A multisensor scheduling algorithm based on the hybrid task decomposition and modified binary particle swarm optimization (MBPSO) is proposed. Firstly, aiming at the complex relationship between sensor resources and tasks, a hybrid task decomposition method is presented, and the resource scheduling problem is decomposed into subtasks; then the sensor resource scheduling problem is changed into the match problem of sensors and subtasks. Secondly, the resource match optimization model based on the sensor resources and tasks is established, which considers several factors, such as the target priority, detecting benefit, handover times, and resource load. Finally, MBPSO algorithm is proposed to solve the match optimization model effectively, which is based on the improved updating means of particle’s velocity and position through the doubt factor and modified Sigmoid function. The experimental results show that the proposed algorithm is better in terms of convergence velocity, searching capability, solution accuracy, and efficiency.
It is important and difficult for anti-missile battle to use the advantage of resource complementarity of multidimensional sensor platform for collaborative detection and trace. In order to improve the efficiency of mission planning of sensor, for the heterogeneity of the observed resources and the phase and dynamics of missions, the paper introduces the concept of task community, analyzes and establishes heterogeneous MAS multi-sensor task planning system and solution mechanism of problems, based on which the paper focuses on establishing multi-sensor task planning sequence generation model based on cycle-event. And the paper proposes an improved particle swarm optimization algorithm. Simulation experiments indicate that the mechanism established in the paper is rational and effective, and is better than the multisensor task planning effect under traditional mode.
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