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Method of Principal Informative Components in Problems of Statistical Measurements of Signal Parameters (Systematic Review)

机译:信号参数统计测量问题中的主要信息组件的方法(系统审查)

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Abstract The method of Principal Informative Components (PIC) is presented for problems of statistical measurements, where the signal to be measured cannot be directly observed. Such situations include image reconstruction, system identification, communication channel reversal, media tomography, etc. The common feature of such problems, usually, is instability of their solutions to small variations of initial data that generally require the attraction of special methods of regularization. The basic principle of PIC method consists in employing decomposition of signals in special bases that were formed from eigenvectors of Fischer’s information operator. These bases are related to the method of Principal Components Analysis (PCA), which is well known in statistics, however, they have a somewhat different meaning as compared to the PCA method. The review indicates that by using the special procedures for selecting coordinate vectors, it is possible, first, to guarantee the signal estimation stability to unpredictable factors of problem and, second, to ensure a significant reduction of total measurement error as compared to the “direct” signal estimation, i.e., without the use of basis notions. The review presents a substantiation of PIC method application for problems of linear and nonlinear estimation. The composite technique of coordinate basis optimization is also considered that combines advantages of the physical approach (obviousness and effectiveness) with advantages of statistically informative approach (minimization of statistical errors). The specified technique is based on projecting the arbitrary coordinate basis on PIC subspace. As a result, the range of possible fluctuations of signal estimation is reduced and the upper bound of statistical error of signal measurement is lowered. Some numerical estimates of the PIC method efficiency are given using the example of problem of medium acoustic tomography that confirms the general theoretical conclusions. The review includes the analysis of some information technologies, where the ideas of PIC method hold a good promise for practical application. In particular, it is suggested that one of such promising fields can be MIMO systems that play an important part in 5G wireless access systems.
机译:摘要介绍了主信息组件(PIC)的方法,用于统计测量问题,其中无法直接观察到要测量的信号。这种情况包括图像重建,系统识别,通信信道反转,媒体断层扫描等。这种问题的共同特征通常是它们的解决方案的不稳定性,对通常需要特殊规则方法的吸引力的初始数据的小变化。 PIC方法的基本原理包括在菲舍尔信息运营商的特征向量形成的特殊碱基中使用分解。这些碱基与主要成分分析(PCA)的方法有关,其在统计中是众所周知的,然而,与PCA方法相比,它们具有稍微不同的含义。审查表明,通过使用用于选择坐标向量的特殊程序,可以首先保证信号估计稳定性与问题的不可预测因素,而是,与“直接相比,确保总测量误差显着降低总测量误差。 “信号估计,即不使用基础概念。审查介绍了PIC方法应用程序,用于线性和非线性估计问题。坐标基础优化的复合技术也被认为是将物理方法(显而易见的和有效性)的优点与统计信息丰富的方法(最小化统计误差最小)相结合。指定的技术基于在PIC子空间上投影任意坐标基础。结果,降低了信号估计的可能波动范围,并且信号测量的统计误差的上限降低。使用中等声学断层摄影问题的示例给出了PIC方法效率的一些数值估计,证实了一般理论结论。该审查包括分析一些信息技术,其中PIC方法的思想对实际应用具有良好的承诺。特别地,建议这样的有希望的领域可以是MIMO系统,其在5G无线接入系统中发挥重要部分。

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