Pub Date : 2020-11-22DOI: 10.1080/19479832.2020.1845245
Zengyong Xu, M. Rao
ABSTRACT Vehicle detection is a hotspot in the field of remote sensing image analysis. In particular, campus vehicle detection can assess the density of traffic in an area and provide security for students. The detection accuracy is low for dense vehicle areas or complex background areas. According to the feature of campus vehicle, we propose a multiscale information fusion strategy to construct a novel deep learning framework for campus vehicle detection. This new method based on Single Shot MultiBox Detector (SSD) combines a lightweight deep neural network MobileNet to extract features. A sub-network composed of multiple convolutional layers is connected to detect and locate the object. This method fuses feature information on multiple levels. When removing overlapped object candidate regions, the threshold value is set based on the non-maximum suppression method to eliminate redundant candidate regions. Therefore, the generated negative samples are reduced, which guarantees the stable effect of the proposed model. Experiments show that the proposed vehicle detection method has a faster detection speed. The robustness and accuracy of the proposed model are better than other related vehicle detection methods.
{"title":"Multiscale information fusion-based deep learning framework for campus vehicle detection","authors":"Zengyong Xu, M. Rao","doi":"10.1080/19479832.2020.1845245","DOIUrl":"https://doi.org/10.1080/19479832.2020.1845245","url":null,"abstract":"ABSTRACT Vehicle detection is a hotspot in the field of remote sensing image analysis. In particular, campus vehicle detection can assess the density of traffic in an area and provide security for students. The detection accuracy is low for dense vehicle areas or complex background areas. According to the feature of campus vehicle, we propose a multiscale information fusion strategy to construct a novel deep learning framework for campus vehicle detection. This new method based on Single Shot MultiBox Detector (SSD) combines a lightweight deep neural network MobileNet to extract features. A sub-network composed of multiple convolutional layers is connected to detect and locate the object. This method fuses feature information on multiple levels. When removing overlapped object candidate regions, the threshold value is set based on the non-maximum suppression method to eliminate redundant candidate regions. Therefore, the generated negative samples are reduced, which guarantees the stable effect of the proposed model. Experiments show that the proposed vehicle detection method has a faster detection speed. The robustness and accuracy of the proposed model are better than other related vehicle detection methods.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":"12 1","pages":"83 - 97"},"PeriodicalIF":2.3,"publicationDate":"2020-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19479832.2020.1845245","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44932225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-11DOI: 10.1080/19479832.2020.1845244
H. Hallabia, H. Hamam, A. Ben Hamida
ABSTRACT In this paper, we propose a context-driven injection scheme for pansharpening, in which the injection coefficients are computed over superpixel segments obtained by means of a modified Simple Linear Iterative Clustering (t-SLIC) technique applied on the texture descriptors of the PAN image. By using the t-SLIC algorithm, various homogeneous-connected components can be generated according to their spectral properties. The proposed pansharpening method relies on a multiresolution framework by employing the Generalized Laplacian Pyramid (GLP) tailored to the Modulation Transfer Function (MTF) of the MS sensors for extracting the high frequency details. First, the injection gains are locally computed as regression coefficients between the upsampled MS and low-resolution PAN regions at a reduced scale. Then, they are multiplied by a global weighting factor computed per spectral band and defined as the ratio of variance between expanded MS bands and PAN image. Finally, the spatial details are modulated by means of the estimated global-local injection coefficients at superpixel level to produce the high-resolution MS image. The validation is assessed with two datasets acquired by IKONOS and WorldView-3 satellites. The experimental results show that the proposed method achieves a favourable performance both visually and quantitatively compared to the state of-the-art pansharpening algorithms.
