THE RESEARCH OF GRADATION FUSION ALGORITHM BASED ON MULTISENSOR ASYNCHRONOUS SAMPLING SYSTEM  

作　　者：Wen Chenglin Zhang Liantang Ge Quanbo 

机构地区：[1]College of Automation, Hangzhou Dianzi University, Hangzhou 310018, China [2]College of Computer and Information Engineering, Henan University, Kaifeng 475001, China

出　　处：《Journal of Electronics(China)》2005年第5期534-545,共12页电子科学学刊（英文版）

基　　金：Supported by the National Natural Science Foundation of China (No.60434020, 60374020)International Cooperation Item of Henan (No.0446650006)Henan Outstanding Youth Science Fund (No.0312001900).

摘　　要：This letter explores the distributed multisensor dynamic system, which has uniform sampling velocity and asynchronous sampling data for different sensors, and puts forward a new gradation fusion algorithm of multisensor dynamic system. As the total forecasted increment value between the two adjacent moments is the forecasted estimate value of the corresponding state increment in the fusion center, the new algorithm models the state and the forecasted estimate value of every moment. Kalman filter and all measurements arriving sequentially in the fusion period are employed to update the evaluation of target state step by step, on the condition that the system has obtained the target state evaluation that is based on the overall information in the previous fusion period. Accordingly, in the present period, the fusion evaluation of the target state at each sampling point on the basis of the overall information can be obtained. This letter elaborates the form of this new algorithm. Computer simulation demonstrates that this new algorithm owns greater precision in estimating target state than the present asynchronous fusion algorithm calibrated in time does.This letter explores the distributed multisensor dynamic system, which has uniform sampling velocity and asynchronous sampling data for different sensors, and puts forward a new gradation fusion algorithm of multisensor dynamic system. As the total forecasted increment value between the two adjacent moments is the forecasted estimate value of the corresponding state increment in the fusion center, the new algorithm models the state and the forecasted estimate value of every moment. Kalman filter and all measurements arriving sequentially in the fusion period are employed to update the evaluation of target state step by step, on the condition that the system has obtained the target state evaluation that is based on the overall information in the previous fusion period. Accordingly, in the present period, the fusion evaluation of the target state at each sampling point on the basis of the overall information can be obtained. This letter elaborates the form of this new algorithm. Computer simulation demonstrates that this new algorithm owns greater precision in estimating target state than the present asynchronous fusion algorithm calibrated in time does.

关 键 词：Multisensor system Gradation fusion Asynchronous sampling Kalman filtering 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]