A suboptimal CI algorithm and its application to distributed target tracking
When the fused variables are independent or the statistics of variables are known perfectly , Kalman filter is rigorous and yields minimum mean squared error estimate. But in most situations, it is impossible to guarantee that the fused variables are independent and even might be highly correlated. In that case, it is possible to “over estimate the statistics.CI algorithm can achieve a consistent estimation, when the correlation between the fused variables are unknown. However the caculation of optimal ω of CI costs lots of time. A simple way to calculate a suboptimal ωis proposed to reduce the time complexity and get a suboptimal estimation. According to the geometrical explanation of CI, a simple calculation equation of ω is given. And the functions of three kinds of fusion algorithm are illustrated in an application of decentralized estimation with circle topology, where it is impossible to consistently use a Kalman filter or other algorithms that need independent constraints.
convariace intersection simple convex combination distributed system data association
Hai-Xiao Cui Xiao-Jun Wu Xiao-Qing Luo
School of IOT engineering Jiangnan university Wuxi, China
国际会议
无锡
英文
329-332
2011-10-14（万方平台首次上网日期，不代表论文的发表时间）