Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework...Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.展开更多
针对室内可见光定位中非视距信道(Non Line of Sight,NLOS)导致定位精度不足的问题,提出了一种基于动态高斯加权(Dynamic Gaussian Weighted,DGW)接收信号强度指示(Received Signal Strength Indicator,RSSI)和遗传算法(Genetic Algorit...针对室内可见光定位中非视距信道(Non Line of Sight,NLOS)导致定位精度不足的问题,提出了一种基于动态高斯加权(Dynamic Gaussian Weighted,DGW)接收信号强度指示(Received Signal Strength Indicator,RSSI)和遗传算法(Genetic Algorithm,GA)改进支持向量回归(Support Vector Regression,SVR)的室内可见光定位算法。首先,构建指纹库并划分数据集,计算接收器与光源之间的距离动态调整高斯函数的标准差,再结合RSSI信号的波动性进行自适应加权,以减少NLOS对定位的影响。然后,使用GA优化SVR模型的参数,得到最佳定位模型。最后,使用最佳定位模型对加权后的指纹数据进行定位预测。实验结果表明:本算法的平均定位误差为7.1 cm,相较于SVR、SVR-GA等算法降低了21.1%~42.3%,并且能有效降低NLOS的影响、提高室内定位的精度,具有较强的应用前景。展开更多
A model of correcting the nonlinear error of photoelectric displacement sensor was established based on the least square support vector machine.The parameters of the correcting nonlinear model,such as penalty factor a...A model of correcting the nonlinear error of photoelectric displacement sensor was established based on the least square support vector machine.The parameters of the correcting nonlinear model,such as penalty factor and kernel parameter,were optimized by chaos genetic algorithm.And the nonlinear correction of photoelectric displacement sensor based on least square support vector machine was applied.The application results reveal that error of photoelectric displacement sensor is less than 1.5%,which is rather satisfactory for nonlinear correction of photoelectric displacement sensor.展开更多
文摘Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.
文摘针对室内可见光定位中非视距信道(Non Line of Sight,NLOS)导致定位精度不足的问题,提出了一种基于动态高斯加权(Dynamic Gaussian Weighted,DGW)接收信号强度指示(Received Signal Strength Indicator,RSSI)和遗传算法(Genetic Algorithm,GA)改进支持向量回归(Support Vector Regression,SVR)的室内可见光定位算法。首先,构建指纹库并划分数据集,计算接收器与光源之间的距离动态调整高斯函数的标准差,再结合RSSI信号的波动性进行自适应加权,以减少NLOS对定位的影响。然后,使用GA优化SVR模型的参数,得到最佳定位模型。最后,使用最佳定位模型对加权后的指纹数据进行定位预测。实验结果表明:本算法的平均定位误差为7.1 cm,相较于SVR、SVR-GA等算法降低了21.1%~42.3%,并且能有效降低NLOS的影响、提高室内定位的精度,具有较强的应用前景。
基金Project(50925727) supported by the National Fund for Distinguish Young Scholars of ChinaProject(60876022) supported by the National Natural Science Foundation of China+1 种基金Project(2010FJ4141) supported by Hunan Provincial Science and Technology Foundation,ChinaProject supported by the Fund of the Key Construction Academic Subject (Optics) of Hunan Province,China
文摘A model of correcting the nonlinear error of photoelectric displacement sensor was established based on the least square support vector machine.The parameters of the correcting nonlinear model,such as penalty factor and kernel parameter,were optimized by chaos genetic algorithm.And the nonlinear correction of photoelectric displacement sensor based on least square support vector machine was applied.The application results reveal that error of photoelectric displacement sensor is less than 1.5%,which is rather satisfactory for nonlinear correction of photoelectric displacement sensor.
