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
The lack of interpretability of the neural network algorithm has become the bottleneck of its wide application.We propose a general mathematical framework,which couples the complex structure of the system with the non...The lack of interpretability of the neural network algorithm has become the bottleneck of its wide application.We propose a general mathematical framework,which couples the complex structure of the system with the nonlinear activation function to explore the decoupled dimension reduction method of high-dime sional system and reveal the calculation mechanism of the neural network.We apply our framework to some network models and a real system of the whole neuron map of Caenorhabditis elegans.Result shows that a simple linear mapping relationship exists between network structure and network behavior in the neural network with highdimensional and nonlinear characteristics.Our simulation and theoretical results fully demonstrate this interesting phenomenon.Our new interpretation mechanism provides not only the potential mathematical calculation principle of neural network but also an effective way to accurately match and predict human brain or animal activities,which can further expand and enrich the interpretable mechanism of artificial neural network in the future.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.
基金This work was supported by the National Natural Science Foundation of China(Grant Nos.72071153,71631001,and 71771186)the Natural Science Foundation of Shaanxi Province(Project No.2020JM-486)the Fund of the Key Laboratory of Equipment Integrated Support Technology(Project No.6142003190102).
文摘The lack of interpretability of the neural network algorithm has become the bottleneck of its wide application.We propose a general mathematical framework,which couples the complex structure of the system with the nonlinear activation function to explore the decoupled dimension reduction method of high-dime sional system and reveal the calculation mechanism of the neural network.We apply our framework to some network models and a real system of the whole neuron map of Caenorhabditis elegans.Result shows that a simple linear mapping relationship exists between network structure and network behavior in the neural network with highdimensional and nonlinear characteristics.Our simulation and theoretical results fully demonstrate this interesting phenomenon.Our new interpretation mechanism provides not only the potential mathematical calculation principle of neural network but also an effective way to accurately match and predict human brain or animal activities,which can further expand and enrich the interpretable mechanism of artificial neural network in the future.
