Jianxiong Wen

Wuhan University

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Co-Authors

Haibo Lv

Hulunbuir University

Junquan Yuan

Air Force Engineering University

Xianrong Wan

Wuhan University

Yue Wang

Dian Diagnostics (China)

Jianxin Yi

Wuhan University

Xingyu Li

Ocean University of China

Jiulu Gong

Beijing Institute of Technology

Liang Zhou

Naval University of Engineering

Kai Liu

Shandong Agricultural University

Zepeng Wang

Beijing Institute of Big Data Research

Cooperative Institutions

Beijing Institute of Technology

Harbin Institute of Technology

Wuhan University

Xidian University

National Engineering Research Center of Electromagnetic Radiation Control Materials

Beihang University

Tsinghua University

Hulunbuir University

Guilin University of Technology

Heilongjiang Institute of Technology

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Range Ambiguity Clutter Suppression Method for Space-Air Based Bistatic Radar

2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE) (2018)

Yue Wang Liang Zhou Jianxiong Wen Junquan Yuan Zhongyan Huang

Conventional Doppler warping (DW) method has good suppression effect for space-air based bistatic radar (SABBR) non-stationary clutter. But if radar geometrical configuration relations are unknown and range ambiguity exists, conventional DW method cannot be used. Aiming at range ambiguity clutter suppression problem for SABBR, a non-stationary clutter suppression method is put forward based on the ideas of improved DW method and reconstructing clutter covariance matrix (IDW+RCCM). Firstly, clutter covariance matrix of specified range ring is estimated through improved DW method. Then, power spectrum of the range ring is calculated as the amplitude value, and the clutter data and covariance matrix of the ambiguous range rings are reconstructed. Finally, the reconstructed data is used to achieve space-time adaptive processing for the range ambiguity clutter. Simulation results demonstrate the effectiveness of the proposed method.

Radar horizon

10.1109/icmcce.2018.00079

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Citations (3)

Convolution Kernel Pruning Algorithm Based on Average Percentage of Zeros and Data Distribution Similarity

2021 IEEE International Conference on Unmanned Systems (ICUS) (2024)

Xingyu Li Jiulu Gong Haibo Lv Jianxiong Wen Kai Liu

Kernel (algebra)

Similarity (geometry)

Pruning

Convolution (computer science)

10.1109/icus61736.2024.10840150

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Off-grid DOA estimation for passive radar

IET conference proceedings. (2021)

Jianxiong Wen Jianxin Yi Xianrong Wan

To solve the problem that the true target direction of arrival (DOA) is not on the discrete grid points, we adopt a new off-grid sparse strategy to estimate the target DOA of passive radar. This strategy is the root sparse Bayesian learning method, which utilizes a coarse grid and treats the sampling position in the coarse grid as an adaptive parameter. Then, the coarse mesh is iteratively refined using the expectation maximization algorithm, and the estimated angle is the root of a certain polynomial. Simulation results show that the method has higher estimation accuracy and lower computational complexity in DOA estimation of passive radar. In addition, this method can not only estimate the DOA of multi-target signals in a single snapshot, but also provide new ideas for DOA estimation of coherent signals.

Direction of arrival

Snapshot (computer storage)

Passive Radar

Position (finance)

10.1049/icp.2021.0522

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Citations (3)