THIRD-ORDER CUMULANTS RECONSTRUCTION FROM COMPRESSIVE MEASUREMENTS

被引：0

作者：

Wang, Yanbo ^{[1
]}

Tian, Zhi ^{[1
]}

机构：

[1] George Mason Univ, Dept Elect & Comp Engn, Fairfax, VA 22030 USA

来源：

2020 54TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS | 2020年

关键词：

Cumulants; moments; third-order statistics; compressive sampling; compressed sensing; HIGHER-ORDER STATISTICS;

D O I：

10.1109/IEEECONF51394.2020.9443443

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Estimation of popular third-order statistics hinges on the availability of a huge amount of data records, which brings severe challenges on the data collecting hardware in terms of considerable storage costs, overwhelming energy consumption and unaffordably high sampling rate especially when dealing with wideband signals. To overcome these challenges, we propose a systematic approach to estimate the third-order statistics of wide-sense stationary signals under the compressive sampling framework. Without any sparsity constraints on the signal or cumulants, we provide the sufficient conditions for lossless third-order statistics recovery via solving simple least-squares, and analyze the strongest achievable compression ratio in closed-form.

引用

页码：1380 / 1384

页数：5

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