基于库普曼-卡尔曼滤波的海面目标检测方法OA

Marine target detection is severely affected by sea clutter,which poses the nonlinear,non-stationary,and non-Gaussian characteristics.Existing detectors,based on statistical models,feature extraction,and deep learning,en-counter challenges in dynamic sea clutter environments.Thus,a sea clutter prediction model based on the Koopman-Kalman filter(KKF)is proposed herein,and a marine target detector is constructed under the prediction model.First,a Hankel-matrix form of the spatial and temporal sea clutter is constructed,thereby transforming the sea clutter matrix into a higher-dimensional space.Subsequently,dynamic mode decomposition(DMD)is employed to perform linear modeling on the augmented sea clutter matrix,uncovering the inherent spatio-temporal nonlinear dynamic patterns of the sea clutter.A linear evolution model for sea clutter is then established using Koopman modes and Koopman eigenval-ues.This model is then converted into a state-space equation form,which can be integrated with the Kalman filter to achieve short-term prediction of spatial and temporal sea clutter sequences.Finally,the absolute prediction error is uti-lized as the detection statistic.Additionally,a detection threshold is set to determine whether a target exists for a certain false alarm rate,thereby constructing the KKF detector.The proposed detector can transform marine target detection into a problem of predicting the spatio-temporal evolution of sea clutter.The detection process requires neither iterative training nor prior knowledge,making it particularly advantageous for short-duration target detection.Experimental re-sults on measured data show that the proposed detector outperforms existing comparative approaches for a short dura-tion,in which the target presence lasts only 10 ms,offering a promising approach for marine target detection.