对瞬时失效率、平均失效率、返修率的表征内容和本质含义进行了深入的探讨。同时详解了平均无故障间隔时间(Meantime Between Failures,MTBF)与返修率的内在联系。对这几个可靠性参量在实际应用和工程计算时的适用场合做了分析,用简洁...对瞬时失效率、平均失效率、返修率的表征内容和本质含义进行了深入的探讨。同时详解了平均无故障间隔时间(Meantime Between Failures,MTBF)与返修率的内在联系。对这几个可靠性参量在实际应用和工程计算时的适用场合做了分析,用简洁易懂的论述和简单的实例,重点说明了瞬时失效率、平均失效率和返修率的内在含义。展开更多
An improved particle filtering（IPF） is presented to perform maneuvering target tracking in dense clutter.The proposed filter uses several efficient variance reduction methods to combat particle degeneracy,low mode p...An improved particle filtering（IPF） is presented to perform maneuvering target tracking in dense clutter.The proposed filter uses several efficient variance reduction methods to combat particle degeneracy,low mode prior probabilities and measure-ment-origin uncertainty.Within the framework of a hybrid state estimation,each particle samples a discrete mode from its poste-rior distribution and the continuous state variables are approximated by a multivariate Gaussian mixture that is updated by an unscented Kalman filtering（UKF）.The uncertainty of measurement origin is solved by Monte Carlo probabilistic data associa-tion method where the distribution of interest is approximated by particle filtering and UKF.Correct data association and precise behavior mode detection are successfully achieved by the proposed method in the environment with heavy clutter and very low mode prior probability.The performance of the proposed filter is examined and compared by Monte Carlo simulation over typical target scenario for various clutter densities.The simulation results show the effectiveness of the proposed filter.展开更多
文摘对瞬时失效率、平均失效率、返修率的表征内容和本质含义进行了深入的探讨。同时详解了平均无故障间隔时间(Meantime Between Failures,MTBF)与返修率的内在联系。对这几个可靠性参量在实际应用和工程计算时的适用场合做了分析,用简洁易懂的论述和简单的实例,重点说明了瞬时失效率、平均失效率和返修率的内在含义。
基金National Natural Science Foundation of China (60975028)National High-tech Research and Development Program (2009AA112203)+1 种基金Fundamental Research Funds for the Central Universities (CHD2009JC037)Natural Science Basic Research Plan in Shaanxi Province (2006F12)
文摘An improved particle filtering（IPF） is presented to perform maneuvering target tracking in dense clutter.The proposed filter uses several efficient variance reduction methods to combat particle degeneracy,low mode prior probabilities and measure-ment-origin uncertainty.Within the framework of a hybrid state estimation,each particle samples a discrete mode from its poste-rior distribution and the continuous state variables are approximated by a multivariate Gaussian mixture that is updated by an unscented Kalman filtering（UKF）.The uncertainty of measurement origin is solved by Monte Carlo probabilistic data associa-tion method where the distribution of interest is approximated by particle filtering and UKF.Correct data association and precise behavior mode detection are successfully achieved by the proposed method in the environment with heavy clutter and very low mode prior probability.The performance of the proposed filter is examined and compared by Monte Carlo simulation over typical target scenario for various clutter densities.The simulation results show the effectiveness of the proposed filter.
