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Distributed Phased-Array Radar Mainlobe Interference Suppression and Cooperative Localization Based on CEEMDAN-WOBSS.

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  • 1College of Missile Engineering, Rocket Force Engineering University, Xi'an 710025, China.

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Summary
This summary is machine-generated.

This study presents a novel framework for radar systems to combat jamming and improve target localization. The signal-data dual-domain cooperative antijamming and localization (SDCAL) method enhances radar performance in challenging environments.

Keywords:
blind source separationcooperative target localizationdistributed radar networkslow-SNR environmentsmainlobe interference suppression

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Area of Science:

  • Radar Systems Engineering
  • Signal Processing
  • Array Signal Processing

Background:

  • Distributed phased-array radar systems suffer performance degradation due to mainlobe interference, especially with strong jamming or low-reflectivity targets.
  • Effective antijamming and precise target localization are critical for robust radar system operation.

Purpose of the Study:

  • To introduce a novel signal-data dual-domain cooperative antijamming and localization (SDCAL) framework.
  • To enhance the performance of distributed phased-array radar systems in challenging jamming scenarios.

Main Methods:

  • Adaptive complete ensemble empirical mode decomposition with improved blind source separation and wavelet optimization (CEEMDAN-WOBSS) for signal denoising and separation.
  • Constant False Alarm Rate (CFAR)-based pulse compression for range estimation.
  • Multi-node data fusion for three-dimensional target localization.

Main Results:

  • The adaptive CEEMDAN-WOBSS approach reconstructs the signal covariance matrix to preserve subspace rank, accelerating separation matrix convergence under low SNR.
  • Pulse compression and CFAR detection yield reliable inter-node distance measurements for accurate fusion.
  • The proposed SDCAL framework significantly improves interference suppression, detection probability, and localization accuracy compared to conventional methods.

Conclusions:

  • The SDCAL framework offers a robust solution for collaborative sensing in challenging jamming environments.
  • The integrated CEEMDAN-WOBSS, CFAR pulse compression, and multi-node fusion effectively address mainlobe interference and enhance localization precision.