The reasonable scheduling of multisensor systems to maximize combat benefits has become a research hotspot in the field of sensor management. To minimize the uncertainty in the threat level of targets and improve the survivability of sensors, a risk-based multisensor scheduling method is proposed in this paper. In this scheduling problem, the best sensors are systematically selected to observe targets for the trade-off between the threat assessment risk and the emission risk. First, the scheduling problem is modelled as a partially observable Markov decision process (POMDP) for target threat assessment. Second, the calculation methods of the threat assessment risk and the emission risk are proposed to quantify the potential loss caused by the uncertainty in the threat level of targets and the emission of sensors. Then, a nonmyopic sensor scheduling objective function is built to minimize the total risk which is the weighted sum of the threat assessment risk and the emission risk. Furthermore, to solve the high complexity computational problem in optimization, a decision tree search algorithm based on branch pruning is designed. Finally, simulations are conducted, and the results show that the proposed algorithm can significantly reduce the searching time and memory consumption in optimization compared with those of traditional algorithms, and the proposed method has a better risk control effect than the existing sensor scheduling methods.
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