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A Two-Stage STAP Method Based on Fine Doppler Localization and Sparse Bayesian Learning in the Presence of Arbitrary

Kun Liu1, Tong Wang1, Jianxin Wu2

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This study introduces a two-stage sparse recovery based space-time adaptive processing (SR-STAP) method to improve nonhomogeneous clutter suppression despite unknown array errors. The novel approach accurately estimates spatial steering vectors, enhancing performance in radar systems.

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airborne radararbitrary array errorclutter suppressionspace-time adaptive processingsparse Bayesian learning

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Area of Science:

  • Signal Processing
  • Radar Systems Engineering
  • Array Signal Processing

Background:

  • Traditional sparse recovery based space-time adaptive processing (SR-STAP) methods suffer performance degradation due to steering vector mismatch when array errors are present.
  • Existing methods often use ideal spatial steering vectors, failing to account for real-world array imperfections.

Purpose of the Study:

  • To propose a robust two-stage SR-STAP method for effective nonhomogeneous clutter suppression in the presence of arbitrary array errors.
  • To address the model mismatch problem caused by array errors in SR-STAP algorithms.

Main Methods:

  • A two-stage approach is proposed: the first stage estimates spatial steering vectors with array errors using spatial-temporal coupling and Doppler localization.
  • The second stage constructs a space-time dictionary using the estimated steering vectors and employs a multiple measurement vectors sparse Bayesian learning (MSBL) algorithm for STAP.

Main Results:

  • The proposed SR-STAP method demonstrates superior clutter suppression and target detection performance compared to traditional methods.
  • Effectiveness is validated through simulations, showing robust performance even with arbitrary array errors.

Conclusions:

  • The developed two-stage SR-STAP method effectively mitigates performance degradation caused by array errors.
  • This approach offers a significant advancement for radar systems requiring reliable clutter suppression in challenging environments.