We use magnetic field data observed by the Swarm mission from 2014 to 2020 to construct,for the first time,a two-dimensional(2 D)lithospheric magnetic anomaly model of Egypt and its surrounding area.Nighttime data dur...We use magnetic field data observed by the Swarm mission from 2014 to 2020 to construct,for the first time,a two-dimensional(2 D)lithospheric magnetic anomaly model of Egypt and its surrounding area.Nighttime data during quiet geomagnetic conditions has been expanded in terms of the Legendre polynomial in harmonic terms N=6-50.The damped least square method has been used to estimate the model coefficients based on the lithospheric magnetic data.Modeled data at two different altitudes(438-448 km and 503-511 km)were compared with the CHAOS model.Results exhibit that the 2 D model is superior to the CHAOS model in the capability of extracting more information about small-scale crustal anomaly field.At low altitudes(438-448 km),the strength of the anomaly field increases,but the noise of the external fields has greatly reduced at high altitudes(503-511 km).Besides,the magnetic anomaly field at low altitudes has illuminated short-scale anomalies that didn’t appear at high altitudes.Both the total and vertical magnetic anomaly vectors showed their ability to reveal tectonic structures compared with Moho depth map and the geological maps.展开更多
This study utilizes the 62208000 Swarm satellite data to establish a high-precision main magnetic field at the height of the satellites in China and its adjacent regions.The CHAOS-6 model is used to remove the crustal...This study utilizes the 62208000 Swarm satellite data to establish a high-precision main magnetic field at the height of the satellites in China and its adjacent regions.The CHAOS-6 model is used to remove the crustal and external fields and obtain 2788 main field grid data.We use the main field grid data to build a three-dimensional(3D)surface spline(3DSS)model of the satellite altitude in China.Other regional models(namely the 3D,two-dimensional(2D)Taylor,and 2D surface spline models)and the CHAOS-6 model are employed to model and analyze the same region.The results show that the 3DSS model can represent a good fi tting for the northward(X)and eastward(Y)components and the total intensity(F).This model demonstrates the most stable results for the 20 points that did not take part in the modeling.Compared with the other three regional models,the root–mean–square error values and the average residuals of the new model are approximately 65%and 69%lower for each component,respectively.This study does not rely on ground station data to derive a more accurate regional main fi eld model.The results further show that less data height difference and high-density data distributions greatly improve the regional model accuracy.The new model has a certain application value to related space geophysics,such as in spatial positioning and navigation,and to the study of regional magnetic anomalies.展开更多
Satellite swarm coordinated flight(SSCF)technology has promising applications,but its complex nature poses significant challenges for control implementation.In response,this paper proposes an easily solvable adaptive ...Satellite swarm coordinated flight(SSCF)technology has promising applications,but its complex nature poses significant challenges for control implementation.In response,this paper proposes an easily solvable adaptive control scheme to achieve high-performance trajectory tracking of the SSCF system subject to actuator efficiency losses and external disturbances.Most existing adaptive controllers based on the certaintyequivalent(CE)principle show unpredictability and nonconvergence in their online parameter estimations.To overcome the above vulnerabilities and the difficulties caused by input failures of SSCF,this paper proposes an adaptive estimator based on scaling immersion and invariance(I&I),which reduces the computational complexity while improving the performance of the parameter estimator.Besides,a barrier Lyapunov function(BLF)is applied to satisfy both the boundedness of the system states and the singularity avoidance of the computation.It is proved that the estimator error becomes sufficiently small to converge to a specified attractive invariant manifold and the closed-loop SSCF system can obtain asymptotic stability under full-state constraints.Finally,numerical simulations are performed for comparison and analysis to verify the effectiveness and superiority of the proposed method.展开更多
To improve the global search ability of particle swarm optimization (PSO), a multi-population PSO based on pyramid model (PPSO) is presented. Then, it is applied to solve the layout optimization problems against t...To improve the global search ability of particle swarm optimization (PSO), a multi-population PSO based on pyramid model (PPSO) is presented. Then, it is applied to solve the layout optimization problems against the background of an international commercial communication satellite (INTELSAT-Ⅲ) module. Three improvements are developed, including multi-population search based on pyramid model, adaptive collision avoidance among particles, and mutation of degraded particles. In the numerical examples of the layout design of this simplified satellite module, the performance of PPSO is compared to global version PSO and local version PSO (ring and Neumann PSO). The results show that PPSO has higher computational accuracy, efficiency and success ratio.展开更多
The Swarm satellite mission was launched on 22 November 2013,it is the first European Space Agency’s constellation of three satellites,dedicated to monitoring geomagnetic field changes.The measurements delivered by t...The Swarm satellite mission was launched on 22 November 2013,it is the first European Space Agency’s constellation of three satellites,dedicated to monitoring geomagnetic field changes.The measurements delivered by the three satellites are very valuable for a range of applications,including the earthquake prediction study.However,for more than 5 years,relatively little advancement has been achieved in establishing a systematic approach for detecting anomalies from the satellite measurements for predicting earthquakes.This paper presents the challenges of developing a pragmatic framework for automatic anomaly detection and highlights innovative features of functional components developed.Through a case study we demonstrate a functionality pipeline of the system in detecting anomalies,and present our solutions to coping with data sparsity and parameter tuning as well as insights into the differences between discovering seismic anomalies from periodic and non-periodic data observed by the Swarm satellites.展开更多
文摘We use magnetic field data observed by the Swarm mission from 2014 to 2020 to construct,for the first time,a two-dimensional(2 D)lithospheric magnetic anomaly model of Egypt and its surrounding area.Nighttime data during quiet geomagnetic conditions has been expanded in terms of the Legendre polynomial in harmonic terms N=6-50.The damped least square method has been used to estimate the model coefficients based on the lithospheric magnetic data.Modeled data at two different altitudes(438-448 km and 503-511 km)were compared with the CHAOS model.Results exhibit that the 2 D model is superior to the CHAOS model in the capability of extracting more information about small-scale crustal anomaly field.At low altitudes(438-448 km),the strength of the anomaly field increases,but the noise of the external fields has greatly reduced at high altitudes(503-511 km).Besides,the magnetic anomaly field at low altitudes has illuminated short-scale anomalies that didn’t appear at high altitudes.Both the total and vertical magnetic anomaly vectors showed their ability to reveal tectonic structures compared with Moho depth map and the geological maps.
