In the application of persistent scatterer interferometry(PSI),deformation information is extracted from persistent scatterer(PS)points.Thus,the density and position of PS points are critical for PSI.To increase the P...In the application of persistent scatterer interferometry(PSI),deformation information is extracted from persistent scatterer(PS)points.Thus,the density and position of PS points are critical for PSI.To increase the PS density,a time-series InSAR chain termed as"super-resolution persistent scatterer interferometry"(SR-PSI)is proposed.In this study,we investigate certain important properties of SR-PSI.First,we review the main workflow and dataflow of SR-PSI.It is shown that in the implementation of the Capon algorithm,the diagonal loading(DL)approach should be only used when the condition number of the covariance matrix is sufficiently high to reduce the discontinuities between the joint images.We then discuss the density and positioning accuracy of PS when compared with traditional PSI.The theory and experimental results indicate that SR-PSI can increase the PS density in urban areas.However,it is ineffective for the rural areas,which should be an important consideration for the engineering application of SR-PSI.Furthermore,we validate that the positioning accuracy of PS can be improved by SRPSI via simulations.展开更多
With the rapid development of wireless local area network （WLAN） technology, an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online calibration effort to o...With the rapid development of wireless local area network （WLAN） technology, an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online calibration effort to overcome signal time-varying. A novel fingerprint positioning algorithm, known as the adaptive radio map with updated method based on hidden Markov model （HMM）, is proposed. It is shown that by using a collection of user traces that can be cheaply obtained, the proposed algorithm can take advantage of these data to update the labeled calibration data to further improve the position estimation accuracy. This algorithm is a combination of machine learning, information gain theory and fingerprinting. By collecting data and testing the algorithm in a realistic indoor WLAN environment, the experiment results indicate that, compared with the widely used K nearest neighbor algorithm, the proposed algorithm can improve the positioning accuracy while greatly reduce the calibration effort.展开更多
基金supported by the National Natural Science Foundation of China(62101284)the State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of Ministry of Natural Resources+4 种基金China Academy of Surveying and Mapping(2021-03-11)the Natural Science Project of Jiangsu Province(21KJB420003)Nanjing University of Posts and Telecommunications Start-up Fund(NY221033,NY220168)the Foundation of Jiangsu Province Shuangchuang Doctor Grant(JSSCBS20210543)Beijing Key Laboratory of Urban Spatial Information Engineering(20210215)。
文摘In the application of persistent scatterer interferometry(PSI),deformation information is extracted from persistent scatterer(PS)points.Thus,the density and position of PS points are critical for PSI.To increase the PS density,a time-series InSAR chain termed as"super-resolution persistent scatterer interferometry"(SR-PSI)is proposed.In this study,we investigate certain important properties of SR-PSI.First,we review the main workflow and dataflow of SR-PSI.It is shown that in the implementation of the Capon algorithm,the diagonal loading(DL)approach should be only used when the condition number of the covariance matrix is sufficiently high to reduce the discontinuities between the joint images.We then discuss the density and positioning accuracy of PS when compared with traditional PSI.The theory and experimental results indicate that SR-PSI can increase the PS density in urban areas.However,it is ineffective for the rural areas,which should be an important consideration for the engineering application of SR-PSI.Furthermore,we validate that the positioning accuracy of PS can be improved by SRPSI via simulations.
基金supported by the National Natural Science Foundation of China(61571162)the Major National Science and Technology Project(2014ZX03004003-005)
文摘With the rapid development of wireless local area network （WLAN） technology, an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online calibration effort to overcome signal time-varying. A novel fingerprint positioning algorithm, known as the adaptive radio map with updated method based on hidden Markov model （HMM）, is proposed. It is shown that by using a collection of user traces that can be cheaply obtained, the proposed algorithm can take advantage of these data to update the labeled calibration data to further improve the position estimation accuracy. This algorithm is a combination of machine learning, information gain theory and fingerprinting. By collecting data and testing the algorithm in a realistic indoor WLAN environment, the experiment results indicate that, compared with the widely used K nearest neighbor algorithm, the proposed algorithm can improve the positioning accuracy while greatly reduce the calibration effort.
