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您的检索式：作者名="LIN Liangkui"
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| 1 | Using deep learning to detect small targets in infrared oversampling images显示文摘According to the oversampling imaging characteristics, an infrared small target detection method based on deep learning is proposed. A 7-layer deep convolutional neural network(CNN) is designed to automatically extract small target features and suppress clutters in an end-to-end manner. The input of CNN is an original oversampling image while the output is a cluttersuppressed feature map. The CNN contains only convolution and non-linear operations, and the resolution of the output feature map is the same as that of the input image. The L1-norm loss function is used, and a mass of training data is generated to train the network effectively. Results show that compared with several baseline methods, the proposed method improves the signal clutter ratio gain and background suppression factor by 3 – 4 orders of magnitude, and has more powerful target detection performance. | LIN Liangkui WANG Shaoyou TANG Zhongxing | 2018 | Journal of Systems Engineering and Electronics2018，29，5： | 14 |
| 2 | Multi-target state-estimation technique for the particle probability hypothesis density filter显示文摘A simple yet effective state-estimation algorithm is presented and demonstrated to have advantages over previous standard clustering techniques used for the particle probability hypothesis density filter.The idea behind the proposed algorithm is that it uses the latest available information(i.e.,the measurements) to direct particle clustering.The particle likelihood and target number estimation,computed during probability hypothesis density recursion,are both used to partition particles into clusters,and the center of each cluster gives the state estimation of an individual target.Simulation results indicate that the proposed algorithm outperforms the standard clustering approach using the k-means algorithm,achieving higher accuracy and shorter computational time. | LIN LiangKui XU Hui SHENG WeiDong AN Wei | 2012 | Science China(Information Sciences)2012，55，10： | 7 |
| 3 | QPSO-based algorithm of CSO joint infrared super-resolution and trajectory estimation显示文摘The midcourse ballistic closely spaced objects(CSO) create blur pixel-cluster on the space-based infrared focal plane,making the super-resolution of CSO quite necessary.A novel algorithm of CSO joint super-resolution and trajectory estimation is presented.The algorithm combines the focal plane CSO dynamics and radiation models,proposes a novel least square objective function from the space and time information,where CSO radiant intensity is excluded and initial dynamics(position and velocity) are chosen as the model parameters.Subsequently,the quantum-behaved particle swarm optimization(QPSO) is adopted to optimize the objective function to estimate model parameters,and then CSO focal plane trajectories and radiant intensities are computed.Meanwhile,the estimated CSO focal plane trajectories from multiple space-based infrared focal planes are associated and filtered to estimate the CSO stereo ballistic trajectories.Finally,the performance(CSO estimation precision of the focal plane coordinates,radiant intensities,and stereo ballistic trajectories,together with the computation load) of the algorithm is tested,and the results show that the algorithm is effective and feasible. | Liangkui Lin Hui Xu Dan Xu Wei An Kai Xie | 2011 | Journal of Systems Engineering and Electronics2011，22，3： | 5 |
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