Parameters Estimation for Generalized K-Distributed Clutter Model

In this paper, a novel parameter estimation algorithm is provided for generalized k-distributed clutter model based on the parameter decoupling technology. This estimation algorithm converts a 4-D nonlinear optimization problem into a 1-D optimization problem, which can not only improve the estimation performance, but also reduce the calculation burden and the number of necessary clutter samples. Finally, the simulation experiment is carried out to prove the validity and veracity of the algorithm proposed in this paper