An Improved ICCP Matching Algorithm for use in an Interference Environment during Geomagnetic Navigation

Xiao, Jing; Duan, Xinbin; Qi, Xiaohui; Liu, Yifei

doi:10.1017/s0373463319000535

Cited by 9 publications

(3 citation statements)

References 26 publications

(31 reference statements)

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“…The Earth's magnetic field is the inherent physical field of the Earth [1,2]. It has crucial applications in geomagnetic navigation [3,4], attitude calculation [5][6][7], magnetic anomaly signal detection [8,9], and so on. The error of geomagnetic data will not accumulate over time.…”

Section: Introductionmentioning

confidence: 99%

Improved ellipsoid fitting aided geomagnetic sensor calibration algorithm

Jiang,

Tan

2024

Meas. Sci. Technol.

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Aiming at the problems that the traditional single ellipsoid fitting fails to eliminate the errors of the geomagnetic sensor and does not consider the geomagnetic anomaly information, etc., this paper proposes a Robust Least Squares combined with the Levenberg-Marquardt (LM-RLS) algorithm to fit an accurate ellipsoid to achieve accurate data calibration. According to the geomagnetic sensor error model, the total amount of geomagnetism is used as the constraint, the fitted ellipsoid coefficients of Robust Least Squares (RLS) are used as the initial values, and the LM algorithm is used to calculate the magnetic field dispersion points to the nearest point of the fitted ellipsoid surface. Then, the initial calibration value is solved by the criterion of the minimum weighted sum of squares of the closest point distance. Finally, the accurate ellipsoid is obtained to realize data calibration. The results of the dynamic experiment show that after the ellipsoid is precisely fitted by the LM-RLS algorithm, the sum of the absolute values of the distance from the magnetic field discrete data to the ellipsoid surface is reduced by 48.33% (RLS) and 43.10% (RWTLS). The relative errors of the magnetic field in the X, Y, and Z axes are reduced from 0.48%, 0.78%, 1.48% (RLS) and 0.30%, 0.58%, 1.14% (RWTLS) to 0.10%, 0.04%, 0.16 %, respectively. The accuracy of geomagnetic calibration is improved by 68.06% (RLS) and 61.02% (RWTLS) in the dynamic experiment. The algorithm improves the accuracy of geomagnetic measurement, which provides a new idea for the calibration of three-axis geomagnetic sensors.

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Section: Introductionmentioning

confidence: 99%

Improved ellipsoid fitting aided geomagnetic sensor calibration algorithm

Jiang,

Tan

2024

Meas. Sci. Technol.

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“…Gravity matching algorithms [9,10] are the critical part of gravity aided INS navigation, and mainly consist of TERCOM [11], ICCP (iterative closest contour point), SITAN (sandia inertial terrain aided navigation) and their variants. In comparison, TERCOM has been extensively studied by scholars, resulting from its insensitivity to initial error, simple calculation mechanism, high positioning accuracy and strong robustness [12,13]. However, its mismatching occurrence in the matching processing will seriously affect the calibration effect of INS system parameters [14,15], and even lead to the failure of underwater sailing mission.…”

Section: Introductionmentioning

confidence: 99%

Improving Matching Efficiency and Out-of-domain Reliability of Underwater Gravity Matching Navigation Based on a Novel Soft-margin Local Semicircular-domain Re-searching Model

Zhao

Zheng

et al. 2022

Remote Sensing

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This paper mainly studies the improvement of the efficiency and out-of-domain reliability of gravity matching navigation for underwater vehicles. To overcome the traversal low-efficiency problem of the traditional terrain contour matching (TERCOM) algorithm and improve the positioning reliability of its out-of-domain mismatches, a novel soft-margin local semicircular-domain re-searching model (SLSR) is proposed by integrating the soft-margin circular grid matching (SCGM) mechanism and the local semicircular grid re-matching (LSGR) mechanism. SCGM uses three times the inertial navigation cumulative error and adds the unit grid resolution as the soft margin boundary to generate the soft-margin circular domain, which contributes to reducing in-domain matching grid points and enhancing the matching efficiency of algorithms. Then the optimal matching position in this soft-margin circular domain is obtained by using the optimization principle of matching indices. LSGR is triggered when the optimal matching position of SCGM is located near the soft-margin circular-domain boundary. It employs this optimal matching point as the center and the unit inertial navigation error as the radius to recreate the semicircular local re-searching matched grid domain (termed as semicircular domain). Moreover, the optimal matching point in this semicircular domain is obtained by the matching index optimization principle, and then it is compared and updated to obtain the final best matching position of SLSR. The simulation results show that SCGM and LSGR of the proposed SLSR method can effectively improve the matching efficiency and out-of-domain matching reliability of underwater navigation, respectively. Under the same testing conditions for the tracking starting points from three gravity regions, the number of out-of-domain mismatches of SLSR, compared with TERCOM, are lower up to 92.68%, 90.24% and 98.62%, while the average matching accuracies are relatively improved by 88.37%, 85.48% and 83.66%, which verifies the validity and feasibility of the proposed SLSR model on improving the efficiency and out-of-domain reliability of underwater gravity matching navigation.

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“…Geomagnetic navigation technology can provide a reliable navigation method by measuring geomagnetic fields for mobile robots, such as underwater vehicle navigation (AUV) (Kinsey et al, 2006 ) and unmanned aircraft (UA) (Yuan et al, 2011 ; Kebria et al, 2020 ). Conventional geomagnetic navigation methods mainly focus on geomagnetic matching algorithms, which mainly include the Mean Absolute Difference algorithm (MAD) (Caifa et al, 2011 ), Mean Square Difference algorithm (MSD) (Rong, 2016 ), and Iterative Closest Contour Point algorithm (ICCP) (Lin et al, 2007 ; Xiao et al, 2019 ; Luo et al, 2021a ). However, the conventional matching methods mainly depend on a priori geomagnetic map.…”

Section: Introductionmentioning

confidence: 99%

A Correlation Analysis of Geomagnetic Field Characteristics in Geomagnetic Perceiving Navigation

Jiang

et al. 2021

Front. Neurorobot.

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It is well-known that geomagnetic fields have multiple components or parameters, and that these geomagnetic parameters are related to each other. In this paper, a parameter selection method is proposed, and this paper mainly discusses the correlation of geomagnetic field parameters for geomagnetic navigation technology. For the correlation analysis between geomagnetic parameters, the similarity calculation of the correlation coefficient is firstly introduced for geomagnetic navigation technology, and the grouped results are obtained by data analysis. At the same time, the search algorithm (Hex-path algorithm) is used to verify the correlation analysis results. The results show the same convergent state for the approximate correlation coefficient. In other words, the simulation results are in agreement with the similarity calculation results.

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An Improved ICCP Matching Algorithm for use in an Interference Environment during Geomagnetic Navigation

Cited by 9 publications

References 26 publications

Improved ellipsoid fitting aided geomagnetic sensor calibration algorithm

Improved ellipsoid fitting aided geomagnetic sensor calibration algorithm

Improving Matching Efficiency and Out-of-domain Reliability of Underwater Gravity Matching Navigation Based on a Novel Soft-margin Local Semicircular-domain Re-searching Model

A Correlation Analysis of Geomagnetic Field Characteristics in Geomagnetic Perceiving Navigation

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