An efficient cycle slip detection method is proposed for high precision positioning and navigation results with global positioning system （GPS）,which is based on the assumption of a high sampling interval, measureme...An efficient cycle slip detection method is proposed for high precision positioning and navigation results with global positioning system （GPS）,which is based on the assumption of a high sampling interval, measurement errors are so small that they can be ignored in the temporal single difference observables. And ambiguities are ordinarily equal to zero,but could be the number of cycles that have ＂slipped＂ if loss-of-lock has occurred.Therefore,cycle slips are estimated as parameters of time-relative positioning observation equations.Because the temporal single difference observables are taken at different epochs and different stations with a single GPS receiver,if time-relative positioning observation equations are linearized as that of conventional relative positioning,the design matrix will be rank defective.To obtain a stable linearization scheme,time-relative positioning observation equations are further analyzed,and the concept of virtual measurement is applied.A sample of data collected on a vehicle test shows that a cycle slip detection approach based on time-relative positioning theory can detect slips at the value of one cycle.The results also indicate if two satellites are so near to each other that they have the same equivalent to satellite-receiver geometry,cycle slip detection will be difficult and may get wrong results.Cycle slips of different satellites also affect detection by satellite-receiver geometry.展开更多
Sensors are ubiquitous in the Internet of Things for measuring and collecting data. Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data. Since the I...Sensors are ubiquitous in the Internet of Things for measuring and collecting data. Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data. Since the Internet of Things contains many sorts of sensors, the measurement data collected by these sensors are multi-type data, sometimes contai- ning temporal series information. If we separately deal with different sorts of data, we will miss useful information. This paper proposes a method to dis- cover the correlation in multi-faceted data, which contains many types of data with temporal informa- tion, and our method can simultaneously deal with multi-faceted data. We transform high-dimensional multi-faeeted data into lower-dimensional data which is set as multivariate Gaussian Graphical Models, then mine the correlation in multi-faceted data by discover the structure of the multivariate Gausslan Graphical Models. With a real data set, we verifies our method, and the experiment demonstrates that the method we propose can correctly fred out the correlation among multi-faceted meas- urement data.展开更多
文摘An efficient cycle slip detection method is proposed for high precision positioning and navigation results with global positioning system （GPS）,which is based on the assumption of a high sampling interval, measurement errors are so small that they can be ignored in the temporal single difference observables. And ambiguities are ordinarily equal to zero,but could be the number of cycles that have ＂slipped＂ if loss-of-lock has occurred.Therefore,cycle slips are estimated as parameters of time-relative positioning observation equations.Because the temporal single difference observables are taken at different epochs and different stations with a single GPS receiver,if time-relative positioning observation equations are linearized as that of conventional relative positioning,the design matrix will be rank defective.To obtain a stable linearization scheme,time-relative positioning observation equations are further analyzed,and the concept of virtual measurement is applied.A sample of data collected on a vehicle test shows that a cycle slip detection approach based on time-relative positioning theory can detect slips at the value of one cycle.The results also indicate if two satellites are so near to each other that they have the same equivalent to satellite-receiver geometry,cycle slip detection will be difficult and may get wrong results.Cycle slips of different satellites also affect detection by satellite-receiver geometry.
基金the Project"The Basic Research on Internet of Things Architecture"supported by National Key Basic Research Program of China(No.2011CB302704)supported by National Natural Science Foundation of China(No.60802034)+2 种基金Specialized Research Fund for the Doctoral Program of Higher Education(No.20070013026)Beijing Nova Program(No.2008B50)"New generation broadband wireless mobile communication network"Key Projects for Science and Technology Development(No.2011ZX03002-002-01)
文摘Sensors are ubiquitous in the Internet of Things for measuring and collecting data. Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data. Since the Internet of Things contains many sorts of sensors, the measurement data collected by these sensors are multi-type data, sometimes contai- ning temporal series information. If we separately deal with different sorts of data, we will miss useful information. This paper proposes a method to dis- cover the correlation in multi-faceted data, which contains many types of data with temporal informa- tion, and our method can simultaneously deal with multi-faceted data. We transform high-dimensional multi-faeeted data into lower-dimensional data which is set as multivariate Gaussian Graphical Models, then mine the correlation in multi-faceted data by discover the structure of the multivariate Gausslan Graphical Models. With a real data set, we verifies our method, and the experiment demonstrates that the method we propose can correctly fred out the correlation among multi-faceted meas- urement data.
