To identify the endemic error of the precise point positioning which cannot be weakened or eliminated in precise point positioning (PPP) zero-difference model, the 24 h observation data acquired from CHAN station on O...To identify the endemic error of the precise point positioning which cannot be weakened or eliminated in precise point positioning (PPP) zero-difference model, the 24 h observation data acquired from CHAN station on Oct 31st, 2010, were adopted for analyses, different correction models of various errors were discussed and their influences on traditional zero-difference model were analyzed. The results show that the errors cannot be ignored. They must be corrected with suitable models and estimated with auxiliary parameters. The influence magnitudes of all errors are defined, and the results have guiding significance to improve the accuracy of precise point positioning zero-difference model.展开更多
Traditional cubature Kalman filter(CKF)is a preferable tool for the inertial navigation system(INS)/global positioning system(GPS)integration under Gaussian noises.The CKF,however,may provide a significantly biased es...Traditional cubature Kalman filter(CKF)is a preferable tool for the inertial navigation system(INS)/global positioning system(GPS)integration under Gaussian noises.The CKF,however,may provide a significantly biased estimate when the INS/GPS system suffers from complex non-Gaussian disturbances.To address this issue,a robust nonlinear Kalman filter referred to as cubature Kalman filter under minimum error entropy with fiducial points(MEEF-CKF)is proposed.The MEEF-CKF behaves a strong robustness against complex nonGaussian noises by operating several major steps,i.e.,regression model construction,robust state estimation and free parameters optimization.More concretely,a regression model is constructed with the consideration of residual error caused by linearizing a nonlinear function at the first step.The MEEF-CKF is then developed by solving an optimization problem based on minimum error entropy with fiducial points(MEEF)under the framework of the regression model.In the MEEF-CKF,a novel optimization approach is provided for the purpose of determining free parameters adaptively.In addition,the computational complexity and convergence analyses of the MEEF-CKF are conducted for demonstrating the calculational burden and convergence characteristic.The enhanced robustness of the MEEF-CKF is demonstrated by Monte Carlo simulations on the application of a target tracking with INS/GPS integration under complex nonGaussian noises.展开更多
针对弱信号环境下全球定位系统(global position system,GPS)信号捕获问题,提出了一种基于双块零拓展(double block zero padding,DBZP)差分相干捕获算法。该算法将快速傅里叶变换(fast Fourier transform,FFT)、DBZP、差分相干及频率...针对弱信号环境下全球定位系统(global position system,GPS)信号捕获问题,提出了一种基于双块零拓展(double block zero padding,DBZP)差分相干捕获算法。该算法将快速傅里叶变换(fast Fourier transform,FFT)、DBZP、差分相干及频率误差修正等4项技术有机结合,从而有效减小了在FFT计算过程中由大多普勒频移引起的码片速率变化而造成的相关功率损失,同时也削弱了残余多普勒频率造成的功率损失。实验表明,算法能明显提高系统捕获性能,在仿真数据集下,与直接FFT差分相干算法相比,捕获灵敏度提高了约2.8dB,并在给定的积分时间及载噪比下,捕获频率误差的标准差小于20Hz;在实验数据集下,与直接FFT差分相干算法相比,捕获结果信噪比提高了约3dB。展开更多
系统地分析了对流层延迟特性及其误差改正模型的精度及适应性。对GPS信号的对流层延迟误差产生机理进行了理论分析;对常用的4种对流层误差改正模型:霍普菲尔德(Hop-field)模型、萨斯塔莫宁(Saastamoinen)模型、Black模型及Egnos模型的...系统地分析了对流层延迟特性及其误差改正模型的精度及适应性。对GPS信号的对流层延迟误差产生机理进行了理论分析;对常用的4种对流层误差改正模型:霍普菲尔德(Hop-field)模型、萨斯塔莫宁(Saastamoinen)模型、Black模型及Egnos模型的特点及建模方法进行了详细论述;利用从GPS技术权威支持机构Crustal Dynamics Data Information System (CDDIS)得到的GPS对流层相关数据,定量分析了4种对流层误差改正模型的精确性及适用性条件。最后,为对流层延迟改正模型的选择给出了结论性的意见,所得结果为GPS精确定位时对流层延迟改正模型的选择提供了理论依据,具有工程应用参考价值。展开更多
基金Project(20060417004)supported by the PhD Programs Foundation of Ministry of Education of ChinaProject(2009S049)supported by the Liaoning Province University Research Program,China
文摘To identify the endemic error of the precise point positioning which cannot be weakened or eliminated in precise point positioning (PPP) zero-difference model, the 24 h observation data acquired from CHAN station on Oct 31st, 2010, were adopted for analyses, different correction models of various errors were discussed and their influences on traditional zero-difference model were analyzed. The results show that the errors cannot be ignored. They must be corrected with suitable models and estimated with auxiliary parameters. The influence magnitudes of all errors are defined, and the results have guiding significance to improve the accuracy of precise point positioning zero-difference model.
