Algorithms of signal parameter estimation based on the theory of Compressive Sensing and their computer simulation

The problem of estimating unknown signal parameters in the presence of noise is a classical problem in statistical radio engineering. There are many algorithms for solving this problem. Many of these algorithms are based on the use of well-known statistical optimality criteria in order to improve the estimation efficiency. Therefore, they acquire a high computational complexity. In this article, in order to reduce computational complexity, it is proposed to modernize the classical correlation algorithm based on the new Compressive Sensing theory, which has been actively developed in recent decades. The article presents a new simplified algorithm of signal parameters estimation, outlines operational speed benefits, and compares the efficiency of the proposed algorithm to the efficiency of the classical algorithm as well as some other algorithms. The proposed algorithm has also been compared to the classical one according to computational complexity. Practical implementation of the proposed algorithm requires much smaller number of arithmetical operations with some deterioration in unknown parameter estimation accuracy.