One-Shot Near-Field Compressive Sensing using Surface-Wave Goubau Line

2018 ◽  
Author(s):  
Dingfei Ma ◽  
Qingfeng Zhang ◽  
Xiaolan Tang
Keyword(s):  
Surface Wave ◽  
Compressive Sensing ◽  
Near Field

Surface-Wave Green's Tensors in the Near Field

2014 ◽  
Vol 104 (3) ◽  
pp. 1578-1586 ◽  
Author(s):  
M. M. Haney ◽  
H. Nakahara
Keyword(s):  
Surface Wave ◽  
Near Field

scholarly journals Surface Wave Multipath Signals in Near-Field Microwave Imaging

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Paul M. Meaney ◽  
Fridon Shubitidze ◽  
Margaret W. Fanning ◽  
Maciej Kmiec ◽  
Neil R. Epstein ◽  
...  
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Antenna Arrays ◽  
Imaging Techniques ◽  
Near Field ◽  
Microwave Imaging ◽  
Feed Line ◽  
Receiving Antenna ◽  
Wave Effects ◽  
Multipath Signals

Microwave imaging techniques are prone to signal corruption from unwanted multipath signals. Near-field systems are especially vulnerable because signals can scatter and reflect from structural objects within or on the boundary of the imaging zone. These issues are further exacerbated when surface waves are generated with the potential of propagating along the transmitting and receiving antenna feed lines and other low-loss paths. In this paper, we analyze the contributions of multi-path signals arising from surface wave effects. Specifically, experiments were conducted with a near-field microwave imaging array positioned at variable heights from the floor of a coupling fluid tank. Antenna arrays with different feed line lengths in the fluid were also evaluated. The results show that surface waves corrupt the received signals over the longest transmission distances across the measurement array. However, the surface wave effects can be eliminated provided the feed line lengths are sufficiently long independently of the distance of the transmitting/receiving antenna tips from the imaging tank floor. Theoretical predictions confirm the experimental observations.

scholarly journals Structure-Aware Bayesian Compressive Sensing for Near-Field Source Localization Based on Sensor-Angle Distributions

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Si Qin ◽  
Yimin D. Zhang ◽  
Qisong Wu ◽  
Moeness G. Amin
Keyword(s):  
Spatial Frequency ◽  
Compressive Sensing ◽  
Source Localization ◽  
Near Field ◽  
Joint Space ◽  
Angle Distribution ◽  
Field Source ◽  
Frequency Distributions ◽  
Linear Arrays ◽  
Time Frequency

A novel technique for localization of narrowband near-field sources is presented. The technique utilizes the sensor-angle distribution (SAD) that treats the source range and direction-of-arrival (DOA) information as sensor-dependent phase progression. The SAD draws parallel to quadratic time-frequency distributions and, as such, is able to reveal the changes in the spatial frequency over sensor positions. For a moderate source range, the SAD signature is of a polynomial shape, thus simplifying the parameter estimation. Both uniform and sparse linear arrays are considered in this work. To exploit the sparsity and continuity of the SAD signature in the joint space and spatial frequency domain, a modified Bayesian compressive sensing algorithm is exploited to estimate the SAD signature. In this method, a spike-and-slab prior is used to statistically encourage sparsity of the SAD across each segmented SAD region, and a patterned prior is imposed to enforce the continuous structure of the SAD. The results are then mapped back to source range and DOA estimation for source localization. The effectiveness of the proposed technique is verified using simulation results with uniform and sparse linear arrays where the array sensors are located on a grid but with consecutive and missing positions.

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