Peng Wang

Underground near-field electromagnetic positioning system is based on Near-Field Electromagnetic Ranging (NFER) technology. Obtaining the thickness of underground multi-layer media during the positioning process introduces an ill-posed problem in the traditional electromagnetic model due to matrix inversion. Therefore, we propose a positioning method based on Back Propagation (BP) neural network, which avoids the matrix inversion and contains the ranging model and positioning model. The positioning model is based on the ranging model and simultaneously predicts the ranging value and the thickness of each layer. The positioning is realized by utilizing the thickness value and range value combined with 2D-DOA. This positioning method simplifies the positioning process compared with the trilateration algorithm. The results of the validation sets show that the positioning accuracy can reach 0.6m at a depth of 40 m. Garson algorithm performs sensitivity analysis on BP neural network, and it can be concluded that the signal angle contributes the most to the prediction of the results. Ultimately, the results reveal that the BP neural network-based positioning method performs well in non-homogeneous media (NH) environments across various depth spaces and signal-to-noise ratios (SNRs).

Underground metal target detection mainly aims to estimate the attributes of underground metal targets and classify metal targets based on observed data. Electromagnetic induction(EMI) methods have been widely used in the field because of the high precision and strong penetration to metal targets. The development and improvement of electromagnetic underground metal target detection methods can be implemented by a framework that is experimental supporting, modular, and extensible. In this paper, we organize the components of electromagnetic underground metal target detection in a comprehensive, modular and extensible framework. Furthermore, we created an open-source platform of the framework in Python called MicEMD(Modeling, Inversion, and Classification in ElectroMagnetic Detection, https://github.com/UndergroundDetection/MICEMD. The graphical user interface(GUI) and the library with a Python application programming interface(API) are contained in MicEMD. Included in MicEMD are staggered frequency-domain and time-domain electromagnetic forward modeling, least-squares inversion, and data-based classification methods at present. MicEMD's capabilities are presented by two synthetic case studies. The first example shows the application of frequency-domain inversion. The second example shows the application of time-domain classification. It is anticipated that MicEMD offers a flexible tool in electromagnetic underground metal target detection.

Underground target detection plays an important role in maintaining underground space security and relies on efficient real-time detection methods. Electromagnetic detection methods including ground penetrating radar and electromagnetic induction have been implemented in detecting underground targets and have been shown to have bright prospects for the future. This paper presents a comprehensive review of current electromagnetic detection methods and recent advances in underground target detection. There are two main problems in electromagnetic underground target detection, namely inversion and classification. We summarize solutions to these problems based on electromagnetic methods as well as deep learning. In addition, this paper proposes a technical framework based on data acquisition and data processing to describe the application of electromagnetic methods. Finally, this paper concludes that although various methods have been proposed, there still exist several future research hotspots in the fields of soil water content, tree root system research, and underground unexploded ordnance. This paper can be a reference for researchers in underground target detection and related fields. Therefore, the research of underground target electromagnetic detection method is expected to increase significantly.

Underground metal target detection technology has been widely applied in industrial production, resource exploration, and engineering construction, etc. However, due to the influence of non-negligible noise and high dimensionality in collected data, achieving efficient and accurate underground target classification remains a grand challenge for further applications of underground metal target detection on portable devices with limited computing capability and energy supply. This study aimed to seek out robust and efficient data-based strategies to classify the underground metal targets of different shapes and materials based on electromagnetic induction detection. We investigated thirty-three classification strategies based on eleven dimensionality reduction methods, namely, the least absolute shrinkage and selection operator (LASSO), genetic algorithm-support vector machine (GA-SVM), Pearson correlation coefficient (PCC), mutual information (MI), maximal relevance minimal redundancy Pearson correlation (mRMRP), maximal relevance minimal redundancy mutual information (mRMRMI), statistical features (SF), principal component analysis (PCA), kernel principal component analysis (KPCA), locally linear embedding (LLE), and stacked denoising autoencoder (SDAE), and three machine learning models, namely, artificial neural network (ANN), linear support vector machine (L-SVM), and Gaussian Naïve Bayes (GNB). Several parameters, including classification accuracy, the number of features after dimensionality, the feature type importance, and the time consumption were considered to evaluate the data-based classification strategies. Among the classification strategies investigated and considering the above evaluation parameters, the artificial neural network (ANN) classifier assisted with the kernel principal component analysis (KPCA) feature extraction method yielded the best performance in the material-based classification (accuracy:0.99) and the shape-based classification (accuracy:0.99). The locally linear embedding (LLE) improved the robustness of machine learning classifiers and efficiency of the artificial neural network in the material-based classification (improvement of average accuracy:0.17, reduction of classification time cost:14%) and shape-based classification (improvement of average accuracy:0.16, reduction of classification time cost:22%). Our comparative investigation provides a robust and efficient data-based strategy for underground metal target classification, which is significant for applications of underground metal target detection on portable devices with limited computing capability and energy supply. The cross-combination strategy of dimensionality reduction methods and machine learning models provides a way to find the optimal machine learning model for underground target detection.

Peer-to-peer (P2P) direction estimation based on rotary (TOA) ranging is an effective method for infrastructure-free navigation in indoor environments, which is crucial to first responders, especially firefighters. With the sequential measured distances and corresponding azimuth angles, P2P direction estimation can be defined as a sequence matching problem. This article presents P2PNavi, a system-level algorithmic solution, to overcome the impact of unavoidable indoor ranging error on the matching accuracy. By means of novel preprocessing and matching algorithms, P2PNavi can effectively improve the signal-to-noise ratio (SNR) of the measured sequence and reduce the direction estimation error. The solution is verified by experiments with commercially available TOA devices in a typical office building. P2PNavi significantly outperforms nonsystem-level methods in terms of the root mean square error of direction estimation.

