An Algorithm of Angular Superresolution Using the Cholesky Decomposition and Its Implementation Based on Parallel Computing Technology

An algorithm of angular superresolution based on the Cholesky decomposition, which is a modification of the Capon algorithm, is proposed. It is shown that the proposed algorithm makes it possible to abandon the inversion of the covariance matrix of input signals. The proposed algorithm is compared with the Capon algorithm by the number of operations. It is established that the proposed algorithm, with a large dimension of the problem, provides some gain both when implemented on a single-threaded and multithreaded computer. Numerical estimates of the performance of the proposed and original algorithm using the Compute Unified Device Architecture (CUDA) NVidia parallel computing technology are obtained. It is established that the proposed algorithm saves GPU computing resources and is able to solve the problem of constructing a spatial spectrum when the dimensionality of the covariance matrix of input signals is almost doubled.