针对在基于迭代张量高阶奇异值分解(HOSVD)实现运动目标提取过程中面临的正则化参数的手动选择问题, 采用Morozov's偏差准则的方法实现基于HOSVD的运动目标检测的正则化参数自适应估计。正则化参数根据误差水平进行选择和调整, 在算法迭代中实现收敛。实验证明, 所提方法减少了调试时间, 并且能较准确完整地提取运动目标。

To solve the problem of manual selection of regularized parameters in the process of moving target extraction based on iterative tensor High-Order Singular Value Decomposition (HOSVD), Morozov's deviation criterion is used to achieve the adaptive estimation of normalized parameters in moving target detection based on HOSVD. The regularized parameter is selected and adjusted according to the error level, and rapid convergence is realized in the iterative process of the algorithm. Experiments prove that this method greatly reduces the debugging time, and can accurately and completely extract the moving targets.