Passive detection of moving target is an important part of intelligent surveillance. Satellite has the potential to play a key role in many applications of space-air-ground integrated networks(SAGIN). In this paper, w...Passive detection of moving target is an important part of intelligent surveillance. Satellite has the potential to play a key role in many applications of space-air-ground integrated networks(SAGIN). In this paper, we propose a novel intelligent passive detection method for aerial target based on reservoir computing networks. Specifically, delayed feedback networks are utilized to refine the direct signals from the satellite in the reference channels. In addition, the satellite direct wave interference in the monitoring channels adopts adaptive interference suppression using the minimum mean square error filter. Furthermore, we employ decoupling echo state networks to predict the clutter interference in the monitoring channels and construct the detection statistics accordingly. Finally, a multilayer perceptron is adopted to detect the echo signal after interference suppression. Extensive simulations is conducted to evaluate the performance of our proposed method. Results show that the detection probability is almost 100% when the signal-to-interference ratio of echo signal is-36 dB, which demonstrates that our proposed method achieves efficient passive detection for aerial targets in typical SAGIN scenarios.展开更多
This paper shows detailed steps for modeling a quadcopter with Euler-Lagrange equations, followed by controlling it with intelligent control that includes states decoupling. In addition, this control shows non-convent...This paper shows detailed steps for modeling a quadcopter with Euler-Lagrange equations, followed by controlling it with intelligent control that includes states decoupling. In addition, this control shows non-conventional membership functions for the most instable states, in order to get a fast and effective response.展开更多
基金supported by the National Natural Science Foundation of China under Grant 62071364in part by the Aeronautical Science Foundation of China under Grant 2020Z073081001+2 种基金in part by the Fundamental Research Funds for the Central Universities under Grant JB210104in part by the Shaanxi Provincial Key Research and Development Program under Grant 2019GY-043in part by the 111 Project under Grant B08038。
文摘Passive detection of moving target is an important part of intelligent surveillance. Satellite has the potential to play a key role in many applications of space-air-ground integrated networks(SAGIN). In this paper, we propose a novel intelligent passive detection method for aerial target based on reservoir computing networks. Specifically, delayed feedback networks are utilized to refine the direct signals from the satellite in the reference channels. In addition, the satellite direct wave interference in the monitoring channels adopts adaptive interference suppression using the minimum mean square error filter. Furthermore, we employ decoupling echo state networks to predict the clutter interference in the monitoring channels and construct the detection statistics accordingly. Finally, a multilayer perceptron is adopted to detect the echo signal after interference suppression. Extensive simulations is conducted to evaluate the performance of our proposed method. Results show that the detection probability is almost 100% when the signal-to-interference ratio of echo signal is-36 dB, which demonstrates that our proposed method achieves efficient passive detection for aerial targets in typical SAGIN scenarios.
文摘This paper shows detailed steps for modeling a quadcopter with Euler-Lagrange equations, followed by controlling it with intelligent control that includes states decoupling. In addition, this control shows non-conventional membership functions for the most instable states, in order to get a fast and effective response.
