The Rice distribution is applied as a mathematical model in the study of various problems in science and technology. The main task in applications is to estimate the parameters of the Rice distribution from a sample of the measured signal and to separate the parameters of the deterministic signal and noise based on these estimates. Parameter estimation is mainly performed using the maximum likelihood method and the method of moments. However, as is known, both methods often lead to solving systems of equations containing special functions, so additional computational resources are used for the solution. One of the methods to overcome these difficulties is the development of simple, yet sufficiently effective empirical formulas (EFs) that are competitive in accuracy with known algorithms for estimating the parameters of the Rice distribution. This work is devoted to solving this problem.
Asatryan et al. (Mon,) studied this question.