Visible to the public Detection with compressive measurements corrupted by sparse errors

TitleDetection with compressive measurements corrupted by sparse errors
Publication TypeConference Paper
Year of Publication2017
AuthorsXu, W., Yan, Z., Tian, Y., Cui, Y., Lin, J.
Conference Name2017 9th International Conference on Wireless Communications and Signal Processing (WCSP)
ISBN Number978-1-5386-2062-5
Keywordscomposability, compressed sensing, compressive measurement detection problem, compressive sampling, Cyber-physical systems, Detectors, Distortion measurement, Image reconstruction, impulse noise, interference (signal), measurement uncertainty, narrowband interference, Noise measurement, Nyquist-rate samples, privacy, pubcrawl, resilience, Resiliency, signal detection, Signal processing, signal reconstruction, signal reconstruction algorithms, signal sampling, sparse error corruption, Sparse matrices, sparse signal representation

Compressed sensing can represent the sparse signal with a small number of measurements compared to Nyquist-rate samples. Considering the high-complexity of reconstruction algorithms in CS, recently compressive detection is proposed, which performs detection directly in compressive domain without reconstruction. Different from existing work that generally considers the measurements corrupted by dense noises, this paper studies the compressive detection problem when the measurements are corrupted by both dense noises and sparse errors. The sparse errors exist in many practical systems, such as the ones affected by impulse noise or narrowband interference. We derive the theoretical performance of compressive detection when the sparse error is either deterministic or random. The theoretical results are further verified by simulations.

Citation Keyxu_detection_2017