{"title":"A context-driven pansharpening method using superpixel based texture analysis","authors":"H. Hallabia, H. Hamam, A. Ben Hamida","doi":"10.1080/19479832.2020.1845244","DOIUrl":"https://doi.org/10.1080/19479832.2020.1845244","url":null,"abstract":"ABSTRACT In this paper, we propose a context-driven injection scheme for pansharpening, in which the injection coefficients are computed over superpixel segments obtained by means of a modified Simple Linear Iterative Clustering (t-SLIC) technique applied on the texture descriptors of the PAN image. By using the t-SLIC algorithm, various homogeneous-connected components can be generated according to their spectral properties. The proposed pansharpening method relies on a multiresolution framework by employing the Generalized Laplacian Pyramid (GLP) tailored to the Modulation Transfer Function (MTF) of the MS sensors for extracting the high frequency details. First, the injection gains are locally computed as regression coefficients between the upsampled MS and low-resolution PAN regions at a reduced scale. Then, they are multiplied by a global weighting factor computed per spectral band and defined as the ratio of variance between expanded MS bands and PAN image. Finally, the spatial details are modulated by means of the estimated global-local injection coefficients at superpixel level to produce the high-resolution MS image. The validation is assessed with two datasets acquired by IKONOS and WorldView-3 satellites. The experimental results show that the proposed method achieves a favourable performance both visually and quantitatively compared to the state of-the-art pansharpening algorithms.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":"12 1","pages":"1 - 22"},"PeriodicalIF":2.3,"publicationDate":"2020-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19479832.2020.1845244","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43219529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-02DOI: 10.1080/19479832.2020.1838629
Rajesh Gogineni, A. Chaturvedi, Daya Sagar B S
ABSTRACT Pan-sharpening is a remote sensing image fusion technique that generates a high-resolution multispectral (HRMS) image on combining a low resolution multispectral (MS) image and a panchromatic (PAN) image. In this paper, a new optimisation model is proposed for pan-sharpening. The proposed model consists of three terms: (i) a data synthesis fidelity term formulated on inferring the relationship between source MS image and fused image to preserve the spectral information, (ii) a total generalised variation-based prior term to inject the significant spatial details from PAN image to pan-sharpened image, and (iii) a spectral distortion reduction term that exploits the correlation between multispectral image bands. To solve the resultant convex optimisation problem, an efficient and convergence guaranteed operator splitting framework based on the alternating direction method of multipliers (ADMM) algorithm is formulated. Finally, the proposed model is experimentally validated using full-resolution and reduced-resolution data. The pan-sharpened outcomes exhibit the potential of the proposed method in enhancing the spatial and spectral quality.
{"title":"A variational pan-sharpening algorithm to enhance the spectral and spatial details","authors":"Rajesh Gogineni, A. Chaturvedi, Daya Sagar B S","doi":"10.1080/19479832.2020.1838629","DOIUrl":"https://doi.org/10.1080/19479832.2020.1838629","url":null,"abstract":"ABSTRACT Pan-sharpening is a remote sensing image fusion technique that generates a high-resolution multispectral (HRMS) image on combining a low resolution multispectral (MS) image and a panchromatic (PAN) image. In this paper, a new optimisation model is proposed for pan-sharpening. The proposed model consists of three terms: (i) a data synthesis fidelity term formulated on inferring the relationship between source MS image and fused image to preserve the spectral information, (ii) a total generalised variation-based prior term to inject the significant spatial details from PAN image to pan-sharpened image, and (iii) a spectral distortion reduction term that exploits the correlation between multispectral image bands. To solve the resultant convex optimisation problem, an efficient and convergence guaranteed operator splitting framework based on the alternating direction method of multipliers (ADMM) algorithm is formulated. Finally, the proposed model is experimentally validated using full-resolution and reduced-resolution data. The pan-sharpened outcomes exhibit the potential of the proposed method in enhancing the spatial and spectral quality.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":"12 1","pages":"242 - 264"},"PeriodicalIF":2.3,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19479832.2020.1838629","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45118773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-01DOI: 10.1080/19479832.2020.1838630
Cifeng Wang, Ziming Zou, Xiaoyan Hu, Yunlong Li, Xi Bai
ABSTRACT With the development of new methods and the tremendous progress in transducer technology, the observations and researches have become more and more stereoscopic and full-scale. In order to build the multi-source data fusion system propping up the computations, such as process evolution prediction, structure discovery and association analysis, the digital modelling of the natural entity needs to be carried out, which would help build the corresponding digital entity. In this study, the concepts and models in geoscience are introduced, and the issues overlooked in the digital modelling theories are discussed. On this basis, a unified conceptual model and its pseudo-representation (BPRModel) are built. Furthermore, the application of the model is illustrated under the research of specific natural entity, that is to say, the Earth’s magnetosphere.
