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A new system identification technique for harmonic decomposition

机译：一种新的谐波分解系统​​识别技术

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摘要

An extended and unifying system identification technique is presented for a class of systems that include all main signal models that arise in the harmonic decomposition problem. This technique unifies and extends the previously developed system identification techniques which are improvements on the Pisarenko harmonic decomposition (or, its spatial dual, MUSIC) as they arise in arrays of sensors. The advantages of the technique and some of its specializations include having no assumptions of stationarity on the stochastic processes involved. Another contribution of this technique is that it can also be used without any resort to probability theoretic concepts, thus bypassing the approximation of autocorrelations via time averages, yielding the system parameters exactly. This technique can be utilized to determine the dominant modes of vibrations of flexible structures as well. An analogy is established between arrays of sensors for target signal returns and those that can be used for vibrations in flexible structures. This enables the results developed for each one of these problems to be applied to the other.
机译：针对一类系统提出了一种扩展且统一的系统识别技术，该系统包括在谐波分解问题中出现的所有主要信号模型。该技术统一并扩展了先前开发的系统识别技术，该技术是对Pisarenko谐波分解（或其空间对偶，MUSIC）的改进，因为它们出现在传感器阵列中。该技术及其某些专业化的优点包括，对所涉及的随机过程没有平​​稳性的假设。该技术的另一个贡献是，它也可以在不依赖于概率论概念的情况下使用，从而通过时间平均值绕过自相关的近似，从而精确地得出系统参数。该技术也可用于确定柔性结构的主要振动模式。在用于目标信号返回的传感器阵列与可用于柔性结构振动的传感器阵列之间建立一个类比。这使得针对这些问题中的每一个得出的结果都可以应用于另一个。

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