A multi-sensor-system cooperative scheduling method for multi-task collaboration is proposed in this paper.We studied the method for application in ground area detection and target tracking.The aim of sensor schedulin...A multi-sensor-system cooperative scheduling method for multi-task collaboration is proposed in this paper.We studied the method for application in ground area detection and target tracking.The aim of sensor scheduling is to select the optimal sensors to complete the assigned combat tasks and obtain the best combat benefits.First,an area detection model was built,and the method of calculating the detection risk was proposed to quantify the detection benefits in scheduling.Then,combining the information on road constraints and the Doppler blind zone,a ground target tracking model was established,in which the posterior Carmér-Rao lower bound was applied to evaluate future tracking accuracy.Finally,an objective function was developed which considers the requirements of detection,tracking,and energy consumption control.By solving the objective function,the optimal sensor-scheduling scheme can be obtained.Simulation results showed that the proposed sensor-scheduling method can select suitable sensors to complete the required combat tasks,and provide good performance in terms of area detection,target tracking,and energy consumption control.展开更多
Multiple optimization objectives are often taken into account during the process of sensor deployment.Aiming at the problem of multi-sensor deployment in complex environment,a novel multi-sensor deployment method base...Multiple optimization objectives are often taken into account during the process of sensor deployment.Aiming at the problem of multi-sensor deployment in complex environment,a novel multi-sensor deployment method based on the multi-objective intelligent search algorithm is proposed.First,the complex terrain is modeled by the multi-attribute grid technology to reduce the computational complexity,and a truncation probability sensing model is presented.Two strategies,the local mutation operation and parameter adaptive operation,are introduced to improve the optimization ability of quantum particle swarm optimization(QPSO)algorithm,and then an improved multi-objective intelligent search algorithm based on QPSO is put forward to get the Pareto optimal front.Then,considering the multi-objective deployment requirements,a novel multi-sensor deployment method based on the multi-objective optimization theory is built.Simulation results show that the proposed method can effectively deal with the problem of multi-sensor deployment and provide more deployment schemes at once.Compared with the traditional algorithms,the Pareto optimal fronts achieved by the improved multi-objective search algorithm perform better on both convergence time and solution diversity aspects.展开更多
基金Project supported by the Defense Pre-research Fund Project of China(No.LJ20191C020393)。
文摘A multi-sensor-system cooperative scheduling method for multi-task collaboration is proposed in this paper.We studied the method for application in ground area detection and target tracking.The aim of sensor scheduling is to select the optimal sensors to complete the assigned combat tasks and obtain the best combat benefits.First,an area detection model was built,and the method of calculating the detection risk was proposed to quantify the detection benefits in scheduling.Then,combining the information on road constraints and the Doppler blind zone,a ground target tracking model was established,in which the posterior Carmér-Rao lower bound was applied to evaluate future tracking accuracy.Finally,an objective function was developed which considers the requirements of detection,tracking,and energy consumption control.By solving the objective function,the optimal sensor-scheduling scheme can be obtained.Simulation results showed that the proposed sensor-scheduling method can select suitable sensors to complete the required combat tasks,and provide good performance in terms of area detection,target tracking,and energy consumption control.
基金This work is also supported by the National Defence Advance Research of China[No.012015012600A2203].
文摘Multiple optimization objectives are often taken into account during the process of sensor deployment.Aiming at the problem of multi-sensor deployment in complex environment,a novel multi-sensor deployment method based on the multi-objective intelligent search algorithm is proposed.First,the complex terrain is modeled by the multi-attribute grid technology to reduce the computational complexity,and a truncation probability sensing model is presented.Two strategies,the local mutation operation and parameter adaptive operation,are introduced to improve the optimization ability of quantum particle swarm optimization(QPSO)algorithm,and then an improved multi-objective intelligent search algorithm based on QPSO is put forward to get the Pareto optimal front.Then,considering the multi-objective deployment requirements,a novel multi-sensor deployment method based on the multi-objective optimization theory is built.Simulation results show that the proposed method can effectively deal with the problem of multi-sensor deployment and provide more deployment schemes at once.Compared with the traditional algorithms,the Pareto optimal fronts achieved by the improved multi-objective search algorithm perform better on both convergence time and solution diversity aspects.
