针对传统无迹卡尔曼滤波(unscented Kalman filter,UKF)在系统状态发生突变时估计精度下降的问题,将改进的强跟踪滤波算法与基于高斯概率密度高阶导的无迹卡尔曼滤波算法(high order probability density derivative,HUKF)相结合,提出...针对传统无迹卡尔曼滤波(unscented Kalman filter,UKF)在系统状态发生突变时估计精度下降的问题,将改进的强跟踪滤波算法与基于高斯概率密度高阶导的无迹卡尔曼滤波算法(high order probability density derivative,HUKF)相结合,提出了高阶强跟踪无迹卡尔曼滤波方法(high order strong tracking UKF,HSUKF)。该算法采用高斯概率密度函数高阶导数的极值作为Sigma样点进行无迹转换,通过样本点捕捉更高阶的中心矩来提高非线性变换近似精度。将改进的强跟踪滤波算法引入到HUKF中,通过渐消因子修正预测新息协方差和预测互协方差矩阵,强迫新息正交,在不增加计算复杂度的前提下提高了算法在状态发生突变时的适应能力。将本文算法应用于时差频差的无源跟踪中,通过对目标状态发生突变的跟踪问题进行数值仿真和实例论证表明HSUKF算法兼具了计算复杂度低和估计精度高的特性,且在系统状态发生突变的情况下表现出良好的滤波性能。展开更多
Since in most blind source separation(BSS)algorithms the estimations of probability density function(pdf)of sources are fixed or can only switch between one sup-Gaussian and other sub-Gaussian model,they may not be ef...Since in most blind source separation(BSS)algorithms the estimations of probability density function(pdf)of sources are fixed or can only switch between one sup-Gaussian and other sub-Gaussian model,they may not be efficient to separate sources with different distributions.So to solve the problem of pdf mismatch and the separation of hybrid mixture in BSS,the generalized Gaussian model(GGM)is introduced to model the pdf of the sources since it can provide a general structure of univariate distributions.Its great advantage is that only one parameter needs to be determined in modeling the pdf of different sources,so it is less complex than Gaussian mixture model.By using maximum likelihood(ML)approach,the convergence of the proposed algorithm is improved.The computer simulations show that it is more efficient and valid than conventional methods with fixed pdf estimation.展开更多
文摘针对传统无迹卡尔曼滤波(unscented Kalman filter,UKF)在系统状态发生突变时估计精度下降的问题,将改进的强跟踪滤波算法与基于高斯概率密度高阶导的无迹卡尔曼滤波算法(high order probability density derivative,HUKF)相结合,提出了高阶强跟踪无迹卡尔曼滤波方法(high order strong tracking UKF,HSUKF)。该算法采用高斯概率密度函数高阶导数的极值作为Sigma样点进行无迹转换,通过样本点捕捉更高阶的中心矩来提高非线性变换近似精度。将改进的强跟踪滤波算法引入到HUKF中,通过渐消因子修正预测新息协方差和预测互协方差矩阵,强迫新息正交,在不增加计算复杂度的前提下提高了算法在状态发生突变时的适应能力。将本文算法应用于时差频差的无源跟踪中,通过对目标状态发生突变的跟踪问题进行数值仿真和实例论证表明HSUKF算法兼具了计算复杂度低和估计精度高的特性,且在系统状态发生突变的情况下表现出良好的滤波性能。
文摘Since in most blind source separation(BSS)algorithms the estimations of probability density function(pdf)of sources are fixed or can only switch between one sup-Gaussian and other sub-Gaussian model,they may not be efficient to separate sources with different distributions.So to solve the problem of pdf mismatch and the separation of hybrid mixture in BSS,the generalized Gaussian model(GGM)is introduced to model the pdf of the sources since it can provide a general structure of univariate distributions.Its great advantage is that only one parameter needs to be determined in modeling the pdf of different sources,so it is less complex than Gaussian mixture model.By using maximum likelihood(ML)approach,the convergence of the proposed algorithm is improved.The computer simulations show that it is more efficient and valid than conventional methods with fixed pdf estimation.
