Abstract:
Robust and accurate parameter estimation of K-distributed clutter plays an important role in target detection for marine radars. The estimation performance of the existin...Show MoreMetadata
Abstract:
Robust and accurate parameter estimation of K-distributed clutter plays an important role in target detection for marine radars. The estimation performance of the existing estimators is limited by only two moments or percentiles. To break this limit, a self-learning estimator using gate recurrent unit (GRU) network is proposed to estimate shape parameter by a well-designed feature vector composed of ten moment ratios and 13 percentile ratios. It is theoretically proved that the feature vector is independent of scale parameter. Then, the scale parameter is determined by a shape-parameter-dependent percentile. Finally, it is verified by simulated data and measured data that the proposed estimator is suitable for the complicated and various clutter environments.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 20)