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Airborne Radar Space–Time Adaptive Processing Algorithm Based on Dictionary and Clutter Power Spectrum Correction

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Publicado en:Electronics vol. 13, no. 11 (2024), p. 2187
Autor principal: Gao, Zhiqi
Otros Autores: Deng, Wei, Huang, Pingping, Xu, Wei, Tan, Weixian
Publicado:
MDPI AG
Materias:
Sparsity
Covariance matrix
Dictionaries
Accuracy
Yaw
Radar detection
Signal to noise ratio
Moving targets
Target detection
Recovery
Clutter
Arrays
Algorithms
Machine learning
Airborne radar
Space-time adaptive processing
Adaptive algorithms
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Resumen:Sparse recovery space–time adaptive processing (SR-STAP) technology improves the moving target detection performance of airborne radar. However, the sparse recovery method with a fixed dictionary usually leads to an off-grid effect. This paper proposes a STAP algorithm for airborne radar based on dictionary and clutter power spectrum joint correction (DCPSJC-STAP). The algorithm first performs nonlinear regression in a non-stationary clutter environment with unknown yaw angles, and it corrects the corresponding dictionary for each snapshot by updating the clutter ridge parameters. Then, the corrected dictionary is combined with the sparse Bayesian learning algorithm to iteratively update the required hyperparameters, which are used to correct the clutter power spectrum and estimate the clutter covariance matrix. The proposed algorithm can effectively overcome the off-grid effect and improve the moving target detection performance of airborne radar in actual complex clutter environments. Simulation experiments verified the effectiveness of this algorithm in improving clutter estimation accuracy and moving target detection performance.
ISSN:2079-9292
DOI:10.3390/electronics13112187
Fuente:Advanced Technologies & Aerospace Database