Circuits, Systems, and Signal Processing

, Volume 38, Issue 2, pp 918–929 | Cite as

Preprocessed Compressive Adaptive Sense and Search Algorithm

  • Yun LinEmail author
  • Qiang Hu
Short Paper


To address the performance degradation due to the cancellation of positive and negative entries of adaptive compressed sensing algorithms, we propose a simple adaptive sensing and group testing algorithm for sparse signals. The algorithm, termed “preprocessed compressive adaptive sense and search”, divides the input signal into two equal-length subsignals that include only non-positive or non-negative entries through a nonlinear preprocessing process and subsequently generates adaptive sensing and group testing. The proposed algorithm is computationally less intensive than non-adaptive compressed sensing and requires only klog(n/k) measurements to recover a k-sparse signal of dimension n. A theoretical guarantee for signal recovery is provided, and the provided numerical examples demonstrate a better recovery performance than non-adaptive sensing and compressive adaptive sense and search algorithms for the same signal-to-noise ratio.


Compressed sensing Adaptive measurement Sparse Preprocessing CASS 


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Chongqing Key Lab of Mobile Communications TechnologyChongqing University of Posts and TelecommunicationsChongqingPeople’s Republic of China

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