摘要
针对无线传感器网络中存在的通信带宽受限等问题,设计了基于事件触发的分布式卡尔曼一致性滤波算法。采用增量发送的传输机制,每个传感器仅在当前时刻观测值与最新触发时刻观测值之间的差值平方超出阈值时,才将观测值发送到相应的估值器。而每个估值器都可以通过时间触发的规则从其邻居节点接收到估计值。为了避免估值之间互协方差矩阵的计算,通过局部最小化方差的上界提出了事件触发的分布式卡尔曼滤波器。基于李雅普诺夫方法证明了算法在均方意义下的指数有界性。数值仿真验证了事件触发的阈值越小,通信率越高且滤波器的估计精度越高;否则,通信率越低且估计精度越低。
Aimed at the problem that communication bandwidth is limited in wireless sensor networks,a distributed Kalman consensus filtering algorithm is designed based on event-triggered strategy.The transmission mechanism of send-on-delta is adopted.Each sensor sends its own observations to the corresponding estimators only when the square of difference between the current observation and the latest sent observation exceeds a tolerable threshold.In addition,each estimator can receive estimates from its neighbor nodes through time-triggered rules.In order to avoid the calculation of cross-covariance matrices between estimators,an event-triggered distributed Kalman filter is proposed by locally minimizing an upper bound of the variance.The exponential boundedness of the algorithm in the sense of mean square is proved by the Lyapunov method.The numerical simulation shows that the less the event-triggered threshold value,the greater the communication rate,and the higher the estimation accuracy of the proposed filter.Otherwise,the lower the communication rate,the lower the estimation accuracy.
作者
刘春玉
孙书利
LIU Chunyu;SUN Shuli(School of Electronic Engineering,Heilongjiang University,Harbin 150080,China)
出处
《空军工程大学学报（自然科学版）》
CSCD
北大核心
2021年第3期89-95,共7页
Journal of Air Force Engineering University(Natural Science Edition)
关键词
传感器网络
事件触发
分布式滤波
卡尔曼一致性滤波
均方意义下的指数有界性
sensor network
event-triggered
distributed filtering
Kalman consensus filtering
exponential boundedness in the sense of mean square
