海杂波背景下漂浮小目标检测新算法

A New Algorithm for Floating Small Target Detection Under Sea Clutter Background

针对海杂波背景下漂浮小目标检测问题,提出一种海杂波背景下漂浮小目标检测新算法。利用互补自适应噪声集合经验模式分解-鲁棒性独立主成分分析与Savitzky-Golay滤波算法(EEMDCAN-Robust ICA&SG)联合去噪算法处理海面回波信号,对重构信号用改进蝙蝠算法优化KELM模型做混沌预测。实验结果表明:新算法在不破坏海杂波混沌特性的前提下,极大地抑制了海杂波。在同等条件下,与传统混沌预测算法相比,训练时间短、预测精度高。新算法的检测性能稳定,低信杂比下的检测性能显著优于传统算法,证明新算法能快速实现对漂浮小目标的有效检测。

Aiming at the problem of floating small target detection under sea clutter background a new floating small target detection algorithm EEMDCAN-Robust ICA&SG is proposed.The denoising algorithm combines EEMDCAN Robust ICA with Savitzky-Golay filtering algorithm to process sea echo signal and the improved bat algorithm is used to optimize KELM model for chaotic prediction of reconstructed signal.The experimental results show that:1)The new algorithm greatly suppresses the sea clutter without destroying the chaotic characteristics of the sea clutter;2)Under the same conditions the training time is shorter and the prediction accuracy is higher compared with that of the traditional chaos prediction algorithms;and 3)The detection performance of the new algorithm is stable and the detection performance under low signal to clutter ratio is significantly superior to that of the traditional algorithm.It has been proved that the new algorithm can detect small floating targets quickly and effectively.