An Improved Space-time Adaptive Processing Algorithm Based on Low Rank Approximation
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摘要: 针对非均匀杂波情况下的空时自适应处理的小样本问题,该文提出一种基于权矩阵低秩逼近的空时自适应处理方法。与传统的低秩逼近算法不同,利用空时导向矢量特殊的克罗累计性,该文重新构造新的权矩阵,使得该权矩阵的行数与列数尽可能地相近或相同,以减少算法所需的样本个数和计算量。采用低秩逼近方法对新构造的权矩阵进行表示,则原二次优化问题转化为求解一个双二次代价函数问题。实验表明,改进的空时权矩阵低秩逼近方法能有效地提高空时自适应处理的收敛速度和降低算法复杂度。Abstract: To handle the small sample support problem under the heterogeneous clutter environment, a fast convergence Space-Time Adaptive Processing (STAP) algorithm based on low-rank approximation of the weight matrix is proposed. Unlike the traditional Low-Rank Approximation (LRA) algorithm for STAP, the weight matrix is reconstructed so that the numbers of its columns and rows are the same or close to each other by utilizing the special Kronecker property of the space time steering vector, which to reduce the requirement of samples and computational load. By using the low-rank approximation method to approximate the adaptive weight matrix, the original quadratic optimal problem transforms into a bi-quadratic optimal problem. Experimental results verify that the Improved LRA (ILRA) method can improve the convergence rate and reduce the computational load.
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