{"title":"Towards the digital modelling of natural entities and its Pseudo-representation","authors":"Cifeng Wang, Ziming Zou, Xiaoyan Hu, Yunlong Li, Xi Bai","doi":"10.1080/19479832.2020.1838630","DOIUrl":"https://doi.org/10.1080/19479832.2020.1838630","url":null,"abstract":"ABSTRACT With the development of new methods and the tremendous progress in transducer technology, the observations and researches have become more and more stereoscopic and full-scale. In order to build the multi-source data fusion system propping up the computations, such as process evolution prediction, structure discovery and association analysis, the digital modelling of the natural entity needs to be carried out, which would help build the corresponding digital entity. In this study, the concepts and models in geoscience are introduced, and the issues overlooked in the digital modelling theories are discussed. On this basis, a unified conceptual model and its pseudo-representation (BPRModel) are built. Furthermore, the application of the model is illustrated under the research of specific natural entity, that is to say, the Earth’s magnetosphere.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":"13 1","pages":"206 - 217"},"PeriodicalIF":2.3,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19479832.2020.1838630","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47419482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-26DOI: 10.1080/19479832.2020.1838628
S. Beygi, I. Talovina, M. Tadayon, A. B. Pour
ABSTRACT Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery was used to identify argillic, phyllic and propylitic alteration zones and mapping geological structural features for porphyry copper exploration in the Kacho-Mesqal zone, Urumieh- Dokhtar Magmatic Arc, Iran. The image processing techniques such as specialised band ratio, Selective Principal Component Analysis (SPCA), and Spectral Angle Mapping (SAM) image processing methods were implemented to the visible and near-infrared and shortwave infrared bands of ASTER. Results indicate that the argillic alteration zone is broadly distributed in the granodiorite intrusion, andesitic rock, tuff breccia and ignimbrite. Phyllic alteration is mainly mapped associated with sandstone and some parts of andesitic lithology. Propylitic alteration zone is identified in andesite, sandstone, shale and marl, dacite to rhyodacite, andesite-basalt, tuff and andesite lava and granodiorite intrusion. The fracture density map shows that the argillic alteration is mostly abundant in the high-density fracture zone, whereas propylitic and phyllic zones are located in moderate to low-density fracture zones. Consequently, high potential zones for copper mineralisation in the study area are identified within the high to moderate fracture density zones associated with argillic and assemblage of argillic, phyllic and propylitic alteration zones in granodiorite and andesite units.
{"title":"Alteration and structural features mapping in Kacho-Mesqal zone, Central Iran using ASTER remote sensing data for porphyry copper exploration","authors":"S. Beygi, I. Talovina, M. Tadayon, A. B. Pour","doi":"10.1080/19479832.2020.1838628","DOIUrl":"https://doi.org/10.1080/19479832.2020.1838628","url":null,"abstract":"ABSTRACT Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery was used to identify argillic, phyllic and propylitic alteration zones and mapping geological structural features for porphyry copper exploration in the Kacho-Mesqal zone, Urumieh- Dokhtar Magmatic Arc, Iran. The image processing techniques such as specialised band ratio, Selective Principal Component Analysis (SPCA), and Spectral Angle Mapping (SAM) image processing methods were implemented to the visible and near-infrared and shortwave infrared bands of ASTER. Results indicate that the argillic alteration zone is broadly distributed in the granodiorite intrusion, andesitic rock, tuff breccia and ignimbrite. Phyllic alteration is mainly mapped associated with sandstone and some parts of andesitic lithology. Propylitic alteration zone is identified in andesite, sandstone, shale and marl, dacite to rhyodacite, andesite-basalt, tuff and andesite lava and granodiorite intrusion. The fracture density map shows that the argillic alteration is mostly abundant in the high-density fracture zone, whereas propylitic and phyllic zones are located in moderate to low-density fracture zones. Consequently, high potential zones for copper mineralisation in the study area are identified within the high to moderate fracture density zones associated with argillic and assemblage of argillic, phyllic and propylitic alteration zones in granodiorite and andesite units.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":"12 1","pages":"155 - 175"},"PeriodicalIF":2.3,"publicationDate":"2020-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19479832.2020.1838628","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44715893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-13DOI: 10.1080/19479832.2020.1829718
