摘要
针对标准粒子滤波算法存在的粒子退化问题,提出了一种改进的粒子滤波算法,该算法将不敏卡尔曼滤波算法(UKF)、线性优化的思想和基本粒子滤波算法相结合,运用不敏卡尔曼滤波算法获得重要性概率密度函数,提高了粒子的使用效率;运用线性优化的思想,保证了所有粒子都以一定的概率对状态估计作出贡献,提高了粒子的多样性。仿真结果表明,改进的算法很好的解决了基本粒子滤波存在的粒子退化问题,具有更高的状态估计精度。
Due to the degenerac existing in the particle filter,a new combined particle filter algorithm is presented in this paper.Which based on the unscented Kalman filter（UKF） altorithm and linear optimization method.In the proposed algorithm,unscented Kalman filter algorithm is used to generate the importance proposal distribution,and the linear optimazation method is applied to enhance the diversity of samples with using all particles contribute to the state estimate.The Monte Carlo simulation results show that the improved particle filter can solve the degenerac in particle filter and has a good accuracy in state estimating.
出处
《舰船电子工程》
2011年第11期57-59,139,共4页
Ship Electronic Engineering
关键词
粒子滤波
不敏卡尔曼滤波
线性优化
particle filter
unscented Kalman filter
linear optimization method
