An Interactive Fusion Algorithm based on Geometric Analysis in Multi-sensor System
Kalman Filter is a very important tool in multi sensor data fusion. One problem with the Kalman Filter is that it requires either that the measurements are independent or that the cross-covariance or correlation is known. However, cross-covariance among measurements from different local sensors is inevitable owning to common process noise and not easily calculated owning to insufficient information and high computational complexity. So a recent reseasch emphasis focuses on seeking new methods of fusing state vectors and their covariance or simplifying current methods, in this paper, a new geometric fusion method called covariance coverage is proposed, which not only neednt to consider crosscovariance between measurements, but also has low computational complexity. Whats more, covariance coverage method has strong extensibility and can directly support the fusion of tracking system who has more than two local sensors. Simulation experiments and results show that the accurateness of fusion state estimate by covariance coverage method is clearly higher than that of each local sensor and a little letter than that of covariance intersection algorithm proposed by J.Julier.
component cross-covariance multi-sensor coverage geometric analysis
LIU Zhi WANG Minghui LIU Zhi
School of Computer Science Sichuan University Chengdu, China College of Computer Science&Engineering Chongqing University of Technology Chongqing, China
国际会议
厦门
英文
346-350
2010-10-29（万方平台首次上网日期，不代表论文的发表时间）