Persymmetric Adaptive Union Subspace Detection

IF 1.3 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Frontiers in signal processing Pub Date : 2021-12-15 DOI:10.3389/frsip.2021.782182
Liyan Pan, Yongchan Gao, Z. Ye, Yuzhou Lv, Ming Fang
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Abstract

This paper addresses the detection of a signal belonging to several possible subspace models, namely, a union of subspaces (UoS), where the active subspace that generated the observed signal is unknown. By incorporating the persymmetric structure of received data, we propose three UoS detectors based on GLRT, Rao, and Wald criteria to alleviate the requirement of training data. In addition, the detection statistic and classification bound for the proposed detectors are derived. Monte-Carlo simulations demonstrate the detection and classification performance of the proposed detectors over the conventional detector in training-limited scenarios.
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超对称自适应联合子空间检测
本文讨论了属于几个可能的子空间模型的信号的检测,即子空间的并集(UoS),其中产生观测信号的活动子空间是未知的。通过结合接收数据的超对称结构,我们提出了基于GLRT、Rao和Wald准则的三种UoS检测器,以减轻对训练数据的需求。此外,还推导了该检测器的检测统计量和分类界。蒙特卡罗仿真表明,在训练受限的情况下,该检测器的检测和分类性能优于传统检测器。
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