In decades,the battlefield environment is becoming more and more complex with plenty of electronic equipments.Thus,in order to improve the survivability of radar sensors and satisfy the requirement of maneuvering targ...In decades,the battlefield environment is becoming more and more complex with plenty of electronic equipments.Thus,in order to improve the survivability of radar sensors and satisfy the requirement of maneuvering target tracking with a low probability of intercept,a non-myopic scheduling is proposed to minimize the radiation cost with tracking accuracy constraint.At first,the scheduling problem is formulated as a partially observable Markov decision process(POMDP).Then the tracking accuracy and radiation cost over the future finite time horizon are predicted by the posterior carmer-rao lower bound(PCRLB) and the hidden Markov model filter,respectively.Finally,the proposed scheduling is implemented efficiently by utilizing the branch and bound(B&B) pruning algorithm.Simulation results show that the performance of maneuvering target tracking was improved by the improved interacting multiple model(IMM),and the scheduler time and maximum memory consumption were significant reduced by the present B&B pruning algorithm without losing the optimal solution.展开更多
This paper researches the adaptive scheduling problem of multiple electronic support measures(multi-ESM) in a ground moving radar targets tracking application. It is a sequential decision-making problem in uncertain e...This paper researches the adaptive scheduling problem of multiple electronic support measures(multi-ESM) in a ground moving radar targets tracking application. It is a sequential decision-making problem in uncertain environment. For adaptive selection of appropriate ESMs, we generalize an approximate dynamic programming(ADP) framework to the dynamic case. We define the environment model and agent model, respectively. To handle the partially observable challenge, we apply the unsented Kalman filter(UKF) algorithm for belief state estimation. To reduce the computational burden, a simulation-based approach rollout with a redesigned base policy is proposed to approximate the long-term cumulative reward. Meanwhile, Monte Carlo sampling is combined into the rollout to estimate the expectation of the rewards. The experiments indicate that our method outperforms other strategies due to its better performance in larger-scale problems.展开更多
基金supported by the National Defense Pre-research Foundation of China(012015012600A2203)。
文摘In decades,the battlefield environment is becoming more and more complex with plenty of electronic equipments.Thus,in order to improve the survivability of radar sensors and satisfy the requirement of maneuvering target tracking with a low probability of intercept,a non-myopic scheduling is proposed to minimize the radiation cost with tracking accuracy constraint.At first,the scheduling problem is formulated as a partially observable Markov decision process(POMDP).Then the tracking accuracy and radiation cost over the future finite time horizon are predicted by the posterior carmer-rao lower bound(PCRLB) and the hidden Markov model filter,respectively.Finally,the proposed scheduling is implemented efficiently by utilizing the branch and bound(B&B) pruning algorithm.Simulation results show that the performance of maneuvering target tracking was improved by the improved interacting multiple model(IMM),and the scheduler time and maximum memory consumption were significant reduced by the present B&B pruning algorithm without losing the optimal solution.
基金supported by the National Natural Science Foundation of China(6157328561305133)
文摘This paper researches the adaptive scheduling problem of multiple electronic support measures(multi-ESM) in a ground moving radar targets tracking application. It is a sequential decision-making problem in uncertain environment. For adaptive selection of appropriate ESMs, we generalize an approximate dynamic programming(ADP) framework to the dynamic case. We define the environment model and agent model, respectively. To handle the partially observable challenge, we apply the unsented Kalman filter(UKF) algorithm for belief state estimation. To reduce the computational burden, a simulation-based approach rollout with a redesigned base policy is proposed to approximate the long-term cumulative reward. Meanwhile, Monte Carlo sampling is combined into the rollout to estimate the expectation of the rewards. The experiments indicate that our method outperforms other strategies due to its better performance in larger-scale problems.
