基于磁梯度张量SVD 的磁性目标识别方法

针对磁梯度张量单个分量对斜磁化多目标识别能力不足，受载体姿态影响较大的问题，提出基于磁梯度张量奇异值分解(singular value decomposition，SVD)的磁性目标识别方法。通过磁梯度张量矩阵的奇异值分解，提取奇异值的最大值作为磁性目标识别特征量，增强了对斜磁化多目标的识别能力，证明了磁梯度张量奇异值的张量不变量特性，克服了载体姿态变化对磁梯度张量识别的影响。仿真和实测结果表明，该识别方法能有效区分和识别斜磁化的多目标。

Magnetic Target Recognition Method Based on SVD of Magnetic Gradient Tensor

Aiming at the problem that the single component of magnetic gradient tensor has insufficient ability torecognize oblique magnetized multi-targets and is greatly affected by the carrier attitude, a magnetic target recognitionmethod based on singular value decomposition (SVD) of magnetic gradient tensor is proposed. Through the singular valuedecomposition of the magnetic gradient tensor matrix, the maximum of the singular value is extracted as the magnetic targetrecognition feature, which enhances the recognition ability of inclined-magnetized multi-target, proves the tensor invariantproperty of magnetic gradient tensor singular value, and overcomes the influence of carrier attitude change on magneticgradient tensor recognition. The simulation and experimental results show that the method can effectively distinguish andidentify multiple targets with oblique magnetization.