A fast linear algorithm for magnetic dipole localization using total magnetic field gradient

Fan, Liming; Kang, Xiyuan; Zheng, Quan; Zhang, Xiaojun; Liu, Xuejun; Chun-lei, Chen; Kang, Chong

doi:10.1109/jsen.2017.2781300

Cited by 35 publications

(10 citation statements)

References 29 publications

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“…To find the optimum parameters, we minimize the negative logarithmic likelihood function of training data as shown in (11), arg min (11) where…”

Section: Fuse Learning Methods 1) Svm Model For the Nlos And Los Cmentioning

confidence: 99%

Signal Fuse Learning Method With Dual Bands WiFi Signal Measurements in Indoor Positioning

Own¹,

Hou²,

Tao³

2019

IEEE Access

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Today, indoor localization technology based on WiFi signals has become more and more popular and applicable. It not only facilitates people's lives but also creates enormous economic value. However, during the propagation of the WiFi signal, it is easily interfered by obstacles, and the signal fluctuation is significant, resulting in low accuracy of positioning. To overcome these problems, we reduce the influence of environmental factors firstly. Then the positioning accuracy is improved by using the SVM model to distinguish the NLOS or LOS environment and employing the capsule networks to derive the users' positions with the WiFi 2.4G and 5G signals. As we all know, the WiFi 2.4G signal has excellent penetrability and is less affected by obstacles, while the WiFi 5G signal has excellent stability and small fluctuations. Therefore, we use the advantages of these two kinds of signals to derive the optimal suggestion by the capsule neural network, which is the learning system with minimum data sets needed. The experimental results show that the positioning effect of the two signals simultaneously is better than the positioning effect of a single signal. We also compare with the traditional indoor positioning methods and use the simulation data to carry out the robustness test, and the positioning accuracy reached 0.99 m in the field environment finally.INDEX TERMS Indoor localization, NLOS and LOS channel propagation condition, WiFi 2.4G and WiFi 5G, SVM, capsule network.

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“…To find the optimum parameters, we minimize the negative logarithmic likelihood function of training data as shown in (11), arg min (11) where…”

Section: Fuse Learning Methods 1) Svm Model For the Nlos And Los Cmentioning

confidence: 99%

Signal Fuse Learning Method With Dual Bands WiFi Signal Measurements in Indoor Positioning

Own¹,

Hou²,

Tao³

2019

IEEE Access

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“…When the target is far from the sensor, jB a j is much less than jB e j. When using the total field magnetometers to detect the target, we can obtain the scalar value of the measurement as [13,16]…”

Section: Minimum Entropy Feature Of Magnetic Anomalymentioning

confidence: 99%

“…Recently, researchers have proposed several methods of magnetic anomaly detection [10][11][12][13][14][15][16][17][18][19]. These methods can be divided into two categories.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Magnetic Anomaly Detection Method with Ensemble Empirical Mode Decomposition and Minimum Entropy Feature

et al. 2020

Self Cite

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Due to the fast attenuation of the magnetic field along with the distance, the magnetic anomaly generated by the remote magnetic target is usually buried in the magnetic noise. In order to improve the performance of magnetic anomaly detection (MAD) with low SNR, we propose an adaptive method of MAD with ensemble empirical mode decomposition (EEMD) and minimum entropy (ME) feature. The magnetic data is decomposed into the multiple intrinsic modal functions (IMFs) with different scales by EEMD. According to a defined criterion, the magnetic noise and magnetic signal are reconstructed based on IMFs, respectively. Entropy feature of reconstructed magnetic signal is extracted based on the probability density function (PDF) of the noise which is updated by the reconstructed magnetic noise. Compared to the traditional minimum entropy method, the entropy feature extracted by the proposed method is more obvious. The magnetic anomaly is detected whenever the entropy feature drops below the threshold. Thus, it is effective for revealing the weak magnetic anomaly by the proposed method. The measured magnetic noise is used to validate the performance of the proposed method. The results show that the detection probability of the proposed method is higher with low input SNR.

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“…Nevertheless, the calculations of these methods are more complicated and requires more time than the gradient-based algorithms. Toward this end, Fan et al (2017) proposed a linear algorithm based on total magnetic field gradient, which can be used to locate the target in real time. Liu (2012) introduced a variety of structures of magnetic gradient tensor systems, and the planar cross-shaped structure is the most used one.…”

Section: Introductionmentioning

confidence: 99%

“…A triangle tensor system and a hexahedron tensor system were used for magnetic localization (Wiegert et al, 2008;Wiegert et al, 2002). Fan et al (2017) performed magnetic localization with multiple sensors on a straight line. Allen et al (2005) proposed a planar crossshaped tensor system with significantly reduced structural error.…”

Section: Introductionmentioning

confidence: 99%

Design and optimization of sensor array for magnetic gradient tensor system

Chen

Zhu

Zhang

et al. 2020

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Purpose The differential magnetic gradient tensor system is usually constructed from the three-axis magnetic sensor array. While the effects of measurement error, sensor performance and baseline distance on localization performance of such systems have been widely reported, the research about the effect of spatial design of sensor array is less presented. This paper aims to provide a spatial design method of sensor array and corresponding optimization strategy to localization based on magnetic tensor gradient to get the optimum design of the sensor array. Based on the results of simulation, magnetic localization systems constructed from the proposed array and the traditional array have been built to carry out a localization experiment. The results of experiment have verified the effectiveness of magnetic localization based on the proposed array. Design/methodology/approach The authors focus on the localization of the magnetic target based on magnetic gradient by using three-axis magnetic sensor array and combine a design method with corresponding optimization strategy to get the optimum design of the sensor array. Findings This paper provides an array design and optimization method for magnetic target localization based on magnetic gradient to improve the localization performance. Originality/value In this paper, the authors focus on the magnetic localization based on magnetic gradient by using three-axis magnetic sensors and study the effect of the spatial design of sensor array on localization performance.

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A fast linear algorithm for magnetic dipole localization using total magnetic field gradient

Cited by 35 publications

References 29 publications

Signal Fuse Learning Method With Dual Bands WiFi Signal Measurements in Indoor Positioning

Signal Fuse Learning Method With Dual Bands WiFi Signal Measurements in Indoor Positioning

Adaptive Magnetic Anomaly Detection Method with Ensemble Empirical Mode Decomposition and Minimum Entropy Feature

Design and optimization of sensor array for magnetic gradient tensor system

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