| 引用本文: | 李银伢,谭维茜,盛安冬.改进型粒子滤波算法在多站纯方位被动跟踪中的应用[J].控制理论与应用,2011,28(8):1081~1086.[点击复制] |
| LI Yin-ya,TAN Wei-qian,SHENG An-dong.Application of improved particle filter algorithm to bearings-only passive tracking in multiple stations[J].Control Theory and Technology,2011,28(8):1081~1086.[点击复制] |
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| 改进型粒子滤波算法在多站纯方位被动跟踪中的应用 |
| Application of improved particle filter algorithm to bearings-only passive tracking in multiple stations |
| 摘要点击 2724 全文点击 1625 投稿时间：2010-05-05 修订日期：2010-10-22 |
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| DOI编号 10.7641/j.issn.1000-8152.2011.8.CCTA100480 |
| 2011,28(8):1081-1086 |
| 中文关键词 粒子滤波 被动跟踪 纯方位 |
| 英文关键词 particle filter passive tracking bearings-only |
| 基金项目 国家自然科学基金资助项目(60804019); 南京理工大学卓越计划、紫金之星资助项目(AB39120). |
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| 中文摘要 |
| 针对多站纯方位被动定位与跟踪问题, 给出了一种基于均匀重采样和带自适应因子的改进型粒子滤波算法. 首先, 基于无迹卡尔曼(UKF)粒子滤波器, 将参考分布融入最新观测信息, 得到符合真实状态的后验概率分布; 借助重采样和使用鲁棒估计, 改善了粒子滤波的退化问题. 其次, 引入自适应因子以调整UKF的状态模型协方差与观测模型协方差的比例, 得到较高精度的概率分布. 仿真结果表明, 改进的粒子滤波算法能够实现多站纯方位被动跟踪, 比传统非线性滤波器有更高的跟踪精度. |
| 英文摘要 |
| For the problem of bearings-only passive localization and tracking in multiple stations, we propose an improved particle filter algorithm with an adaptive factor based on evenly re-sampling. In the unscented Kalman filter(UKF) particle filter, the posterior probability distribution of true state-values is obtained by integrating the reference distribution with the latest observed information. The degeneracy phenomenon in the particle filter is relieved by re-sampling and robust estimation approaches. By introducing an adaptive factor for adjusting the proportion between the state-model covariance and the observation-model covariance of UKF, we obtain a probability distribution with higher precision. Simulation results show that the proposed particle filter algorithm provides higher precision than the traditional nonlinear filters in bearings-only passive localization and tracking for multiple stations. |
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