Paper Info
Title
Demonstration Of Spider-Eyes-Like Intelligent Antennas For Dynamically Perceiving Incoming Waves
Abstract
Obtaining a full view and complete information of the surrounding dynamics is of great significance for a plethora of applications in sensing, imaging, navigation, and orientation. However, conventional spatial spectrum methods heavily rely on a priori knowledge with a trial-and-error solution fashion, leading to a great challenge to estimate complete information in volatile scenarios. Inspired by the mechanism of the jumping spider (Salticidae), here a universal detection approach driven by an intelligent antenna array, with the usage of amplitude-only information as inputs, is introduced. The applied machine learning method can process the received time-varying signals in one single feed-forward computation, bypassing a heavy recline on prior knowledge of the array structure. As a demonstration, a compact eight-port antenna array is designed for simultaneous attainments of frequency, direction of arrival, and polarization, covering the entire microwave X band. Both the simulated and experimental results show that the average accuracies for the azimuth angle, elevation angle, and polarization are up to 98%, with a millisecond detection time. Different from conventional methods, the strategy herein does not involve a complex beamforming network and a time-consuming trial-and-error solution fashion, allowing a big step toward a miniaturized, integrated, and cost-effective detector.
Year
DOI
Venue
2021
10.1002/aisy.202100066
ADVANCED INTELLIGENT SYSTEMS
Keywords
DocType
Volume
incoming wave detectors, intelligent perceiving, machine learning, spider-eyes-like antennas
Journal
3
Issue
Citations 
PageRank 
9
0
0.34
References 
Authors
0
12
Name
Order
Citations
PageRank
Zhedong Wang100.34
Chao Qian200.34
Tong Cai300.34
Longwei Tian410.70
Zhixiang Fan500.34
J. Liu66415.00
Yichen Shen7163.09
Li Jing800.34
Jianming Jin900.34
Er-Ping Li1000.34
Bin Zheng1100.34
HongSheng Chen12201.46