摘要
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。
A subspace-based tracking algorithm is proposed for high-resolution DOA estimation, using the generalized singular value decomposition (GSVD) of the sample data matrices. A GSVD updating procedure is presented. With this procedure, a new approximate decomposition can be computed from previous one, with finite operations at each iteration. Combined with exponetial weighting technique, this algorithm can solve DOA estimation tracking problems efficiently.
关键词
DOA估计
跟踪算法
阵列信号处理
奇异值分解
DOA estimation, Tracking algorithm, Array signal processing, Singular value decomposition