm optimization (PSO). Rammal et al [5] reconstructed wideband far-field radiation patterns from near-field transient measurements.…”

Section: Introductionmentioning

confidence: 99%

Compressive Sensing Reconstruction of Wideband Antenna Radiation Characteristics

Debroux¹,

Verdin²

2017

PIER C

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Characterization measurements of wideband antennas can be a time intensive and an expensive process as many data points are required in both the angular and frequency dimensions. Parallel compressive sensing is proposed to reconstruct the radiation-frequency patterns (RFP) of antennas from a sparse and random set of measurements. The modeled RFP of the dual-ridge horn, bicone, and Vivaldi antennas are used to analyze the minimum number of measurements needed for reconstruction, the difference in uniform versus non-uniform reconstruction, and the sparsity transform function used in the compressive sensing algorithm. The effect of additive white Gaussian noise (AWGN) on the minimum number of data points required for reconstruction is also studied. In a noise-free environment, the RFP of the antennas were adequately reconstructed using as little as 33% of the original data points. It was found that the RFPs were adequately reconstructed with less data points when the discrete cosine transforms (DCT), rather than the discrete Fourier transforms (DFT) was used in the compressive sensing algorithm. The presence of noise increases the number of data points required to reconstruct an RFP to a specified error tolerance, but the antenna RFPs can be reconstructed to within 1% root-mean-square-error of the original with a signal to noise ratio as low as −15 dB. The use of compressive sensing can thus lead to a new measurement methodology whereby a small subset of the total angular and frequency measurements is taken at random, and a full reconstruction of radiation and frequency behavior of the antenna is achieved during post-processing.

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Section: Introductionmentioning

confidence: 99%

Compressive Sensing Reconstruction of Wideband Antenna Radiation Characteristics

Debroux¹,

Verdin²

2017

PIER C

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Inflight Antenna Pattern Measurement for Bistatic Synthetic Aperture Radar Systems

Wang

2007

Antennas Wirel. Propag. Lett.

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Precise inflight antenna pattern should be known for synthetic aperture radar (SAR), so that an image pixel intensity is directly expressed in terms of the mean surface backscatter coefficient. For bistatic SAR (BiSAR), in which the transmitter and receiver are mounted on separate platforms, conventional passive calibrators are not applicable any more. As such, an approach is described on how to determine the inflight antenna patterns for BiSAR systems in this letter. This technique is suitable for high-resolution SAR due to the very high dynamic range. More importantly, this technique uses signal encoding to decouple the transponder from the background, which greatly improve the measurement performance. Theoretical analysis and simulation results also demonstrate its validity.

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Reconstruction of Measured Antenna Patterns and Related Time-Varying Aperture Fields

Cited by 2 publications

References 12 publications

Compressive Sensing Reconstruction of Wideband Antenna Radiation Characteristics

Compressive Sensing Reconstruction of Wideband Antenna Radiation Characteristics

Inflight Antenna Pattern Measurement for Bistatic Synthetic Aperture Radar Systems

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