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
以HMR3000磁罗经和BeeL ine GPS组成组合导航系统,对系统功能和其中的关键难题进行了研究。推导了HMR3000误差模型,根据误差模型与BeeL ine GPS系统进行最优组合;针对此系统及组合算法,进行了一系列的试验测试。试验数据表明,组合导航...以HMR3000磁罗经和BeeL ine GPS组成组合导航系统,对系统功能和其中的关键难题进行了研究。推导了HMR3000误差模型,根据误差模型与BeeL ine GPS系统进行最优组合;针对此系统及组合算法,进行了一系列的试验测试。试验数据表明,组合导航系统在天线基线长度大于5m时,航向误差小于0.05°,其精度优于单独的BeeL ine和HMR3000;并解决了该组合系统航向连续性等难题。展开更多
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。
文摘以HMR3000磁罗经和BeeL ine GPS组成组合导航系统,对系统功能和其中的关键难题进行了研究。推导了HMR3000误差模型,根据误差模型与BeeL ine GPS系统进行最优组合;针对此系统及组合算法,进行了一系列的试验测试。试验数据表明,组合导航系统在天线基线长度大于5m时,航向误差小于0.05°,其精度优于单独的BeeL ine和HMR3000;并解决了该组合系统航向连续性等难题。
