RCS Uncertainty Quantification Using the Feature Selective Validation Method
| Autor: | Dijun Liu, Baofa Wang, Ning Fang, Min Su |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Radar cross-section
Computer science 020208 electrical & electronic engineering Monte Carlo method 020206 networking & telecommunications 02 engineering and technology Condensed Matter Physics computer.software_genre Atomic and Molecular Physics and Optics Field (computer science) Data modeling Feature (computer vision) Histogram 0202 electrical engineering electronic engineering information engineering Data mining Electrical and Electronic Engineering Uncertainty quantification computer Simulation Uncertainty analysis |
| Zdroj: | IEEE Transactions on Electromagnetic Compatibility. 60:657-664 |
| ISSN: | 1558-187X 0018-9375 |
| Popis: | Uncertainty quantification is an important issue in the field of radar cross section (RCS) research. To quantify the impact of specific uncertainty factor on RCS, a novel approach based on the feature selective validation (FSV) method combined with Monte Carlo (MC) method is proposed in this paper. MC method is applied as the basic framework for uncertainty analysis, and FSV is initially employed to compare the results derived from sufficient uncertainty simulations. To facilitate and enhance the massive data assessment, a novel single and direct indicator of FSV is proposed as a quantitative descriptor of data uncertainty. The feasibility of the proposed method in RCS uncertainty quantification is benchmarked through many RCS evaluation examples. The impact of attitude uncertainty on the target RCS, including the scene of dynamic flight, is also studied by the proposed method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|