Android mobile devices are widely used in recent years. Due to the openness of Android, applications with malicious behavior have more opportunities to get confidential information, which can cause property damage. Mo...
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ISBN:
(纸本)9783030000189;9783030000172
Android mobile devices are widely used in recent years. Due to the openness of Android, applications with malicious behavior have more opportunities to get confidential information, which can cause property damage. Most of current solutions are hard to detect these rapidly developing malicious applications with high accuracy. In this paper, a static malicious application detection method based on sparse bayesian learning algorithm and n-gram analysis is proposed to solve this problem.
Space-time adaptive processing with finite samples is supposed to be a crucial technique for airborne radar systems. Inspired by the application of Gaussian prior in sparse bayesian learning algorithm and the adaptive...
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Space-time adaptive processing with finite samples is supposed to be a crucial technique for airborne radar systems. Inspired by the application of Gaussian prior in sparse bayesian learning algorithm and the adaptive least absolute shrinkage and selection operator algorithm, a hierarchical bayesian framework with adaptive Laplace priors is proposed. In this paper, a novel method is applied to avoid the high-dimension matrix inverse operation in the proposed algorithm. Moreover, in order to apply the method in the complex-valued domain, the complex-valued signal is split into two independent variables. Then, the sparse recovery problem in the complex-valued domain can be transformed into the real-value domain. Simulation experiments show that the proposed algorithm can achieve great clutter suppression performance and also ensure high computational efficiency.
The electromagnetic (EM) vortex imaging has been found to have a great potential application prospect in the imaging radar field. However, current studies focus on the motionless target, which seriously limits its app...
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The electromagnetic (EM) vortex imaging has been found to have a great potential application prospect in the imaging radar field. However, current studies focus on the motionless target, which seriously limits its application in practice. Therefore, to achieve EM vortex imaging for the motion target, this study proposes a parametric sparse representation model for EM vortex imaging that takes into account a translational motion target and uses the stepped frequency signal. An iterative algorithm is developed based on the sparsebayesianlearning (SBL) algorithm to estimate the velocity, and accomplish the EM vortex imaging exploiting SBL algorithm. Simulation results demonstrate that the proposed algorithm can improve velocity estimate accuracy in terms of relative error and achieve EM vortex imaging for the motion target.
Radar imaging of the ship is one of the important means for surveillance and reconnaissance of targets. Here, the K-distribution is used for the statistical characterisation of echo model in sea-clutter environment. F...
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Radar imaging of the ship is one of the important means for surveillance and reconnaissance of targets. Here, the K-distribution is used for the statistical characterisation of echo model in sea-clutter environment. FEKO software is used to simulate the ship model. The multi-radar data fusion technique and sparse bayesian learning algorithm are used to generate ISAR target images in the different levels of sea condition. The simulation results show that the multi-radar data fusion algorithm not only has a significant inhibitory effect on sea-clutter but also improves the radar image resolution.
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