(untitled) (International Journal of Engineering and Computer Science, 2017)
In this modern years compressive sensing (CS) has a very important and valuable attention in the areas of Computer vision problems, Applied mathematics, Signal processing, Optical engineering ,even in Biomedical imaging also CS has a considerable attention. The main concept about these CS is that we can represents most of the signals with only a few number of non-zero coefficients in a advisable dictionary or basis. Once we construct a signal using compressive sensing reconstruction is also needed. There variety of reconstruction algorithms that are used in the area of CS. In our paper we are mainly concentrated on a new sparse signal recovery algorithm known as sparse Kalman Tree Search(sKTS), It provides a powerful reconstruction of sparse vector if the sequence of correlated observation vectors are feasible. Finally we also make a comparison with the sKTS algorithm and other algorithms like, then check its compactability and efficiency towards compressive sensing.