Demonstration of Spider‐Eyes‐Like Intelligent Antennas for Dynamically Perceiving Incoming Waves

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. Herein, inspired by the jumping spider, a universal detection approach is introduced for dynamically perceiving multiple parameters of incoming waves. The key to its success is the machine learning model that allows real‐time prediction of the multi‐parameters in one single forward calculation. Experiment results show that the overall accuracy of the detector is up to 89% in the X band.