A novel forward-backward smoothing-based learning subspace method for recognition of radar targets
This paper proposes a novel Forward-Backward Smoothing-Based Leaning Subspace Method (FBSLSM), which can satisfy the requirements of being insensitive to the order of presentation of the training samples, and is of faster convergence speed. This method is applied to the recognition of simulating High Resolution Radar (HRR) targets (two for ships, one for chaff). Moreover, for recognition of HRR targets, a new selection method of subspace dimensionality is given. The computer simulating experiments show that the corresponding performance of proposed FBSLSM such as rate of correct recognition and convergence speed is better than that of the ALSM presented by Oja.
subspace radar targets self-supervised learning pattern recognition selection of subspace dimensionality forward-backward smoothing disordered learning