In a context of buried objects detection using electromagnetic induction (EMI), dipole inversion refers to the estimation of object’s location and magnetic polarizability tensor from EMI and sensor’s positional data. In case of planar coil sensors, dipole inversion may become a surprisingly difficult and potentially ill-posed problem due to non-uniform distribution of directional sensitivities, nonlinear nature of target localization, as well as strong correlations between tensor elements and target’s depth. In this paper, we evaluate inversion performances of two categories of planar coil sensors; single-receiver sensors used in conventional metal detection (MD), and multi-receiver sensors aimed at metal characterization (MC). We use three different inversion methods; nonlinear least squares (NLS), HAP method featuring novel auxiliary source model for improved object localization, and differential evolution (DE). Comparative study is performed using synthetic EMI and sensor’s positional data under realistic scenarios involving different targets, depths and signal-to-noise ratios (SNRs). Our results suggest that relatively simple planar MC sensors clearly outperform conventional MD sensors, especially at greater depths. On average, DE method notably improves the invertibility of MD sensors, while a denser scan pattern may help to tackle lower SNR at greater depths.

中文翻译：

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用于埋地物体倒置检测的平面线圈 EMI 传感器的比较研究

在使用电磁感应 (EMI) 检测掩埋物体的背景下，偶极反演是指根据 EMI 和传感器的位置数据估计物体的位置和磁极化率张量。在平面线圈传感器的情况下，由于方向灵敏度的非均匀分布、目标定位的非线性特性以及张量元素与目标深度之间的强相关性，偶极子反转可能成为一个令人惊讶的困难和潜在的不适定问题。在本文中，我们评估了两类平面线圈传感器的反演性能；用于传统金属检测 (MD) 的单接收器传感器，以及用于金属表征 (MC) 的多接收器传感器。我们使用三种不同的反演方法；非线性最小二乘法 (NLS)，HAP 方法具有新颖的辅助源模型，用于改进对象定位和差分进化 (DE)。在涉及不同目标、深度和信噪比 (SNR) 的真实场景下，使用合成 EMI 和传感器的位置数据进行比较研究。我们的结果表明，相对简单的平面 MC 传感器明显优于传统的 MD 传感器，尤其是在更大的深度。平均而言，DE 方法显着提高了 MD 传感器的可逆性，而更密集的扫描模式可能有助于解决更深的低 SNR。我们的结果表明，相对简单的平面 MC 传感器明显优于传统的 MD 传感器，尤其是在更大的深度。平均而言，DE 方法显着提高了 MD 传感器的可逆性，而更密集的扫描模式可能有助于解决更深的低 SNR。我们的结果表明，相对简单的平面 MC 传感器明显优于传统的 MD 传感器，尤其是在更大的深度。平均而言，DE 方法显着提高了 MD 传感器的可逆性，而更密集的扫描模式可能有助于解决更深的低 SNR。