Radar target classification using the relevance vector machine

We introduce a radar target classification technique based on the relevance vector machine (RVM) using high resolution range profiles (HRRPs). Although the radar target classification problem based on the support vector machines (SVMs) applied to the hyper-dimensional feature spaces has received much attention recently, RVM-based approaches have never been appeared in the open literature so far. An RVM typically utilizes significantly fewer basis functions than a comparable SVM and therefore can carry out classification with much faster learning time, while offering many additional advantages. Our simulation results confirm that the RVM is a valid and effective alternative to the SVM, and is more suitable for radar target classification.