Improving Iterated Extended Kalman Filter for non-Gaussian Noise Environments

摘要：

Kalman filter is an efficient algorithm to estimate the state of linear time-discrete dynamical systems with Gaussian noises. Unfortunately, practical systems are mostly non-linear and noise follows non-Gaussian distributions. This article proposes an improvement in Extended Kalman Filter (EKF) algorithm for predicting non-Gaussian distributed state noise. Further introducing the proposed scheme into the indoor positioning system illustrates that considerably higher accuracy in locating and tracking objects can be achieved.

关键词： component Extended Kalman Filter EKF Particle Filter Indoor positioning system non-Gaussian RSS.

作者: Long Kam-Kim Hanh Dang-Ngoc Tuan Do-Hong

作者单位: Department of Telecommunications Engineering,Faculty of Electrical and Electronics Engineering Ho Chi Minh city University of Technology,Ho Chi Minh city,Vietnam.

会议类型: 国际会议

会议名称: The 6th International Forum on Strategic Technology(IFOST 2011)(第六届国际战略技术论坛)

会议地点: 哈尔滨

会议语种:英文

页码: 1114-1117

在线出版日期: 2011-08-22（万方平台首次上网日期，不代表论文的发表时间）