摘要
为了有效跟踪杂波环境下机动目标、降低系统辐射风险,提出一种多平台主动与被动传感器协同跟踪的长时调度方法。将长时调度问题构建为部分可观马尔可夫决策过程,采用交互式多模型概率数据关联算法更新目标信念状态,利用后验克拉美-罗下界预测机动目标长时跟踪精度,提出改进的维特比算法以求解最优调度序列。仿真实验结果表明,所提搜索算法能够显著降低搜索空间和存储空间,所提长时调度方法能够有效控制系统辐射代价、克服传感器频繁切换。
A non-myopic scheduling algorithm of multi-platform active/passive sensors for collaboration tracking is proposed in order to track the maneuvering target in clutter and reduce the system emission risk.The non-myopic scheduling problem is formulated as a partially observable Markov decision process.The target belief state is updated by using the interactive multi-model and the probability data association algorithm,and the posterior Carmér-Rao lower bound is utilized to predict the non-myopic maneuvering target track accuracy.An improved Viterbi algorithm is proposed to search the optimal scheduling sequence.Simulated results show that the proposed search algorithm can be used to reduce the searching space and memory space,control the system emission cost and reduce the excessive sensor switching effectively.
作者
乔成林
单甘霖
段修生
郭峰
QIAO Chenglin;SHAN Ganlin;DUAN Xiusheng;GUO Feng(Department of Electronic and Optical Engineering,Shijiazhuang Campus, Army Engineering University,Shijiazhuang 050003, Hebei, China;School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, Hebei, China;Beijing Aerospace Control Center, Beijing 100094, China)
出处
《兵工学报》
EI
CAS
CSCD
北大核心
2019年第1期115-123,共9页
Acta Armamentarii
基金
武器装备预先研究项目(012015012600A2203)
关键词
传感器长时调度
部分可观马尔可夫决策过程
交互式多模型
概率数据关联
后验克拉美-罗下界
维特比算法
non-myopic sensor scheduling
partially observable Markov decision process
interactive multi-model
probability data association
posterior Carmér-Rao lower bound
Viterbi algorithm
