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Kumaresan和Tufts提出的Mini－Norm算法（KT）通过对空间协方差矩阵进行特征分解，随后构造噪声子空间向量来求解方位。VictorT．Ermolaev和AlexB．Gershman在此基础上，利用指数基替代特征向量基建立了一种选代算法（VA），避免特征分解过程，从而减少了原算法的运算量。本文对该算法再进一步改进（FAST算法），合理简化参数设置，有效解决VA算法中的参量选取和选代收敛问题，使运算量又得到大幅度降低，更接近工程应用。文中介绍了FAST算法各参量的详细设置过程，利用计算机仿真与KT算法的性能进行统计分析比较。我们开展水池实验研究和VLSI实时运算实验来验证其实用性。各种结果均表明FAST算法性能优越，运算小，具有良好的应用前景。
The Mini-Norm algorithm (KT) proposed by Kumaresan and Tufts solves the position by constructing the noise subspace vector by decomposing the spatial covariance matrix. VictorT. Ermolaev and AlexB. Based on this, Gershman established an alternative algorithm (VA) using exponential basis instead of eigenvector basis to avoid the feature decomposition process, thus reducing the computational complexity of the original algorithm. In this paper, the algorithm is further improved (FAST algorithm), to simplify the parameter setting reasonably, to effectively solve the problem of parameter selection and generation convergence in the VA algorithm, so that the computational complexity is greatly reduced and closer to the engineering application. In this paper, the detailed setting process of each parameter of FAST algorithm is introduced, and the performance of computer simulation and KT algorithm are compared and analyzed statistically. We conducted basin experiments and VLSI real-time computing experiments to verify its usefulness. Various results show that the FAST algorithm has the advantages of superior performance, small operation and good application prospect.