Self-tuning Measurement Fusion Kalman Predictor

摘要：

For the multisensor system with unknown noise variances and with the different measurement matrices, the on-line estimators of the noise variances are obtained by a correlation method, and a self-tuning measurement fusion Kalman predictor is presented. Its basic principle is that the optimal fuser, accompanied by a recursive identifier of noise variances, will yield a selftuning fuser. It is strictly proved by using the dynamic error system analysis (DESA) method, that the selftuning Kalman fuser converges to the optimal Kalman fuser with probability one or in a realization. It can reduce the computational burden, and has asymptotic global optimality. A simulation example for a target tracking system with 3-sensor shows its effectiveness.

关键词： Multisensor information fusion weighted measurement fusion self-tuning Kalman predictor noise variance estimation convergence asymptotic global optimality

作者: Yuan Gao WenJing Jia Zili Deng

作者单位: Department of Automation Heilongjiang University 150080, Harbin, China

会议类型: 国际会议

会议名称: The International Colloquium on Onformation Fusion 2007(2007年国际信息融合研讨会)

会议地点: 西安

会议语种:英文

页码: 213-218

在线出版日期: 2007-08-22（万方平台首次上网日期，不代表论文的发表时间）