Near-field frequency - Domain theory for propeller noise

1983 ◽  
Author(s):  
D. HANSON
Keyword(s):  
Frequency Domain ◽  
Near Field ◽  
Domain Theory ◽  
Field Frequency ◽  
Propeller Noise

Near-field frequency-domain theory for propeller noise

AIAA Journal ◽  
1985 ◽  
Vol 23 (4) ◽  
pp. 499-504 ◽  
Author(s):  
Donald B. Hanson
Keyword(s):  
Frequency Domain ◽  
Near Field ◽  
Domain Theory ◽  
Field Frequency ◽  
Propeller Noise

Extrapolation and inversion of near-field propeller noise measurements

2022 ◽  
Vol 185 ◽  
pp. 108395
Author(s):  
Yan Wu ◽  
Michael J. Kingan ◽  
Ryan S. McKay ◽  
Sung Tyaek Go ◽  
Young-min Shim
Keyword(s):  
Near Field ◽  
Propeller Noise ◽  
Noise Measurements ◽  
And Inversion

scholarly journals Frequency-Diverse Holographic Metasurface Antenna for Near-Field Microwave Computational Imaging

2021 ◽  
Vol 8 ◽  
Author(s):  
Jiaqi Han ◽  
Long Li ◽  
Shuncheng Tian ◽  
Xiangjin Ma ◽  
Qiang Feng ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Imaging System ◽  
Near Field ◽  
Horn Antenna ◽  
Computational Imaging ◽  
Simulation Studies ◽  
Field Patterns ◽  
Full Wave ◽  
Imaging Reconstruction ◽  
Iterative Shrinkage

This article presents a holographic metasurface antenna with stochastically distributed surface impedance, which produces randomly frequency-diverse radiation patterns. Low mutual coherence electric field patterns generated by the holographic metasurface antenna can cover the K-band from 18 to 26 GHz with 0.1 GHz intervals. By utilizing the frequency-diverse holographic metasurface (FDHM) antenna, we build a near-field microwave computational imaging system based on reflected signals in the frequency domain. A standard horn antenna is adopted to acquire frequency domain signals radiated from the proposed FDHM antenna. A detail imaging restoration process is presented, and the desired targets are correctly reconstructed using the 81 frequency-diverse patterns through full-wave simulation studies. Compressed sensing technique and iterative shrinkage/thresholding algorithms are applied for the imaging reconstruction. The achieved compressive ratio of this computational imaging system on the physical layer is 30:1.

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