Yipeng Ning, Wengang Sang, Guobiao Yao, Jingxue Bi, Shida Wang
ABSTRACT A GNSS/INS integrated navigation system has been intensively developed and widely applied in multiple areas. It can provide high accuracy position, velocity and attitude for vehicle with appropriate data fusion algorithm. However, the overall performance of a low-cost GNSS/MEMS IMU frequently degrades in shaded environment. The traditional constraints GNSS/MIMU algorithm based on zero-velocity detection can effectively increase positioning performance, but easily be susceptible to false detection. This article aims to improve a ZUPT/DZUPT constraints model to improve the accuracy of navigation solutions during satellites signal blockages for different motion states. Firstly, we present a tightly coupled strategy to integrate GPS/BDS and INS by applying EKF. Then, a compositive static zero-velocity detection scheme is carried out by using the Vondrak low pass filter, GNSS/INS calculated velocity and the original data of INS. Meanwhile, a dynamic ZUPT constraint model is also constructed based on the motion characteristics of vehicle. An vehicle test was performed to validate the new algorithm. The results indicate that proposed method can effectively improve the success rate of zero-velocity detection. When the satellite signal is interrupted for 120 s, the position and velocity accuracy of the vehicle are improved by 74.7%~ 96% and 47%~ 86.2% respectively.
{"title":"GNSS/MIMU tightly coupled integrated with improved multi-state ZUPT/DZUPT constraints for a Land vehicle in GNSS-denied enviroments","authors":"Yipeng Ning, Wengang Sang, Guobiao Yao, Jingxue Bi, Shida Wang","doi":"10.1080/19479832.2020.1829718","DOIUrl":"https://doi.org/10.1080/19479832.2020.1829718","url":null,"abstract":"ABSTRACT A GNSS/INS integrated navigation system has been intensively developed and widely applied in multiple areas. It can provide high accuracy position, velocity and attitude for vehicle with appropriate data fusion algorithm. However, the overall performance of a low-cost GNSS/MEMS IMU frequently degrades in shaded environment. The traditional constraints GNSS/MIMU algorithm based on zero-velocity detection can effectively increase positioning performance, but easily be susceptible to false detection. This article aims to improve a ZUPT/DZUPT constraints model to improve the accuracy of navigation solutions during satellites signal blockages for different motion states. Firstly, we present a tightly coupled strategy to integrate GPS/BDS and INS by applying EKF. Then, a compositive static zero-velocity detection scheme is carried out by using the Vondrak low pass filter, GNSS/INS calculated velocity and the original data of INS. Meanwhile, a dynamic ZUPT constraint model is also constructed based on the motion characteristics of vehicle. An vehicle test was performed to validate the new algorithm. The results indicate that proposed method can effectively improve the success rate of zero-velocity detection. When the satellite signal is interrupted for 120 s, the position and velocity accuracy of the vehicle are improved by 74.7%~ 96% and 47%~ 86.2% respectively.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":"12 1","pages":"226 - 241"},"PeriodicalIF":2.3,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19479832.2020.1829718","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48800079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.1080/19479832.2020.1821100
Ye Tao, Long Zhao, Xiaorong Shen, Zhinpeng Chen, Qieqie Zhang
ABSTRACT WiFi positioning based on fingerprint has received widespread attention and practical applications. However, the fingerprints are susceptible to environmental changes, such as shadowing, multipath, temperature, humidity and obstacles. Due to the instability of received signal strength (RSS), it brings plenty of difficult for WiFi positioning with high accuracy. In this paper, a regularised online sequence extreme learning machine with forgetting parameters (FP-ELM) is adopted to solve the issue accordingly. Forgetting factor and regular factor are adopted in FP-ELM to cope with the time-varying nature of RSS and overcome the issue of irreversible matrix in OS-ELM. The fast running speed of the online sequence extreme learning machine (OS-ELM) is also maintained in FP-ELM. Extensive experiments are carried out in simulation and real experimental areas to explore the characteristics of FP-ELM. Moreover, the positioning results of FP-ELM are compared with the conventional algorithms (OS-ELM and KNN). The simulation and experimental results show when the regular factor is set properly, the positioning result based on FP-ELM algorithm is better than conventional algorithms Figures 1.
