Visible to the public Compressive Detection of Random Signals from Sparsely Corrupted Measurements

TitleCompressive Detection of Random Signals from Sparsely Corrupted Measurements
Publication TypeConference Paper
Year of Publication2018
AuthorsTian, Yun, Xu, Wenbo, Qin, Jing, Zhao, Xiaofan
Conference Name2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC)
Date Publishedaug
ISBN Number978-1-5386-6067-6
Keywordscomposability, compressed sensing, Compressive detection, compressive detection problem, compressive sampling, CS reconstruction, CSP techniques, Current measurement, Cyber physical system, cyber physical systems, data amount, dense noise, detection, Detectors, Noise, privacy, pubcrawl, random signals, resilience, Resiliency, signal detection, signal detection problem, Signal processing, signal processing tasks, signal reconstruction, sparse error, Sparse matrices, sparsely corrupted measurements, Task Analysis, Testing

Compressed sensing (CS) integrates sampling and compression into a single step to reduce the processed data amount. However, the CS reconstruction generally suffers from high complexity. To solve this problem, compressive signal processing (CSP) is recently proposed to implement some signal processing tasks directly in the compressive domain without reconstruction. Among various CSP techniques, compressive detection achieves the signal detection based on the CS measurements. This paper investigates the compressive detection problem of random signals when the measurements are corrupted. Different from the current studies that only consider the dense noise, our study considers both the dense noise and sparse error. The theoretical performance is derived, and simulations are provided to verify the derived theoretical results.

Citation Keytian_compressive_2018