Life distribution analysis based on Lévy subordinators for degradation with random jumps

For a component or a system subject to stochastic degradation with sporadic jumps that occur at random times and have random sizes, we propose to model the cumulative degradation with random jumps using a single stochastic process based on the characteristics of Lévy subordinators, the class of nondecreasing Lévy processes. Based on the inverse Fourier transform, we derive a new closed‐form reliability function and probability density function for lifetime, represented by Lévy measures. The reliability function derived using the traditional convolution approach for common stochastic models such as gamma degradation process with random jumps, is revealed to be a special case of our general model. Numerical experiments are used to demonstrate that our model performs well for different applications, when compared with the traditional convolution method. More importantly, it is a general and useful tool for life distribution analysis of stochastic degradation with random jumps in multidimensional cases. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 483–492, 2015     

基于观测预测粒子滤波的雷达组网目标状态估计

雷达组网系统跟踪目标时,观测数据与目标跟踪状态成严重非线性关系,难以用卡尔曼滤波最优估计方法,处理非高斯非线性系统滤波估计问题的粒子滤波算法容易产生粒子退化问题。因此,使用观测预测粒子滤波算法解决这个问题,该算法基于观测似然进行重要性采样,结合一步预测信息计算粒子权值,保证了采样粒子处于高观测似然区,并充分利用了一步预测信息。仿真验证表明,将观测预测粒子滤波算法应用于目标状态估计,避免了粒子退化,收敛快,估计精度高。