基金supported by the National Natural Science Foundation of China (Nos. 42030203, 41974073, and 41404053)
文摘This study utilizes the 62208000 Swarm satellite data to establish a high-precision main magnetic field at the height of the satellites in China and its adjacent regions.The CHAOS-6 model is used to remove the crustal and external fields and obtain 2788 main field grid data.We use the main field grid data to build a three-dimensional(3D)surface spline(3DSS)model of the satellite altitude in China.Other regional models(namely the 3D,two-dimensional(2D)Taylor,and 2D surface spline models)and the CHAOS-6 model are employed to model and analyze the same region.The results show that the 3DSS model can represent a good fi tting for the northward(X)and eastward(Y)components and the total intensity(F).This model demonstrates the most stable results for the 20 points that did not take part in the modeling.Compared with the other three regional models,the root–mean–square error values and the average residuals of the new model are approximately 65%and 69%lower for each component,respectively.This study does not rely on ground station data to derive a more accurate regional main fi eld model.The results further show that less data height difference and high-density data distributions greatly improve the regional model accuracy.The new model has a certain application value to related space geophysics,such as in spatial positioning and navigation,and to the study of regional magnetic anomalies.
基金supported by the Natural Science Foundation of Shaanxi Province(2020JQ-132)China Postdoctoral Science Foundation(2020M683571)+1 种基金National Natural Science Foundation of China(62103336,11972026,U2013206)Funds for the Central Universities(3102019HTQD007)。
文摘Satellite swarm coordinated flight(SSCF)technology has promising applications,but its complex nature poses significant challenges for control implementation.In response,this paper proposes an easily solvable adaptive control scheme to achieve high-performance trajectory tracking of the SSCF system subject to actuator efficiency losses and external disturbances.Most existing adaptive controllers based on the certaintyequivalent(CE)principle show unpredictability and nonconvergence in their online parameter estimations.To overcome the above vulnerabilities and the difficulties caused by input failures of SSCF,this paper proposes an adaptive estimator based on scaling immersion and invariance(I&I),which reduces the computational complexity while improving the performance of the parameter estimator.Besides,a barrier Lyapunov function(BLF)is applied to satisfy both the boundedness of the system states and the singularity avoidance of the computation.It is proved that the estimator error becomes sufficiently small to converge to a specified attractive invariant manifold and the closed-loop SSCF system can obtain asymptotic stability under full-state constraints.Finally,numerical simulations are performed for comparison and analysis to verify the effectiveness and superiority of the proposed method.
基金This project is supported by National Natural Science Foundation of China (No.50275019, No.50335040, No.50575031).
文摘To improve the global search ability of particle swarm optimization (PSO), a multi-population PSO based on pyramid model (PPSO) is presented. Then, it is applied to solve the layout optimization problems against the background of an international commercial communication satellite (INTELSAT-Ⅲ) module. Three improvements are developed, including multi-population search based on pyramid model, adaptive collision avoidance among particles, and mutation of degraded particles. In the numerical examples of the layout design of this simplified satellite module, the performance of PPSO is compared to global version PSO and local version PSO (ring and Neumann PSO). The results show that PPSO has higher computational accuracy, efficiency and success ratio.
基金National Natural Science Foundation of China(No.41374077)。
文摘The Swarm satellite mission was launched on 22 November 2013,it is the first European Space Agency’s constellation of three satellites,dedicated to monitoring geomagnetic field changes.The measurements delivered by the three satellites are very valuable for a range of applications,including the earthquake prediction study.However,for more than 5 years,relatively little advancement has been achieved in establishing a systematic approach for detecting anomalies from the satellite measurements for predicting earthquakes.This paper presents the challenges of developing a pragmatic framework for automatic anomaly detection and highlights innovative features of functional components developed.Through a case study we demonstrate a functionality pipeline of the system in detecting anomalies,and present our solutions to coping with data sparsity and parameter tuning as well as insights into the differences between discovering seismic anomalies from periodic and non-periodic data observed by the Swarm satellites.