Underground metal target detection refers to estimating the properties of underground metal targets based on a set of observed data. Electromagnetic induction (EMI) method and the least-squares inversion provide the data acquisition method and the parameter estimation method for the detection, respectively. As an important part of least-squares inversion, optimization algorithms directly affect the efficiency of the least-squares inversion. To improve the efficiency of underground metal target detection, it is necessary to compare the performance of different optimization algorithms. In this paper, we analyzed the characteristic of the complex EMI forward model using sensitivity analysis and inverse sensitivity analysis at first. Then, the EMI forward model and least squares were used to build the objective function. The estimation error, run time and number of iterations of six numerical optimization algorithms were compared under different signal-to-noise ratios (SNRs) and under different data acquisition spacing. The algorithms include gradient descent, steepest descent, Newton’s method, Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm, conjugate gradient method and Levenberg–Marquardt (LM) algorithm. A simulation platform was established to generate observed data and compare the optimization algorithms. The results of algorithm comparison showed that the BFGS, conjugate gradient and LM algorithm can efficiently and accurately estimate the properties of underground metal targets and the LM algorithm has the shortest run time and the least number of iterations. Finally, we carefully analyzed the time consumption of each optimization algorithm. The results show that the calculation of the gradient and step length selection greatly affect the efficiency of the optimization algorithms.

As an underground metal detection technology, the electromagnetic induction (EMI) method is widely used in many cases. Therefore, the EMI detection algorithms with excellent performance are worth studying. One of the EMI detection methods in the underground metal detection is the filter method, which first obtains the secondary magnetic field data and then uses the Kalman filter (KF) and the extended Kalman filter (EKF) to estimate the parameters of metal targets. However, the traditional KF methods used in the underground metal detection have an unsatisfactory performance of the convergence as the algorithms are given a random or a fixed initial value. Here, an initial state estimation algorithm for the underground metal detection is proposed. The initial state of the target’s horizontal position is estimated by the first order central moments of the secondary field strength map. In addition, the initial state of the target’s depth is estimated by the full width at half maximum (FWHM) method. In addition, the initial state of the magnetic polarizability tensor is estimated by the least squares method. Then, these initial states are used as the initial values for KF and EKF. Finally, the position, posture and polarizability of the target are recursively calculated. A simulation platform for the underground metal detection is built in this paper. The simulation results show that the initial value estimation method proposed for the filtering algorithm has an excellent performance in the underground metal detection.

Low frequency communication, taking advantage of the features of low frequency electromagnetic signals in near field, is widely used in through-the-earth (TTE) wireless applications. However, the low frequency non-Gaussian noise severely limits the communication performance. In this paper, an adaptive noise cancellation algorithm based on time delay estimation (ANC-TDE) and maximum correntropy criterion (MCC) is proposed. The explicit time delay estimation algorithm based on MCC (MCC-ETDE) is used to estimate the time-varying delay and the noise correlationship between the primary input and reference inputs. With a reference noise selected and time delay compensated, the non-Gaussian noise is canceled by adaptive filter based on MCC. The proposed algorithm is implemented on field programmable gate array (FPGA) and the performance is evaluated by simulation and experiment. As shown in the results, the ANC-TDE algorithm can reduce the complexity of ANC filter and adaptively compensate the varied time delay between the primary input and reference input. With the time delay compensated, the ANC-TDE algorithm has better performance in non-Gaussian noise environment and is more suitable for real-time systems.

In this paper, we describe a rotating anchor-based positioning method using time of arrival (TOA) measurements to obtain the relative positional relationship between two locators, which can help a rescuer to efficiently determine the direction to a trapped person or help a lost person to find the direction to an exit in first responder applications. Since the TOA ranging error of this special scenario cannot be modeled by the distributions proposed for traditional fixed anchor-based indoor geolocation, a new characteristic model based on the α-stable distribution is presented for rotating anchor-based relative positioning. The model is verified by the Kolmogorov-Smirnov hypothesis and Akaike weights and compared with the traditional distributions. Moreover, the parameters of the model are used to improve the direction identification accuracy, further reflecting the value of the model.

Summary: The near‐field electromagnetic ranging technology exploits the near‐field behavior of radio signals to measure distance in indoor real‐time location system. In this paper, we propose a new adaptive time‐delay estimation algorithm based on maximum correntropy criterion (MCC) and received signal strength indication (RSSI), abbreviated as RSSIMCC. The proposed RSSIMCC algorithm estimates the time delay between the electric and magnetic components of low‐frequency signals in the near field instead of detecting the phase difference. An inaccurate time‐delay estimation value obtained according to the RSSI is used as the initial time delay of adaptive time‐delay estimator, which is not far from the true time delay and can improve the convergence speed. Through the analysis of simulation and experiment results, we conclude that the proposed RSSIMCC algorithm can estimate the time delay with small steady‐state error in near‐field electromagnetic ranging application. Then the time delay estimated by RSSIMCC algorithm can be exploited based on the near‐field behavior of radio signals for ranging in an indoor environment. Copyright © 2016 John Wiley & Sons, Ltd.