Extended data:
Application:
1, compressed sensing
In order to reconstruct the original signal effectively, it is necessary to sample the signal according to Nyquist sampling theorem in the traditional way. In recent years, with the rise of sparse representation, a new theory-compressed sensing is proposed to reconstruct the original signal.
2. Target tracking
In recent years, sparse representation has been widely used in the field of target tracking. An infrared target tracking algorithm based on sparse representation model is proposed to solve the problems of low contrast between target and background, and gray features are easily affected by noise. A new target tracking method based on sparse representation is proposed and solved by L 1 norm minimization method. The experimental results show that this method has more stable performance and higher computational efficiency than the existing tracking method based on L 1 norm minimization.
In order to effectively solve the problem of target occlusion in the tracking process, a tracking method based on local sparse representation model is proposed. The experimental results show that this method is more stable and reliable than various popular tracking methods, and has good anti-occlusion, and has achieved good results in tracking infrared targets at sea.
References:
Baidu Encyclopedia-Sparse Representation