Abstract
In this paper, a preliminary evaluation study is conducted, which aiming to investigate the radar cross-section (RCS) value that is capable to be used as an input parameter for Artificial Neural network (ANN) backpropagation for foreign object debris (FOD) size classification. The experimental work procedure for dataset acquisition is described. The FOD simulator is used as the FOD target which is made of metal cylinder shape with nine various dimensions and its RCS is defined by using Maxwell’s equation. The location varying backscattered electromagnetic field from each target is measured for RCS calibration purposes. It is found that by using the received signal from radar, which is the RCS of the target and its locations, it can be utilized as input parameters of backpropagation algorithms. The ANN classification application is to define its size by the ranges; small (-30.99 to-21 dBsm), medium (-20.99 to-11 dBsm), and large (-10.99 to 0 dBsm). The interference signal getting from measurement (22.46 to 25.2 dBsm) exhibited good reflectivity behavior. The acquired input showed to be useful for ANN for FOD size classification.
| Original language | English |
|---|---|
| Pages (from-to) | 165-173 |
| Number of pages | 9 |
| Journal | International Journal of Nanoelectronics and Materials |
| Volume | 14 |
| Issue number | Special Issue InCAPE |
| Publication status | Published - 2021 Dec |
Keywords
- Artificial neural network
- Backpropagation
- Classification
- FOD dataset
- Foreign object debris
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Electrical and Electronic Engineering