## 4 Sensing-Oriented Issues and Solutions
### Feature Extraction and Characterization  
The process to extract features from RCS and related parameters is an inverse problem [141; 142]. It is of paramount importance to mitigate multiple influences to get robust sensing information from the RFID sensor system. Several interferences including sample surface geometry, multiple scattering because of nearby objects, and reader distance between the tag and the reader are mixed and thus need to be separated. The antenna sensor can sense the defect through the extracted feature from sensing variables, but the interferences change the impedance and radiation pattern of the antenna and therefore force the change in sensing variables as well [51]. A robust sensing variable can be selected accordingly as in Section 4.3. Feature extraction method should be utilized to for solve the ill-posed problem and then carefully estimate the defect.  
As seen in Section 4.1, RFS is widely used to characterize defects because of its simplicity and robustness [27]. However, the feature of RFS only considers the local structure of the data manifold and thus could lose important information existing in the global structure of the given data [143]. More importantly, this feature challenges the antenna design and installation because the high-quality factor is required [144].  
As known to us, the characteristic mode analysis is a method used in electromagnetics, which gives insight into the potential resonant characteristics of a structure by finding and examining the inherent modes of the structure [145]. The input admittance of the antenna at a feed point m can be expressed as a summation of the modal admittances [146]:  
\[\mathrm{Y_{in}}[\mathrm{m}]=\sum_{\mathrm{n}}\frac{\mathrm{J_{n}^{2}}[\mathrm{ m}]}{1+\lambda_{n}^{2}}(1-\mathrm{j}\lambda_{n}). \tag{8}\]  
Physically, the eigenvalue \(\lambda_{n}\) represents the net stored energy of the mode and take real values from \(-\infty\) to \(+\infty\), with negative and positive values representing net electric and magnetic energy storage, respectively. The characteristic currents \(J_{n}\) are the real-valued eigencurrents (eigenvectors) of the mode on the structure and give rise to the modal radiation patterns and other field quantities [147]. The resulting eigenvalues and eigenvectors, i.e., \(\lambda_{n}\) and \(J_{n}\), are frequency dependent but independent of the excitation.  
The above insight can build a bridge between impedance and extracted feature [148]. For this reason, pattern recognition methods can be used for feature extraction. In particular, as typical supervised learning algorithms, PCA and independent component analysis (ICA) are broadly investigated to be a feature extractor because of their ability to find the eigenvector that dominates the variance and statistically separate the desired signal from interferences. At the same time, the experienced limitations in term of uncertainties and achievable resolutions suggest a potential usage of low-cost analogue RFID sensors for providing a few-level sets of things. As a result, the analogue RFID sensing can be hence addressed as a classification problem and accordingly well assessed classification algorithms, like the PCA could be applied to multiple indicators to improve the resolution and/or the detection robustness [133]. Enhancing both the sensitivity and robustness was demonstrated for corrosion detection and characterization in conjunction with PCA method [51].