{"title":"WiFi indoor positioning based on regularized online sequence extreme learning machine","authors":"Ye Tao, Long Zhao, Xiaorong Shen, Zhinpeng Chen, Qieqie Zhang","doi":"10.1080/19479832.2020.1821100","DOIUrl":"https://doi.org/10.1080/19479832.2020.1821100","url":null,"abstract":"ABSTRACT WiFi positioning based on fingerprint has received widespread attention and practical applications. However, the fingerprints are susceptible to environmental changes, such as shadowing, multipath, temperature, humidity and obstacles. Due to the instability of received signal strength (RSS), it brings plenty of difficult for WiFi positioning with high accuracy. In this paper, a regularised online sequence extreme learning machine with forgetting parameters (FP-ELM) is adopted to solve the issue accordingly. Forgetting factor and regular factor are adopted in FP-ELM to cope with the time-varying nature of RSS and overcome the issue of irreversible matrix in OS-ELM. The fast running speed of the online sequence extreme learning machine (OS-ELM) is also maintained in FP-ELM. Extensive experiments are carried out in simulation and real experimental areas to explore the characteristics of FP-ELM. Moreover, the positioning results of FP-ELM are compared with the conventional algorithms (OS-ELM and KNN). The simulation and experimental results show when the regular factor is set properly, the positioning result based on FP-ELM algorithm is better than conventional algorithms Figures 1.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":"11 1","pages":"268 - 286"},"PeriodicalIF":2.3,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19479832.2020.1821100","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43307138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-22DOI: 10.1080/19479832.2020.1813816
Chuanyang Wang, Yipeng Ning, Xin Li, Haobo Li
ABSTRACT Ultra-wideband (UWB) is well suited for indoor positioning due to its high resolution and good penetration through objects. As one of nonlinear filter algorithms, unscented Kalman filter (UKF) is widely used to estimate the position. However, UKF cannot resist the effect of outliers. The performance of the filter algorithm will be inevitably influenced. In this study, a robust UKF (RUKF) method accompanied by hypothesis test and robust estimation is proposed. Furthermore, the simulation and measurement experiments are performed to verify the effectiveness and feasibility of the proposed RUKF. Simulation experiment results are given to demonstrate that the RUKF can effectively control the influences of the outliers being treated as systematic errors and large variance random errors. When the outliers come from the thick-tailed distribution, the robust estimation does not play a role, and the RUKF does not work well. The measured experiment results show that the outliers will be generated in the non-line-of-sight environment whose impact is abnormally serious. The robust estimation can provide relatively reliable optimised residuals and control the influences of the outliers caused by gross errors. We can believe that the proposed RUKF is effective to resist the effects of outliers and improves the positioning accuracy.
{"title":"A Robust Unscented Kalman Filter applied to Ultra-wideband Positioning","authors":"Chuanyang Wang, Yipeng Ning, Xin Li, Haobo Li","doi":"10.1080/19479832.2020.1813816","DOIUrl":"https://doi.org/10.1080/19479832.2020.1813816","url":null,"abstract":"ABSTRACT Ultra-wideband (UWB) is well suited for indoor positioning due to its high resolution and good penetration through objects. As one of nonlinear filter algorithms, unscented Kalman filter (UKF) is widely used to estimate the position. However, UKF cannot resist the effect of outliers. The performance of the filter algorithm will be inevitably influenced. In this study, a robust UKF (RUKF) method accompanied by hypothesis test and robust estimation is proposed. Furthermore, the simulation and measurement experiments are performed to verify the effectiveness and feasibility of the proposed RUKF. Simulation experiment results are given to demonstrate that the RUKF can effectively control the influences of the outliers being treated as systematic errors and large variance random errors. When the outliers come from the thick-tailed distribution, the robust estimation does not play a role, and the RUKF does not work well. The measured experiment results show that the outliers will be generated in the non-line-of-sight environment whose impact is abnormally serious. The robust estimation can provide relatively reliable optimised residuals and control the influences of the outliers caused by gross errors. We can believe that the proposed RUKF is effective to resist the effects of outliers and improves the positioning accuracy.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":"11 1","pages":"308 - 330"},"PeriodicalIF":2.3,"publicationDate":"2020-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19479832.2020.1813816","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44470314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-15DOI: 10.1080/19479832.2020.1813814
J. Son, S. Oh, D. Hwang
ABSTRACT GPS can be integrated with other radio navigation systems when GPS signals are not available due to navigation warfare. Before deploying navigation signal sources, the coverage analysis can be performed in order to check a required navigation performance is satisfied in 2-dimensional space. Usually, the coverage analysis is performed for the area in which a vehicle is operated. When an air vehicle is operated and the method in 2-dimensional is directly extended, the computational load can be excessively heavy. In this paper, a reference trajectory-based coverage analysis method for multi-radio integrated navigation systems is proposed using CRLB in order to alleviate computational burden in 3-dimensional space. The performance of the proposed method is evaluated for 2 trajectories and 6 arrangements of navigation signal sources. The results show that the proposed method can be used in the coverage analysis in 3-dimensional space.
