Estimating computational complexity of the square-root covariance filtering algorithm for discrete systems with multiplicative noises

The article considers the task of evaluating the computational complexity of the square-root covariance algorithm of optimal linear filtering for discrete linear stochastic systems with additive and multiplicative noises, which can be used in medical image processing algorithms. The developed algorithm is algebraically equivalent to the standard covariance filter, but has the best computational properties inherent in square-root algorithms. The article presents the results of evaluating its computational complexity in comparison with the standard covariance filtering algorithm.