Manjun Lu
8 Papers
Manjun Lu is an academic researcher. The author has contributed to research in topics: Computer science & Filter bank. The author has an hindex of 1, co-authored 1 publications.
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Papers
Phase-engineered metalenses to generate converging and non-diffractive vortex beam carrying orbital angular momentum in microwave region
Kuang Zhang,Yueyi Yuan,Dawei Zhang,Xumin Ding,Badreddine Ratni,Shah Nawaz Burokur,Manjun Lu,Kun Tang,Qun Wu +8 more
TL;DR: The proposed method provides an efficient approach to control the radius of vortex beam carrying OAM mode in microwave wireless applications for medium-short range distance.
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Research on channelized receiver based on microwave photonic with large instantaneous bandwidth
TL;DR: In this paper , the authors proposed a channelized receiver based on microwave photonic, the system generated two coherent optical frequency comb with different free spectral ranges by adjusting carrier frequency, a different frequency interval is formed between the local frequency comb and the optical comb modulating the signal to be measured.
3
Design and Implementation of FRM-based Filter Bank with Low Complexity for RJIA
TL;DR: Experimental results show that the proposed FRM-based filter band for RJIA offers 60.63% reduction in multipliers complexity and 38.14% reductions in chip power than PRFB, which has advantages of high sensitivity and ability of multiple signals reconnaissance and jamming.
1
An Online Jamming Effect Evaluation Method for Asymmetric Radar Information in Complex Electromagnetic Environment
Wenxu Zhang,Yajie Wang,Zhongkai Zhao,Tong Zhao,Manjun Lu +4 more
TL;DR: Experiments show that the proposed method can achieve efficient jamming effect evaluation under asymmetric radar information conditions, and an improved class-attribute contingency coefficient (ICACC) method is proposed.
Multi-Subband Radar Signal Fusion Processing Based on Deep Neural Network in Low Signal-to-Noise Ratio
TL;DR: A method based on the deep neural network (DNN) that applies nonlinear fitting of deep learning to complete the fusion process of multi-subband radar signal fusion and could improve the radar range resolution and obtain high-resolution one-dimensional range profiles.