LAUSR

The carrier-free ultra-wideband (UWB) radar (impulse radar) has seen a recent surge of interest. In this letter, a novel recognition system for vehicles based on the carrier-free UWB radar is proposed, in which the sparse representation is introduced as an effective feature extraction method. Based on the original K-SVD algorithm, we provide a new dictionary learning (DL) idea. Instead of only embedding discrimination criteria in the objective function, we expand and improve the optimization procedure of the K-SVD algorithm. Moreover, to alleviate the impact of the signal diversity on the recognition performance, we propose a hierarchical code constraint (HCC) and bind it to the improved K-SVD model. In this way, signals from the same class but with different distributions will be represented by the corresponding dictionary atoms. Extensive experiments prove the improved K-SVD with an HCC-IKSVD can effectively take both reconstruction capability and discriminative power of the dictionary into consideration.