Hierarchical Information Dissemination Method for Unmanned Cluster Targets Allocation

doi:10.12382/bgxb.2022.0515

Abstract

Abstract:

For the construction of a practical constructing cluster intelligence, the basic associations of individual/group cooperations should be clarified to develop the extensible models and methods. At present,the differentiation models are difficultly coalesced or integrated, leading to the intense demand of taking hold of a common basis for uniting behavior consistency and interaction. Based on the theory of complex network information transmission and community structure, a hierarchical information dissemination method for cluster targets allocation is proposed for the decoupling of decision making and communication. Therefore, from a mesoscale perspective of information interaction control in and among dynamic communities, the UAU-FO* model-based hierarchical information transmission method is proposed on the basis of the double-layer coupling system model. The proposed method can be used to construct the extensible association foundation between the individual targets selection and the emergence of group cooperation, which provides a supportability for realizing the autonomous aggregation and deaggregation oriented to targets attending bythe propagation effect of decision making.The simulated results shows that the groups can achieve self-directed organization through independent information perception fusion, inter-layer control and group control strategies after the decoupling of decision and communication. Simultaneously, it is verified that the method has good robustness and continuous stability under weak interconnection conditions.

Key words: unmanned cluster, practical application, complex network, dynamic community, decision propagation, target assignment

CLC Number:

TP319

SHEN Yuting, MENG Xin, GAO Yueqing. Hierarchical Information Dissemination Method for Unmanned Cluster Targets Allocation[J]. Acta Armamentarii, 2023, 44(10): 3006-3025.

Figures/Tables 23

Fig.1 Dynamics analysis of agents in local dynamic cognitive network

Fig.2 Hierarchical system model based on local dynamic cognition

Fig.3 State transition process

Fig.4 Target movement publishing and steady-state observation

Fig.5 Multi-agent observation cluster evolution based on community change

Table 1 Basic observating state of UAU-FO* hierarchical model

状态	含义	详细描述
U-F	无关注自由态	节点对任何目标的信息处于无条件接收的状态,且倾向性指向不明确。
U-O*	无关注联合观测态	仅为理论状态,实际不存在,一旦节点处于对某目标的联合观测态,就将切换至有关注的相关状态,故不做讨论。
A-F	有关注且自由态	可作为实际可能存在的一种特例,尤其在任务协商博弈的多轮次过程中,有关注的自由态将是重点讨论的状态(后续将A-F归为不稳定暂态,假定一旦有了关注,就将由F过渡为O*)。
A-O*	有关注联合观测态	有观测目标,处在观测区或向观测区运动过程中,可能存在目标切换,具有OBS和ORD两种隐式状态模式切换。

Fig.6 Representation of state decomposition of hierarchical joint observations

Fig.7 UAU-FO * hierarchical evolution state transition

Fig.8 State control flow

Fig.9 Periodic state of dynamic central node

Fig.10 Hierarchical state transition based on simplified UAU-FO*

Fig.11 Algorithm flow chart

Table 2 Basic index of benefit-cost evaluation based on statistics of information interactions

评估指标及指标计算
距离代价I_ST,_l: $d l ∑ l = 1 s d l$
交互传播密度I_R,_l: $1 2 (e H o p 1 + e H o p 2)$ (e_Hop1 $C U ([t g t l] H o p 1) C U ([T G T t] H o p 1)$ +e_Hop2 $C U ([t g t l] H o p 2) C U ([T G T t] H o p 2)$ )
信息持久度I_ET,_l: $1 2 (e D + e F)$ (e_D $T D, l S U M (T D)$ +e_F $T F, l S U M (T F)$ ) T_D,_l=t-t_app,_l,T_F,_l=t-t_last,_l
任务匹配收益度I_RWD,_l: $1 2 (e p r i + e p r e f)$ (e_pri $w p r i, l S U M (w p r i)$ + e_pref $w p r e f, l S U M (w p r e f)$ )

Table 2 Basic index of benefit-cost evaluation based on statistics of information interactions

评估指标及指标计算
距离代价I_ST,_l: $d l ∑ l = 1 s d l$
交互传播密度I_R,_l: $1 2 (e H o p 1 + e H o p 2)$ (e_Hop1 $C U ([t g t l] H o p 1) C U ([T G T t] H o p 1)$ +e_Hop2 $C U ([t g t l] H o p 2) C U ([T G T t] H o p 2)$ )
信息持久度I_ET,_l: $1 2 (e D + e F)$ (e_D $T D, l S U M (T D)$ +e_F $T F, l S U M (T F)$ ) T_D,_l=t-t_app,_l,T_F,_l=t-t_last,_l
任务匹配收益度I_RWD,_l: $1 2 (e p r i + e p r e f)$ (e_pri $w p r i, l S U M (w p r i)$ + e_pref $w p r e f, l S U M (w p r e f)$ )

Fig.12 Aggregation-isolation-aging-escape process (LinkCapmax=20)

Fig.13 Aggregation-isolation-aging-escape process (LinkCapmax=5)

Fig.14 Communication connection distribution of cluster (aggregation-isolation)

Fig.15 Communication connection distribution of cluster

Fig.16 Statistics of targets observation group

Fig.17 Statistics of formation process of group (observation group of Target 2)

Fig.18 Statistics of information interaction frequency

Fig.19 Statistics of targets observation group (weak connection condition)

Fig.20 Statistics of formation process of group (for Target 2, weak connection condition)

Fig.21 Statistics of information interaction frequency (weak connection condition)

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