Wang Lijie, Zhang Shuang, Chen Shudong, Luo Chaopeng
College of Electronic Science and Engineering, Jilin University, Changchun 130012, China.
Science and Technology on Near-Surface Detection Laboratory, Wuxi 214035, China.
Sensors (Basel). 2022 Feb 20;22(4):1648. doi: 10.3390/s22041648.
A fast inversion algorithm combined with the transient electromagnetic (TEM) detection system has important significance for improving the detection efficiency of unexploded ordnance. The traditional algorithms, such as differential evolution or Gauss-Newton algorithms, usually require tens to thousands of iterations to locate the underground target. A new algorithm with a magnetic gradient tensor and singular value decomposition (SVD) to estimate the target position and characterization quickly and accurately is proposed in this paper. Two modes of magnetic gradient tensor are constructed to accurately locate shallow and deep targets, respectively. The SVD algorithm is applied to the responses to estimate the electromagnetic characteristics of the target quickly and accurately. To verify the performance of the proposed algorithm, a towed TEM sensor is designed, which is constructed with three transmitting coils and nine three-component receiving coils arranged in a 3 × 3 array. Field experiments in survey and cued modes were taken to verify the performance of the proposed algorithm and the towed system. Results show that the magnetic gradient tensor algorithm proposed in this paper can accurately locate a single target within 2.0 m depth, and the error of depth is no more than 8 cm. Even for overlapping response of multi targets, the error of depth is no more than 12 cm. The underground target can be accurately characterized by the SVD algorithm. For targets with depths over 2.0 m, the signal-to-noise ratio of characteristic response estimated by SVD is higher than that of the traditional method. The proposed method needs approximately 40 ms, only 1% of the traditional one, considerably improving detection efficiency and laying a theoretical and experimental foundation for real-time data processing.
一种结合瞬变电磁(TEM)探测系统的快速反演算法对于提高未爆弹药的探测效率具有重要意义。传统算法,如差分进化算法或高斯 - 牛顿算法,通常需要数十到数千次迭代才能定位地下目标。本文提出了一种利用磁梯度张量和奇异值分解(SVD)快速准确估计目标位置和特征的新算法。构建了两种磁梯度张量模式,分别用于准确地定位浅部和深部目标。将SVD算法应用于响应,以快速准确地估计目标的电磁特性。为了验证所提算法的性能,设计了一种拖曳式TEM传感器,它由三个发射线圈和九个三分量接收线圈按3×3阵列排列构成。进行了测量模式和提示模式下的野外实验,以验证所提算法和拖曳系统的性能。结果表明,本文提出的磁梯度张量算法能够在2.0 m深度范围内准确地定位单个目标,深度误差不超过8 cm。即使对于多个目标的重叠响应,深度误差也不超过12 cm。地下目标可以通过SVD算法准确地表征。对于深度超过2.0 m的目标,SVD估计的特征响应的信噪比高于传统方法。所提方法大约需要40 ms,仅为传统方法的1%,大大提高了探测效率,为实时数据处理奠定了理论和实验基础。