Effective Sea Clutter Suppression via MIMO Radar Space-Time Adaptive Processing Strategy

• Published in: IEEE transactions on instrumentation and measurement Vol. 70; pp. 1 - 16
• Main Authors: Hu, Ziying, Wang, Wei, Dong, Fuwang
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Breaking waves; Clutter; Conditional probability; Correlation analysis; Cross correlation; Estimation; MIMO radar; Minimization; Multiple-input–multiple-output (MIMO) radar; multistage Gaussian prior; Probabilistic models; Probability density function; Probability density functions; Radar; Reconstruction; sea clutter suppression; Sea surface; Space-time adaptive processing; space–time adaptive processing (STAP); sparse reconstruction; Statistical analysis; Statistical methods; Surface reconstruction; Training
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2021.3077672

Due to nonlinear varying motions of the physical sea surface, sea clutter suppression is challenging with the broadened and rapidly changed Doppler spectrum content. In this article, an effective sea clutter suppression scheme based on multiple-input-multiple-output (MIMO) radar is presented via studying sea clutter features and reconstructing clutter spatial-Doppler spectrum accurately. To depict complicated Doppler features more precisely, the multistage Gaussian mixture model (GMM) is first developed in this article. In terms of exploring the dominant Doppler characteristics, GMM is more applicable in dynamic sea environments, including main swell and breaking waves. Afterward, for accurate reconstruction of clutter spectrum, the MIMO radar Gaussian mixture prior space-time adaptive processing (GMP-STAP) scheme is proposed for accurate estimation of clutter features. The Kullback-Leibler (KL) divergence is developed, and approximate distribution of posterior probability density functions (PDF) can be explored with low information loss based on complicated prior structure. Closed-form solutions of variables and parameters can be formulated through the minimization of KL divergence. The robust multistage Doppler power spectrum of sea clutter can be reconstructed accordingly. Moreover, considering that the independent and identically distributed (IID) property is easily broken among reference samples, the effective cross-correlation analysis is launched to keep statistical characteristics identical and reduce statistical errors. Finally, according to the experiments, accurate reconstruction of sea clutter features can be realized, and effective sea clutter suppression performance can be illustrated.