{"title":"Reference trajectory-based coverage analysis method in three-dimensional space for multi-radio integrated navigation systems","authors":"J. Son, S. Oh, D. Hwang","doi":"10.1080/19479832.2020.1813814","DOIUrl":"https://doi.org/10.1080/19479832.2020.1813814","url":null,"abstract":"ABSTRACT GPS can be integrated with other radio navigation systems when GPS signals are not available due to navigation warfare. Before deploying navigation signal sources, the coverage analysis can be performed in order to check a required navigation performance is satisfied in 2-dimensional space. Usually, the coverage analysis is performed for the area in which a vehicle is operated. When an air vehicle is operated and the method in 2-dimensional is directly extended, the computational load can be excessively heavy. In this paper, a reference trajectory-based coverage analysis method for multi-radio integrated navigation systems is proposed using CRLB in order to alleviate computational burden in 3-dimensional space. The performance of the proposed method is evaluated for 2 trajectories and 6 arrangements of navigation signal sources. The results show that the proposed method can be used in the coverage analysis in 3-dimensional space.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":"11 1","pages":"287 - 307"},"PeriodicalIF":2.3,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19479832.2020.1813814","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46416728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-02DOI: 10.1080/19479832.2020.1813815
Yunrui Zhang, Qiuzhao Zhang, Chun Ma
ABSTRACT Low-cost GPS/INS integration system is the ideal combination of navigation and positioning. However, the sensitivity of low-cost INS is not good enough for the initial alignment in statics base before navigation. Considering this problem, this paper presents an arbitrary misalignment angle error propagation model which does not rely on small misalignment angles assumption and two simplified versions. These models are presented in the ECEF frame approach and are suitable to implement the in-motion alignment with GPS aided. Another three error models based on quaternion, Rodrigues parameters and modified Rodrigues parameters were also proposed. Three experiments were designed to verify the accuracy and computational efficiency of these error models. The experiments’ results showed that the small misalignment angle model was applicable to the small misalignment angle for higher computational efficiency but without improvement of accuracy. And the large misalignment angle is the best error model for the initial alignment of the arbitrary misalignment angle.
{"title":"Performance evaluation of low-cost GPS/INS in-motion alignment model under ECEF frame","authors":"Yunrui Zhang, Qiuzhao Zhang, Chun Ma","doi":"10.1080/19479832.2020.1813815","DOIUrl":"https://doi.org/10.1080/19479832.2020.1813815","url":null,"abstract":"ABSTRACT Low-cost GPS/INS integration system is the ideal combination of navigation and positioning. However, the sensitivity of low-cost INS is not good enough for the initial alignment in statics base before navigation. Considering this problem, this paper presents an arbitrary misalignment angle error propagation model which does not rely on small misalignment angles assumption and two simplified versions. These models are presented in the ECEF frame approach and are suitable to implement the in-motion alignment with GPS aided. Another three error models based on quaternion, Rodrigues parameters and modified Rodrigues parameters were also proposed. Three experiments were designed to verify the accuracy and computational efficiency of these error models. The experiments’ results showed that the small misalignment angle model was applicable to the small misalignment angle for higher computational efficiency but without improvement of accuracy. And the large misalignment angle is the best error model for the initial alignment of the arbitrary misalignment angle.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":"11 1","pages":"331 - 355"},"PeriodicalIF":2.3,"publicationDate":"2020-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19479832.2020.1813815","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47603137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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