Radar target HRRP recognition based on reconstructive and discriminative dictionary learning

• The reconstructive and discriminative power of atom is considered during the update.
• We construct an objective function based on SRC criterion.
• Sparse coding coefficients of samples are updated using class-optimal SVD vectors.
• RDDLSRCC is more robust to the variation of target aspect and noise׳ effect.

A novel dictionary learning algorithm, namely reconstructive and discriminative dictionary learning based on sparse representation classification criterion (RDDLSRCC), is proposed for radar target high resolution range profile (HRRP) recognition in this paper. The core of proposed algorithm is to incorporate the reconstructive power and discriminative power of atoms during the update of atoms. By constructing the objective function based on sparse representation classification criterion (SRCC), the discriminative performance of atoms can be improved while preserving the same-class reconstruction ability of atoms and reducing their reconstruction contribution to other classes. Moreover, the sparse coding coefficients of samples are updated using class-optimal SVD vectors of class-reconstruction residual matrix, thereby accelerating convergence. Compared with other dictionary learning algorithms, RDDLSRCC is more robust to the variation of target aspect and noise׳s effect. The extensive experimental results on the measured data illustrate that the proposed algorithm achieves a promising target recognition performance.