For the problem of motion state estimation of mobile target tracked by sensor nodes, the information-weighted Kalman consensus filter (IKCF) is introduced for sensor networks with switching communication topologies. In order to improve the dynamic performance of the IKCF, the low-pass consensus filtering algorithm is used to estimate the motion acceleration of the target. Moreover, an improved low-pass consensus filter is proposed to make the estimated motion accelerations converge to a smaller range. With low-pass consensus filtering for measured motion accelerations, the switched linear system of collective estimation errors of the IKCF is derived. Furthermore, it is proved that the switched linear system of collective estimation errors is globally uniformly asymptotically stable with a weighted $${l_2}$$ l 2 -gain. Consequently, the conclusion is deduced that estimations of the target motion state can achieve consensus and converge to the target motion state within a bounded region as $$t \rightarrow \infty $$ t → ∞ under switching topologies. Finally, the effectiveness of the proposed approaches is illustrated by several illustrative examples.

中文翻译：

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具有切换拓扑结构的移动目标分布式运动状态估计

针对传感器节点跟踪的移动目标的运动状态估计问题，为具有切换通信拓扑结构的传感器网络引入信息加权卡尔曼一致性滤波器（IKCF）。为了提高IKCF的动态性能，采用低通一致滤波算法来估计目标的运动加速度。此外，提出了一种改进的低通一致性滤波器，使估计的运动加速度收敛到更小的范围。通过对测量的运动加速度进行低通一致滤波，导出了 IKCF 集体估计误差的切换线性系统。此外，证明了集体估计误差的切换线性系统是全局一致渐近稳定的，具有加权的$${l_2}$$l 2 -gain。最后，推导出目标运动状态的估计可以在切换拓扑下达到一致并收敛到有界区域内的目标运动状态为$$t \rightarrow \infty $$ t → ∞。最后，通过几个说明性示例说明了所提出方法的有效性。