Abstract:

Aiming at the parameter estimation of KK distribution, a semi-experiential algorithm is introduced firstly, and then a new algorithm based on particle swarm optimization is presented. This algorithm takes the discrepancies between the histogram of the clutter data and probability density function of KK distribution as the cost function to search the optimal parameters of KK using the particle swarm optimization algorithm. The performance of the semi-experiential algorithm is analyzed for different mixing coefficient using Monte Carlo simulations. And then the influence of some factors, such as the number of clutter data samples, in the new algorithm on the estimation precision is evaluated. At the end, this new algorithm is applied to some real synthetic aperture radar clutter data. The simulation results clearly show the good performance of this algorithm, and the KK distribution is validated to model the radar clutter much better than some common used models, such as K-distribution.