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Improved sequential MUSIC

机译：改进的顺序音乐

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摘要

MUSIC (multiple signal classification) is one of the most frequently considered methods for source location using sensor arrays. Among the location methods based on one-dimensional search, MUSIC has excellent performance. In fact, no other one-dimensional method that may outperform MUSIC (in large samples) was known to exist. Our goal here is to introduce such a method, called improved sequential MUSIC (IES-MUSIC), which is shown to be strictly more accurate than MUSIC (in large samples). First, a class of sequential MUSIC estimates is introduced, which depend on a scalar-valued user parameter. MUSIC is shown to be a special case of estimate in that class, corresponding to a value of zero for the user parameter. Next, the optimal user parameter value, which minimizes the asymptotic variance of the estimation errors, is derived. IES-MUSIC is the method based on that optimal choice of the user parameter. Simulation results which lend support to the theoretical findings are included
机译：MUSIC（多种信号分类）是使用传感器阵列进行源定位的最常用方法之一。在基于一维搜索的定位方法中，MUSIC具有出色的性能。实际上，没有其他任何一维方法可以胜过MUSIC（在大型样本中）。我们的目标是引入一种称为改进的顺序MUSIC（IES-MUSIC）的方法，该方法被证明比MUSIC精确得多（在大样本中）。首先，引入了一类顺序MUSIC估计，该估计取决于标量值用户参数。在该类中，MUSIC被证明是估计的一种特殊情况，对应于用户参数的零值。接下来，导出使估计误差的渐近方差最小的最佳用户参数值。 IES-MUSIC是基于用户参数的最佳选择的方法。仿真结果为理论发现提供了支持

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