论文标题
低复杂性数据模型和未知背景分布的一些检测测试
Some detection tests for low complexity data models and unknown background distribution
论文作者
论文摘要
我们考虑了几种检测情况，在替代假设下，信号接受了低复杂性模型，并且在零值和替代假设下，背景噪声的分布{norknewnature}。我们为此类情况提出了几种检测策略，这些策略依赖于外源性或内源数据。这些测试程序的灵感启发，并应用于天体物理学的两个特定问题，即从径向速度曲线和高光谱数据存储中的远处星系中检测外球星。
We consider several detection situations where, under the alternative hypothesis, the signal admits a low complexity model and, under both the null and the alternative hypotheses, the distribution of the background noise is {unknown}. We present several detection strategies for such cases, whose design relies on exogenous or on endogenous data. These testing procedures have been inspired by and are applied to two specific problems in Astrophysics, namely the detection of exoplanets from radial velocity curves and of distant galaxies in hyperspectral datacubes.
