Self-learning parameter estimation of K-distributed clutter using
nonlinear GBDT model
In this letter, a self-learning method using gradient boosting decision
tree (GBDT) is proposed to estimate two parameters of K-distributed sea
clutter. Different from the traditional methods using limited two
moments or percentiles, a feature vector extracted from four moment
ratios and nine percentile ratios are fully exploited by a nonlinear
GBDT model, as to automatically estimate shape parameter. It is proved
that the feature vector is independent of scale parameter. Then, scale
parameter is determined by a shape-parameter-dependent percentile.
Finally, both simulated data and measured data are used to confirm that
the proposed estimator can attain robust and good performance in
complicated and various clutter environments.