### Abstract

The problem of weak signal detection in Gaussian noise is addressed in the Neyman-Pearson framework with compressive measurements. A locally optimum detector is first devised assuming that the signal is non-sparse by approximating the test statistic around zero using Taylor series, which is a good estimate only in a small radius around the point of interest. When the signal is sparse, it is shown that the performance of this test degrades. To improve its performance, a new test is devised by deriving the Padé approximation (instead of the Taylor series expansion) of the test statistic around the neighborhood of zero. Padé approximants estimate functions as the rational quotient of two lower-degree polynomials, and consistently have a wider radius of convergence than the Taylor series. The performance of the Padé-approximated test is better than its Taylor series counterpart, and is comparable to the conventional locally optimum test with uncompressed measurements. Simulation results are presented to support the analytical findings of the work.

Original language | English (US) |
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Journal | IEEE Signal Processing Letters |

DOIs | |

State | Accepted/In press - Nov 28 2017 |

### Keywords

- compressive sensing
- Detectors
- Locally optimum detection
- Manganese
- Noise measurement
- Padé approximation
- Signal detection
- Signal to noise ratio
- Simulation
- Taylor series

### ASJC Scopus subject areas

- Signal Processing
- Electrical and Electronic Engineering
- Applied Mathematics

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## Cite this

*IEEE Signal Processing Letters*. https://doi.org/10.1109/LSP.2017.2778422