This paper studies a distributed multitarget filtering problem for a sensor network where each sensor can obtain the measurements and system information of some targets while having no knowledge of others. To estimate the states of all targets, a consensus Kalman information filtering algorithm with an adaptive and finite-time matrix-weighted consensus strategy is proposed. When the communication network is strongly connected and the sensing network is time-varying while being always collectively observable, it is proved that if the targets’ system matrices are time invariant, the mean-square estimation errors of the sensors are bounded for any number of consensus iterations. If the targets’ system matrices are time varying and the number of the consensus steps per information filtering is larger than the diameter of the communication topology, the mean-square estimation errors of the sensors are also bounded. When each sensor is intermittently activated to observe the targets and the network does not remain collectively observable, an allowable lower bound of detection probability is derived to guarantee the stochastic boundedness of the estimation errors. Then, the filtering algorithm is applied to design a distributed containment controller for multiple agents to encircle multiple planar heterogeneous dynamic targets. Finally, simulation examples are given to illustrate the effectiveness of the algorithms.

中文翻译：

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基于分布式共识的多目标滤波及其在编队控制中的应用

本文研究了传感器网络的分布式多目标过滤问题，其中每个传感器都可以在不了解其他目标的情况下获得某些目标的测量值和系统信息。为了估计所有目标的状态，提出了一种具有自适应和有限时间矩阵加权共识策略的共识卡尔曼信息过滤算法。当通信网络连接牢固且感测网络随时间变化而始终可以集体观察时，证明了如果目标系统的矩阵是时不变的，则传感器的均方估计误差会限制在任意数量的共识迭代。如果目标系统的矩阵随时间变化，并且每个信息过滤的共识步骤数大于通信拓扑的直径，传感器的均方估计误差也有界。当间歇性地激活每个传感器以观察目标并且网络无法始终保持可观察状态时，将得出检测概率的允许下限，以保证估计误差的随机有界。然后，将过滤算法应用于为多个代理设计一个包围式控制器，以包围多个平面异构动态目标。最后，通过仿真实例说明了算法的有效性。得出检测概率的下限，以保证估计误差的随机有界。然后，将过滤算法应用于为多个代理设计一个包围式控制器，以包围多个平面异构动态目标。最后，通过仿真实例说明了算法的有效性。得出检测概率的下限，以保证估计误差的随机有界。然后，将过滤算法应用于为多个代理设计一个包围式控制器，以包围多个平面异构动态目标。最后，通过仿真实例说明了算法的有效性。