short-term and long-term dependency modeling of consecutive range profiles for radar target recognition

high resolution range profile (hrrp) is being known as one of the most powerful tools for radar target recognition. the main problem with range profile for radar target recognition is its sensitivity to aspect angle. to overcome this problem, consecutive samples of hrrp (or feature vectors extracted from them)were assumed to be identically independently distributed (iid) in small frames of aspect angles in most of the related works. in this work, an alternative dynamic system based method is proposed to overcome the limitations of the recent methods in the field such as the independency assumption. two different feature extraction schemes using spectral features and pca coefficients is utilized. when the first scheme is employed, modeling is done in short-term, whereas long-term modeling is done while employing the second scheme. experimental results using simulation data shows superiority of our model over common models using either approach to feature extraction.