通信和测量受限条件下异构多UAV分布式协同目标跟踪方法

研究了通信和测量受限的异构多无人机(UAV)网络化分布式协同目标观测与跟踪问题.该分布式UAV系统采用长机一僚机异构型网络结构,以实现在电子静默和战术隐身条件下扩大探测和打击纵深.提出改进的一致性信息滤波(ICF)算法,实现通信和测量范围内各UAV节点的分布式信息融合.由于一致性算法的收敛性与网络拓扑结构的连通性密切相...

Distributed Cooperation Target Tracking for Heterogeneous Multi-UAV Under Communication and Measurement Constraints

This article presents a study of the network distributed cooperation observation and tracking of heterogeneous multi-unmanned aerial vehicle (UAV) based on local communication and limited detection range. The distributed UAV system adopts a heterogeneous Leader-Follower type network structure for the purpose of enlarging detection and attack depth under electronic silence and tactic invisibility. An improved information consensus filter (ICF) is proposed to achieve distributed data fusion for the UAVs within their communication and measurement range. Because the convergence of the consensus algorithm is relevant to the connectivity of the topology graph, communication connectivity robustness is introduced as an optimal index to solve the distributed filter and control problem of the UAVs under communication and measurement constraints. The Leader acts as the control center, while receding horizon optimization (RHO) is used to obtain the optimal solution, thus controlling the trajectory of each UAV to acquire the best tracking performance. Simulations reveal that the performance of the proposed ICF is equal to that of the centralized information filter (CIF) when communication is strong. Compared with the optimization when ignoring the communication connectivity robustness, this algorithm can dramatically enhance the connectivity of the topology graph, improve the tracking performance and convergence of the consensus algorithm, and enhance the reliability and robustness of the system.