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ZHANG Xiang, LAN Lan, MA Nan, et al. Mainlobe blanket interference suppression with PEPC-MIMO radar via low-rank matrix decomposition[J]. Journal of Radars, in press. doi: 10.12000/JR25227
Citation: ZHANG Xiang, LAN Lan, MA Nan, et al. Mainlobe blanket interference suppression with PEPC-MIMO radar via low-rank matrix decomposition[J]. Journal of Radars, in press. doi: 10.12000/JR25227
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Mainlobe Blanket Interference Suppression with PEPC-MIMO Radar via Low-rank Matrix Decomposition

DOI: 10.12000/JR25227 CSTR: 32380.14.JR25227
  • 1.

    National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

  • 2.

    Southwest China Research Institute of Electronic Equipment, Chengdu 610000, China

Funds:  The National Natural Science Foundation of China (62522120, 62471348), The Fundamental Research Funds for the Central Universities (QTZX23068, YJSJ25008)
More Information
  • Corresponding author: LAN Lan, lanlan@xidian.edu.cn
  • Received Date: 2025-11-05
  • Rev Recd Date: 2025-12-23
    • Available Online: 2025-12-28
    Abstract
  • By applying phase coding in transmit elements and pulses, the Element-Pulse Coding Multiple-Input Multiple-Output (EPC-MIMO) radar can effectively suppress mainlobe deceptive interference. However, this approach remains ineffective against mainlobe blanket interference. To address this drawback, this paper investigates the mainlobe blanket interference suppression using a Polarization Element-Pulse Coding Multiple-Input Multiple-Output (PEPC-MIMO) radar system. Specifically, within the framework of stable principal component pursuit decomposition, the interference suppression problem is formulated as a “low-rank + sparse” optimization model by exploiting the low-rank structure of the received signal in the joint time-space-polarization domain. The resulting optimization problem is solved iteratively using a Limited-memory Broyden-Fletcher-Goldfarb-Shanno-based Alternating Optimization (L-BFGS-AO) algorithm, thereby enabling accurate separation of target echoes from mainlobe blanket interference. Furthermore, a sparse reconstruction-based parameter estimation method is proposed to estimate the target’s transmit angle, receive angle, and range ambiguity region. These estimates are then used to construct optimal receive weight vectors for the weighted summation of signals across channels. Simulation results demonstrate the effectiveness of the proposed approach in suppressing mainlobe blanket interference without requiring prior knowledge of the interference.

     

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