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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Improved clutter mitigation performance using knowledge-aided space-time adaptive processing
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Improved clutter mitigation performance using knowledge-aided space-time adaptive processing

机译：使用知识辅助的时空自适应处理提高杂波缓解性能

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This paper presents a framework for incorporating knowledge sources directly in the space-time beamformer of airborne adaptive radars. The algorithm derivation follows the usual linearly-constrained minimum-variance (LCMV) space-time beamformer with additional constraints based on a model of the clutter covariance matrix that is computed using available knowledge about the operating environment. This technique has the desirable property of reducing sample support requirements by "blending" the information contained in the observed radar data and the a priori knowledge sources. Applications of the technique to both full degree of freedom (DoF) and reduced DoF beamformer algorithms are considered. The performance of the knowledge-aided beam forming techniques are demonstrated using high-fidelity simulated X-band radar data
机译：本文提出了一种将知识源直接纳入机载自适应雷达的时空波束形成器中的框架。该算法的推导是基于杂波协方差矩阵的模型，该模型使用通常的线性约束最小方差（LCMV）时空波束形成器，并具有附加约束，该模型是使用有关操作环境的现有知识计算得出的。该技术具有通过“融合”观察到的雷达数据和先验知识源中包含的信息来减少样本支持需求的理想特性。考虑了该技术在完全自由度（DoF）和简化DoF波束形成器算法中的应用。使用高保真模拟X波段雷达数据演示了知识辅助波束形成技术的性能

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