The paper aims to challenge non-GPS navigation problems by using visual sensors and geo-referenced images. An area-based method is proposed to estimate full navigation parameters(FNPs), including attitude, altitude an...The paper aims to challenge non-GPS navigation problems by using visual sensors and geo-referenced images. An area-based method is proposed to estimate full navigation parameters(FNPs), including attitude, altitude and horizontal position, for unmanned aerial vehicle(UAV) navigation. Our method is composed of three main modules: geometric transfer function, local normalized sobel energy image(LNSEI) based objective function and simplex-simulated annealing(SSA) based optimization algorithm. The adoption of relatively rich scene information and LNSEI, makes it possible to yield a solution robustly even in the presence of very noisy cases, such as multi-modal and/or multi-temporal images that differ in the type of visual sensor, season, illumination, weather, and so on, and also to handle the sparsely textured regions where features are barely detected or matched. Simulation experiments using many synthetic images clearly support noise resistance and estimation accuracy, and experimental results using 2367 real images show the maximum estimation error of 5.16(meter) for horizontal position, 9.72(meter) for altitude and 0.82(degree) for attitude.展开更多
Hydrological risk is highly dependent on the occurrence of extreme rainfalls.This fact has led to a wide range of studies on the estimation and uncertainty analysis of the extremes.In most cases,confidence intervals(C...Hydrological risk is highly dependent on the occurrence of extreme rainfalls.This fact has led to a wide range of studies on the estimation and uncertainty analysis of the extremes.In most cases,confidence intervals(CIs)are constructed to represent the uncertainty of the estimates.Since the accuracy of CIs depends on the asymptotic normality of the data and is questionable with limited observations in practice,a Bayesian highest posterior density(HPD)interval,bootstrap percentile interval,and profile likelihood(PL)interval have been introduced to analyze the uncertainty that does not depend on the normality assumption.However,comparison studies to investigate their performances in terms of the accuracy and uncertainty of the estimates are scarce.In addition,the strengths,weakness,and conditions necessary for performing each method also must be investigated.Accordingly,in this study,test experiments with simulations from varying parent distributions and different sample sizes were conducted.Then,applications to the annual maximum rainfall(AMR)time series data in South Korea were performed.Five districts with 38-year(1973–2010)AMR observations were fitted by the three aforementioned methods in the application.From both the experimental and application results,the Bayesian method is found to provide the lowest uncertainty of the design level while the PL estimates generally have the highest accuracy but also the largest uncertainty.The bootstrap estimates are usually inferior to the other two methods,but can perform adequately when the distribution model is not heavy-tailed and the sample size is large.The distribution tail behavior and the sample size are clearly found to affect the estimation accuracy and uncertainty.This study presents a comparative result,which can help researchers make decisions in the context of assessing extreme rainfall uncertainties.展开更多
文摘The paper aims to challenge non-GPS navigation problems by using visual sensors and geo-referenced images. An area-based method is proposed to estimate full navigation parameters(FNPs), including attitude, altitude and horizontal position, for unmanned aerial vehicle(UAV) navigation. Our method is composed of three main modules: geometric transfer function, local normalized sobel energy image(LNSEI) based objective function and simplex-simulated annealing(SSA) based optimization algorithm. The adoption of relatively rich scene information and LNSEI, makes it possible to yield a solution robustly even in the presence of very noisy cases, such as multi-modal and/or multi-temporal images that differ in the type of visual sensor, season, illumination, weather, and so on, and also to handle the sparsely textured regions where features are barely detected or matched. Simulation experiments using many synthetic images clearly support noise resistance and estimation accuracy, and experimental results using 2367 real images show the maximum estimation error of 5.16(meter) for horizontal position, 9.72(meter) for altitude and 0.82(degree) for attitude.
基金supported by Hanyang University(Grant No.HY-2014)
文摘Hydrological risk is highly dependent on the occurrence of extreme rainfalls.This fact has led to a wide range of studies on the estimation and uncertainty analysis of the extremes.In most cases,confidence intervals(CIs)are constructed to represent the uncertainty of the estimates.Since the accuracy of CIs depends on the asymptotic normality of the data and is questionable with limited observations in practice,a Bayesian highest posterior density(HPD)interval,bootstrap percentile interval,and profile likelihood(PL)interval have been introduced to analyze the uncertainty that does not depend on the normality assumption.However,comparison studies to investigate their performances in terms of the accuracy and uncertainty of the estimates are scarce.In addition,the strengths,weakness,and conditions necessary for performing each method also must be investigated.Accordingly,in this study,test experiments with simulations from varying parent distributions and different sample sizes were conducted.Then,applications to the annual maximum rainfall(AMR)time series data in South Korea were performed.Five districts with 38-year(1973–2010)AMR observations were fitted by the three aforementioned methods in the application.From both the experimental and application results,the Bayesian method is found to provide the lowest uncertainty of the design level while the PL estimates generally have the highest accuracy but also the largest uncertainty.The bootstrap estimates are usually inferior to the other two methods,but can perform adequately when the distribution model is not heavy-tailed and the sample size is large.The distribution tail behavior and the sample size are clearly found to affect the estimation accuracy and uncertainty.This study presents a comparative result,which can help researchers make decisions in the context of assessing extreme rainfall uncertainties.
