摘要
研究了具有未知传输干扰和观测丢失的网络化多传感器系统的滤波器设计问题。采用一组满足伯努利分布的随机变量来描述观测丢失现象。在没有传输干扰任何信息的情形下,基于线性无偏最小方差估计准则,设计了系统状态的具有Kalman形式的递推滤波器。应用CI(Covariance Intersection)融合算法给出了分布式次优融合状态滤波器。并基于融合状态滤波器给出了未知传输干扰估计。仿真验证了该算法的有效性。
This paper is concerned with the filter design problem for networked multi-sensor systems with unknown transmission disturbances and packet losses. A group of Bernoulli-distribution random variables are employed to describe the phenomena of packet losses. Based on the linear unbiased minimum variance estimation criterion,a recursive Kalman-type state filter is designed in the absence of any information about the transmission disturbances.Further,applying the CI( Covariance Intersection) fusion algorithm,a distributed suboptimal fusion state filter is designed. The estimators of unknown disturbances are also given based on the fused state filter. Simulation results show the effectiveness of the proposed algorithms.
出处
《黑龙江大学工程学报》
2017年第1期67-72,共6页
Journal of Engineering of Heilongjiang University
基金
国家自然科学基金资助项目(61573132)
黑龙江大学研究生创新科研项目(YJSCX2016-068HLJU)
关键词
未知传输干扰
丢包
网络化多传感器系统
线性无偏最小方差
CI融合
Unknown transmission disturbance
packet loss
networked multi-sensor system
linear unbiased minimum variance
CI fusion
