摘要
在视场互补传感网目标跟踪应用中,当目标跨节点视场时,目标在每个时刻仅能被部分节点观测到并返回目标量测,导致各节点对同一目标的状态估计精度参差不齐,节点间进行自适应一致性信息融合的增益权重失配,使得传感网目标跟踪性能恶化。在现有ACFr滤波算法基础上提出了一种基于置信度的自适应一致性滤波算法(Confidence-based Adaptive Consensus Filter,CB-ACF)。所提算法利用节点对目标可观性的历史统计量,将目标跨节点状态细分为入视场和出视场两种情况,并基于其设计能够反映各节点对目标状态估计精度变化趋势的自适应置信度计算规则,利用节点置信度设计一致性增益权重并对各节点间状态估计值进行自适应一致性滤波融合。仿真验证结果表明,所提算法与经典自适应一致性滤波算法相比,传感网的目标状态估计精度及节点间目标状态估计一致性均有较大提升。
In the application of target tracking in complementary field-of-view sensor networks,when a target crosses the field-of-view of multiple nodes,it can only be observed and return measurement by a subset of nodes at each time step.This leads to disparate accuracy in the state estimation of the same target among different nodes,causing a mismatch in the gain weights for adaptive consensus information fusion between nodes and resulting in degraded target tracking performance in the sensor network.In this paper,we propose a confidence-based adaptive consensus filter algorithm(CB-ACF)based on the existing ACFr algorithm.The proposed algorithm utilizes the historical statistical measures of target observability by nodes to categorize the target s state across nodes into two situations:entering the field-of-view and exiting the field-of-view.It then designs an adaptive confidence calculation rule that reflects the trend of changes in the accuracy of target state estimation by each node.By using node confidence,it designs consistency gain weights and performs adaptive consensus filtering fusion of state estimation values among nodes.Simulation results demonstrate that compared with the prevailing adaptive consensus filter algorithm,the proposed algorithm significantly improves the target state estimation accuracy in the sensor network and enhances the consistency of target state estimation among nodes.
作者
郭尹君
石义芳
GUO Yinjun;SHI Yifang(School of Automation,Hangzhou Dianzi University,Hangzhou Zhejiang 310018,China)
出处
《杭州电子科技大学学报(自然科学版)》
2025年第4期78-87,共10页
Journal of Hangzhou Dianzi University(Natural Sciences)
基金
浙江省教育厅一般科研项目(Y202351779)。
关键词
传感网
目标跟踪
一致性算法
置信度
sensor network
target tracking
consistency algorithm
confidence level
