多传感器网络化线性离散系统的每个传感器基于自己的观测数据可进行局部状态估计。当局部估值被传输给融合中心时,可能遭受DoS(Denial of service)攻击。为了补偿DoS攻击引起的数据丢失,采用丢失数据的预报器进行补偿。应用线性无偏最...多传感器网络化线性离散系统的每个传感器基于自己的观测数据可进行局部状态估计。当局部估值被传输给融合中心时,可能遭受DoS(Denial of service)攻击。为了补偿DoS攻击引起的数据丢失,采用丢失数据的预报器进行补偿。应用线性无偏最小方差矩阵加权融合算法获得分布式融合状态滤波器。所提出的分布式融合滤波器改善了局部估计的精度,且比协方差交叉融合算法具有更高的估计精度。仿真例子验证了算法的有效性。展开更多
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 multisens...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.展开更多
文摘多传感器网络化线性离散系统的每个传感器基于自己的观测数据可进行局部状态估计。当局部估值被传输给融合中心时,可能遭受DoS(Denial of service)攻击。为了补偿DoS攻击引起的数据丢失,采用丢失数据的预报器进行补偿。应用线性无偏最小方差矩阵加权融合算法获得分布式融合状态滤波器。所提出的分布式融合滤波器改善了局部估计的精度,且比协方差交叉融合算法具有更高的估计精度。仿真例子验证了算法的有效性。
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).
文摘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.
