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本文论述了一种基于子空间方法的高分辨DOA估计跟踪问题的解法。该方法基于对采样数据矩阵的广义奇异值分解(GSVD)。本文讨论了GSVD的更新修正算法,在每步计算中只需有限次的运算,即可由前一次的近似分解结果计算出新的近似分解。该过程与指数加权技术相结合,可以处理信号参数估计的跟踪问题。
This paper discusses a solution to the tracking problem of high resolution DOA based on subspace method. The method is based on the generalized singular value decomposition (GSVD) of the sampled data matrix. This paper discusses the GSVD update correction algorithm, which requires only a limited number of operations in each step of the calculation to calculate a new approximate decomposition from the previous approximate decomposition result. This process, combined with exponential weighting techniques, can handle the tracking of signal parameter estimates.