As can be seen from (2), the feature dimension after SVCA transform is independent from the number of targets.
Different from the VCA feature sets which are linearly separable between any two sets, the SVCA feature set calculated on the kth class is linearly separable with the rest of K-1 classes, while the linear separability among K-1 classes is not referred.
For the reason incoherent average is calculated firstly in a subset of HRRPs from a target-aspect sector without scatterers' motion through range cells , then feature extraction is performed with SVCA. No less than K-1 binary-classification classifiers are needed for the classification of K targets, which can be organized as serial mode or parallel mode.
The transformation matrix is obtained with SVCA based on [S.sub.A], and linearly separable feature sets [F.sub.A] and [F.sub.B] can be acquired through feature extraction performed on [S.sub.A] and [S.sub.B].
The spatial distributions of the raw data and SVCA features are shown in Figure 1.
Classification experiments with linear SVM classifiers are carried out based on raw data, VCA and SVCA features.
Additionally, the dimensions of the SVCA features for the four vehicle targets are 32, 42, 54, and 36, respectively, which are much lower than the raw HRRPs (128).
It is also seen from the performance comparison between SVCA-based and VCA-based method that the CCRs are comparable for [epsilon] > 0.6, which indicates that the separability of SVCA features is nearly the same as VCA features.
Linearly separable features are extracted with SVCA, and target classification is implemented with linear SVM classifiers.
The proof of the linear separability for SVCA features.
All entries underwent two main stages of assessment, including nominations from SVCA members and associates and two rounds of presentations and judging by an independent panel of judges.
The team of expert judges were made up of industry venture capitalists and private equity veterans, such as members of the SVCA "Hall of Fame" like Mr.