Estimation of sea clutter parameter based on the multi-feature-point model validation method

2021 ◽  
Vol 15 (04) ◽  
Author(s):  
Xiaofei Han ◽  
Qi Zhang ◽  
Purui Zhang ◽  
Yadan Yang ◽  
Xin Zhang ◽  
...  
2019 ◽  
Vol 158 ◽  
pp. 394-400
Author(s):  
Taha Houcine Kerbaa ◽  
Amar Mezache ◽  
Houcine Oudira

Author(s):  
Gernoth Götz ◽  
Oldrich Polach

This article presents an evaluation of the model validation method that was provided as the output of the European research project DynoTRAIN and implemented in the recently revised European standard EN 14363. The input parameters of the validation method, namely the section length, number, and selection of sections as well as the selected parameters of the simulation models are varied. The evaluation shows that a single section that provides a large deviation between simulation and measurement can, in rare cases, influence the results of the overall validation. Nevertheless, the investigations demonstrate a good robustness, as the final validation result is very rarely influenced by the variation of sections selected for validation, by the use of a higher number of sections than the minimum of 12, or by longer sections than that specified for on-track tests in accordance with EN 14363. The validation methodology is also able to recognize the errors in vehicle model parameters, if the errors have a relevant influence on the behaviour of the running dynamics of the evaluated vehicle.


2011 ◽  
Vol 133 (4) ◽  
Author(s):  
Zhenfei Zhan ◽  
Yan Fu ◽  
Ren-Jye Yang ◽  
Yinghong Peng

Validation of computational models with multiple correlated functional responses requires the consideration of multivariate data correlation, uncertainty quantification and propagation, and objective robust metrics. This paper presents an enhanced Bayesian based model validation method together with probabilistic principal component analysis (PPCA) to address these critical issues. The PPCA is employed to handle multivariate correlation and to reduce the dimension of the multivariate functional responses. The Bayesian interval hypothesis testing is used to quantitatively assess the quality of a multivariate dynamic system. The differences between the test data and computer-aided engineering (CAE) results are extracted for dimension reduction through PPCA, and then Bayesian interval hypothesis testing is performed on the reduced difference data to assess the model validity. In addition, physics-based threshold is defined and transformed to the PPCA space for Bayesian interval hypothesis testing. This new approach resolves some critical drawbacks of the previous methods and adds some desirable properties of a model validation metric for dynamic systems, such as symmetry. Several sets of analytical examples and a dynamic system with multiple functional responses are used to demonstrate this new approach.


Sign in / Sign up

Export Citation Format

Share Document