The Geometry of Signal Detection with Applications to Radar Signal Processing

作者：Yongqiang Cheng, Xiaoqiang Hua, Hongqiang Wang, Yuliang Qin, Xiang Li · 发表于：Entropy · 年份：2016 · DOI：10.3390/e18110381 · 被引用次数：59 · 研究领域：Distributed Sensor Networks and Detection Algorithms、Advanced Statistical Methods and Models、Statistical Mechanics and Entropy

The problem of hypothesis testing in the Neyman–Pearson formulation is considered from a geometric viewpoint. In particular, a concise geometric interpretation of deterministic and random signal detection in the philosophy of information geometry is presented. In such a framework, both hypotheses and detectors can be treated as geometrical objects on the statistical manifold of a parameterized family of probability distributions. Both the detector and detection performance are geometrically elucidated in terms of the Kullback–Leibler divergence. Compared to the likelihood ratio test, the geometric interpretation provides a consistent but more comprehensive means to understand and deal with signal detection problems in a rather convenient manner. Example of the geometry based detector in radar constant false alarm rate (CFAR) detection is presented, which shows its advantage over the classical processing method.