基于传感器网络的时延补偿分布式状态估计

Distributed State Estimation with Time DelayCompensation Based on Sensor Network

提出一种基于传感器网络的分布式状态估计算法,用于具有随机传输时延的网络化系统。为了节省通信带宽,引入了自适应概率量化机制。考虑随机传输时延的存在,利用缓存器中的数据设计了改进的无迹卡尔曼滤波(UKF)算法进行时延补偿。在局部估计完成后,利用快速协方差交叉方法进行融合估计,以获取更加精确的状态估计。此外,还分析了融合估计误差的有界性。最后通过数值模拟算例验证了所提算法的有效性。

A distributed state estimation algorithm based on sensor network is proposed for networked systems with random transmission delays.In order to save communication bandwidth,an adaptive probability quantization mechanism is introduced.Considering the existence of random transmission delays,an improved Unscented Kalman Filter(UKF)algorithm is designed by using the data in the buffer to compensate for the delays.The fast covariance crossing method is used for fusion estimation after local estimation is completed,so as to obtain more accurate state estimation results.In addition,the boundedness of fusion estimation errors is analyzed.The effectiveness of the algorithm is verified by a numerical simulation example.