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Performance analysis for time-frequency MUSIC algorithm in presence of both additive noise and array calibration errors

机译:同时存在加性噪声和阵列校准误差的时频MUSIC算法的性能分析

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This article deals with the application of Spatial Time-Frequency Distribution (STFD) to the direction finding problem using the Multiple Signal Classification (MUSIC)algorithm. A comparative performance analysis is performed for the method under consideration with respect to that using data covariance matrix when the received array signals are subject to calibration errors in a non-stationary environment. An unified analytical expression of the Direction Of Arrival (DOA) error estimation is derived for both methods. Numerical results show the effect of the parameters intervening in the derived expression on the algorithm performance. It is particularly observed that for low Signal to Noise Ratio (SNR) and high Signal to sensor Perturbation Ratio (SPR) the STFD method gives better performance, while for high SNR and for the same SPR both methods give similar performance.
机译:本文讨论了使用多信号分类(MUSIC)算法将空间时频分布(STFD)应用于测向问题。当接收到的阵列信号在非平稳环境中经受校准误差时,针对使用数据协方差矩阵的方法对所考虑的方法进行比较性能分析。两种方法均得出了到达方向(DOA)误差估计的统一解析表达式。数值结果表明,参数干预导出的表达式对算法性能的影响。特别要注意的是,对于低信噪比(SNR)和高信噪比(SPR),STFD方法提供更好的性能,而对于高SNR和相同的SPR,这两种方法都提供相似的性能。

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