Initialization of the Kalman Filter without Assumptions on the Initial State

摘要：

In absence of covariance data, Kalman filters are usually initialized by guessing the initial state. Making the variance of the initial state estimate large makes sure that the estimate converges quickly and that the influence of the initial guess soon will be negligible. If, however, only very few measurements are available during the estimation process and an estimate is wanted as soon as possible, this might not be enough. This paper presents a method to initialize the Kalman filter without any knowledge about the distribution of the initial state and without making any guesses.

作者: Magnus Linderoth Kristian Soltesz Anders Robertsson Rolf Johansson

作者单位: Department of Automatic Control,LTH,Lund University,Sweden

会议类型: 国际会议

会议名称: 2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

会议地点: 上海

会议语种:英文

页码: 4992-4997

在线出版日期: 2011-05-09（万方平台首次上网日期，不代表论文的发表时间）