A Comparison of Three Swarm-Based Optimization Algorithms in Wind Turbine Radar Clutter Micro-Motion Parameters Estimation.

Wind turbine radar clutter seriously affects the detection performance of radar. The effective estimation of micro-motion parameters is an important part of wind turbine radar clutter suppression. Aiming at the problem of wind turbine radar clutter micro-motion parameters estimation, the micro-motion parameters estimation effectiveness of three swarm-based optimization algorithms, namely Particle Swarm Optimization (PSO) algorithm, Artificial Bee Colony (ABC) algorithm and Grey Wolf Optimizer (GWO), is compared in this paper. Firstly, the basic principles of three swarm-based optimization algorithms are introduced. Then the wind turbine radar clutter model is established to determine the micro-motion parameters to be estimated and the steps of micro-motion parameters estimation are given. Finally, the micro-motion parameters estimation results of the three algorithms are compared and analyzed through simulation experiments. The results show that the three swarm-based optimization algorithms can estimate the micro-motion parameters. The GWO has the smallest estimation error, which has the potential value for practical wind turbine radar clutter suppression.