Adaptive orthogonal multi matching pursuit algorithm for signal reconstruction based on compressive sensing
基于压缩感知信号重建的自适应正交多匹配追踪算法*

The newly emerging compressive sensing theory in recent years has opened up a new path for the development of signal processing, which describes that it can reconstruct the original signal from a small amount of random sampling as long as the signal is sparse or compressible, which disobeys with the traditional Nyquist sampling theorem. Based on the study and summarize of the traditional matching algorithm, this paper presented a new adaptive orthogonal matching pursuit algorithm (AOMMP) for the reconstruction of the sparse signal. The algorithm divided each iteration into two stages for the choice of matching atoms, which accelerated the matching speed of the atom and improved the accuracy of the matching, ultimately led to exact reconstruction of the original signal. Finally, compared the AOMMP algorithm with the traditional OMP algorithm under the software simulation. Experimental results show that the AOMMP reconstruction algorithm is superior to traditional OMP algorithm on the reconstruction quality and the speed of the algorithm.