PCL 운용환경에서 통계적 가설 검정을 활용한 저피탐 표적 식별
KCI
- Title
- PCL 운용환경에서 통계적 가설 검정을 활용한 저피탐 표적 식별
- Authors
- 이경민; 최인오; 김민; 박정기; 곽현규; KIM, KYUNG TAE
- Date Issued
- 2019-11
- Publisher
- 한국전자파학회
- Abstract
- In this paper, we propose a new framework of target classification for a passive coherent location(PCL) radar network. The framework uses radar cross sections(RCSs) obtained from multiple bistatic radars, and is computationally more efficient compared with the conventional method that uses time-varying RCSs obtained from a monostatic radar. Firstly, we construct the training set of the bistatic RCS distribution of each target using the scenario-based method and a PCL radar network with multiple transmitters and a receiver. Next, assuming that a test sequence consists of bistatic RCSs, we classify each target using statistical hypothesis test algorithms, such as Z-test, Wilcoxon test, and sign test. The proposed framework demonstrated better performance than the conventional method, in terms of computational efficiency.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/110641
- DOI
- 10.5515/KJKIEES.2019.30.11.911
- ISSN
- 1226-3133
- Article Type
- Article
- Citation
- 한국전자파학회 논문지, vol. 30, no. 11, page. 911 - 921, 2019-11
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