Sparse signal representation (also known as sparse representation) is a very interesting research field in the field of signal processing in recent 20 years. Many research papers and seminars show the vigorous development in this field. The purpose of signal sparse representation is to use as few atoms as possible to represent the signal in a given over-complete dictionary, so that we can get a more concise representation of the signal, which makes it easier for us to obtain the information contained in the signal and facilitate further processing of the signal, such as compression coding.

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