A simple sensor management criterion for radar beam control based on the CB-MeMBer filter

2015 ◽  
Author(s):  
Fotios Katsilieris ◽  
Hans Driessen ◽  
Alexander Yarovoy
Keyword(s):  
Sensor Management ◽  
Beam Control ◽  
Radar Beam

Flexible Beam Control Using an Adaptive Truss

1990 ◽  
Author(s):  
Thomas J. Warrington ◽  
C. Garnett Horner
Keyword(s):  
Flexible Beam ◽  
Beam Control

Emerging Applications in Probability (Sensor Management).

1995 ◽  
Author(s):  
Avner Friedman ◽  
Keith Kastella
Keyword(s):  
Sensor Management

Optimal Sensor Management for Next-Generation EMI Systems

2008 ◽  
Author(s):  
Lawrence Carin ◽  
Nilanjan Dasgupta ◽  
Hui Li
Keyword(s):  
Sensor Management ◽  
Next Generation

scholarly journals Study on Radar Echo-Filling in an Occlusion Area by a Deep Learning Algorithm

2021 ◽  
Vol 13 (9) ◽  
pp. 1779
Author(s):  
Xiaoyan Yin ◽  
Zhiqun Hu ◽  
Jiafeng Zheng ◽  
Boyong Li ◽  
Yuanyuan Zuo
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Learning Algorithm ◽  
Weather Radar ◽  
Loss Functions ◽  
Training Dataset ◽  
Echo Intensity ◽  
Common Mean ◽  
Deep Learning Algorithm ◽  
Radar Beam

Radar beam blockage is an important error source that affects the quality of weather radar data. An echo-filling network (EFnet) is proposed based on a deep learning algorithm to correct the echo intensity under the occlusion area in the Nanjing S-band new-generation weather radar (CINRAD/SA). The training dataset is constructed by the labels, which are the echo intensity at the 0.5° elevation in the unblocked area, and by the input features, which are the intensity in the cube including multiple elevations and gates corresponding to the location of bottom labels. Two loss functions are applied to compile the network: one is the common mean square error (MSE), and the other is a self-defined loss function that increases the weight of strong echoes. Considering that the radar beam broadens with distance and height, the 0.5° elevation scan is divided into six range bands every 25 km to train different models. The models are evaluated by three indicators: explained variance (EVar), mean absolute error (MAE), and correlation coefficient (CC). Two cases are demonstrated to compare the effect of the echo-filling model by different loss functions. The results suggest that EFnet can effectively correct the echo reflectivity and improve the data quality in the occlusion area, and there are better results for strong echoes when the self-defined loss function is used.

Smart DRA for beam width and orientation control

Frequenz ◽  
2020 ◽  
Vol 74 (11-12) ◽  
pp. 383-392
Author(s):  
Rajveer S. Yaduvanshi ◽  
Richa Gupta ◽  
Saurabh Katiyar
Keyword(s):  
Radiation Pattern ◽  
High Efficiency ◽  
Beam Width ◽  
Beam Control ◽  
Design Flexibility ◽  
Compact Size ◽  
Close Proximity ◽  
Cellular Communication Network ◽  
Arc Shape ◽  
Broadside Radiation

AbstractSmartdielectric resonator antenna (DRA) having beam control mechanism is anew area to be explored by antenna researchers. Proposed new geometry DRA has low loss, design flexibility, high efficiency, compact size and desired radiated beam control. Developing beam control in new geometry DRAs is investigated for the first time in this letter. Unique technique for beam control and beam width control is proposed using pit top and mount top DRA. Gain is controlled from 5.0 to 9.98 dBi and beam is controlled from ±30° to ±70° in broadside radiation pattern. U shape pit DRA has maximum directive gain of 9.98 dBi and efficiency 98% at 5.8 GHz frequency. Measured and simulated results of radiation pattern and reflection coefficient are found to be in close proximity. Hardware of U shape pit top DRA, mount top DRA, left side arc top DRA, right side arc shape top DRA is developed and investigated. Mobile and cellular communication network need wide coverage, hence large beam width is required. Narrowing of beam width at higher order mode is also achieved.

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