基金supported by the Fundamental Research Funds for the Central Universities(xzy022020045)the National Natural Science Foundation of China(61976175)。
文摘Traditional cubature Kalman filter(CKF)is a preferable tool for the inertial navigation system(INS)/global positioning system(GPS)integration under Gaussian noises.The CKF,however,may provide a significantly biased estimate when the INS/GPS system suffers from complex non-Gaussian disturbances.To address this issue,a robust nonlinear Kalman filter referred to as cubature Kalman filter under minimum error entropy with fiducial points(MEEF-CKF)is proposed.The MEEF-CKF behaves a strong robustness against complex nonGaussian noises by operating several major steps,i.e.,regression model construction,robust state estimation and free parameters optimization.More concretely,a regression model is constructed with the consideration of residual error caused by linearizing a nonlinear function at the first step.The MEEF-CKF is then developed by solving an optimization problem based on minimum error entropy with fiducial points(MEEF)under the framework of the regression model.In the MEEF-CKF,a novel optimization approach is provided for the purpose of determining free parameters adaptively.In addition,the computational complexity and convergence analyses of the MEEF-CKF are conducted for demonstrating the calculational burden and convergence characteristic.The enhanced robustness of the MEEF-CKF is demonstrated by Monte Carlo simulations on the application of a target tracking with INS/GPS integration under complex nonGaussian noises.
文摘针对弱信号环境下全球定位系统(global position system,GPS)信号捕获问题,提出了一种基于双块零拓展(double block zero padding,DBZP)差分相干捕获算法。该算法将快速傅里叶变换(fast Fourier transform,FFT)、DBZP、差分相干及频率误差修正等4项技术有机结合,从而有效减小了在FFT计算过程中由大多普勒频移引起的码片速率变化而造成的相关功率损失,同时也削弱了残余多普勒频率造成的功率损失。实验表明,算法能明显提高系统捕获性能,在仿真数据集下,与直接FFT差分相干算法相比,捕获灵敏度提高了约2.8dB,并在给定的积分时间及载噪比下,捕获频率误差的标准差小于20Hz;在实验数据集下,与直接FFT差分相干算法相比,捕获结果信噪比提高了约3dB。
文摘系统地分析了对流层延迟特性及其误差改正模型的精度及适应性。对GPS信号的对流层延迟误差产生机理进行了理论分析;对常用的4种对流层误差改正模型:霍普菲尔德(Hop-field)模型、萨斯塔莫宁(Saastamoinen)模型、Black模型及Egnos模型的特点及建模方法进行了详细论述;利用从GPS技术权威支持机构Crustal Dynamics Data Information System (CDDIS)得到的GPS对流层相关数据,定量分析了4种对流层误差改正模型的精确性及适用性条件。最后,为对流层延迟改正模型的选择给出了结论性的意见,所得结果为GPS精确定位时对流层延迟改正模型的选择提供了理论依据,具有工程应用参考价值。
