2021/04/14 by Weijian Liu, Zhaojian Zhang, Liu, Weijian +7 · 1 citation
Computer Science · Engineering · #Applications (stat.AP) #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Radar Systems and Signal Processing #Signal Processing (eess.SP) #Sparse and Compressive Sensing Techniques #Target Tracking and Data Fusion in Sensor Networks #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2105.13282
openalex publication_date 2021/04/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
In this paper, we reconsider the problem of detecting a matrix-valued rank-one signal in unknown Gaussian noise, which was previously addressed for the case of sufficient training data. We relax the above assumption to the case of limited training data. We re-derive the corresponding generalized likelihood ratio test (GLRT) and two-step GLRT (2S--GLRT) based on certain unitary transformation on the test data. It is shown that the re-derived detectors can work with low sample support. Moreover, in sample-abundant environments the re-derived GLRT is the same as the previously proposed GLRT and the re-derived 2S--GLRT has better detection performance than the previously proposed 2S--GLRT. Numerical examples are provided to demonstrate the effectiveness of the re-derived detectors.