摘要
本文提出了一种具有在线调整噪声参数功能的卡尔曼自适应滤波算法及其在船舶导航目标跟踪中的应用。实际中系统噪声和量测噪声的统计特性是动态变化的,但在传统卡尔曼滤波中一般认为系统噪声模型是先验已知的,噪声均值和协方差都是定值,这必然造成滤波效果不理想、目标跟踪精度低甚至出现目标跟踪丢失的问题。针对这种情况,通过在线自适应调整噪声均值和协方差,动态跟踪噪声统计特性的变化,从而提高对目标的跟踪精度。在线实现可以有效地利用系统的部分数据进行更新迭代,减小计算量并且易于工程实现。最后通过船舶目标仿真实验的结果验证了本算法的有效性。
A Kalman filter with on-line parameter adjustment functionality and its application in ship target tracking is proposed. In practical systems, noise statistics has dynamic features. Gennerally, in conventional Kalman filtering, the variances of state and measurement noise are assumed to be fixed and known beforehand. However, this assumption does not hold in practice generally. The Kalman filter without correct information on noise covariances could produce poor performance on filtering. In the worst case,it leads the filter to diverge. In order to overcome this problem, a method which adaptivly ad- justs the noise mean and covariance to track the dynamic noise is proposed,so as to improve the precision of target tracking. Online realization can effectively use part of data to update iteration, which reduces calculation and is easy for engineering realization. Finally, the results of the ship target simulation dem- onstrate the effectiveness of the porposed algorithm.
出处
《计算机工程与科学》
CSCD
北大核心
2012年第6期93-96,共4页
Computer Engineering & Science
基金
广东自然科学基金资助项目(8451064101000498)
关键词
在线实现
卡尔曼滤波
目标跟踪
on-line realization
Kalman filtering
target tracking
