面向空中目标威胁评估的多传感器管理方法

为了降低在空中目标威胁评估任务中由于威胁评估结果的不准确性和传感器辐射所带来的潜在损失，提出了一种基于风险的多传感器管理方法。首先，基于部分可观马尔可夫决策过程建立了传感器管理模型；然后，给出了基于信息状态的威胁评估风险和传感器辐射风险的预测方法以量化潜在损失；接着，为获得更优的作战收益，以多步风险预测值为决策依据，以两种风险的加权和最小为优化目标建立了长期目标函数；最后，在求解目标函数时，将传感器管理问题转化为决策树搜索，设计了一种基于分支定界的标准代价搜索算法以快速获得高质量的管理方案。仿真实验表明，所提算法能够在搜索到高质量解的同时大幅减少计算时间和内存消耗；所提方法能够对风险进行准确预测，且相比于经典的传感器管理方法，所提方法具有更好的风险控制效果。

Multi-sensor management approach for aerial target threat assessment

To reduce the potential losses caused by the inaccuracy of threat assessment and sensor radiation in the process of aerial target threat assessment, a risk-based multi-sensor management approach is proposed in this paper. First, a sensor management model based on partially observable Markov decision process is built. Second, the belief-state-based prediction methods for threat assessment risk and radiation risk are proposed to quantify the potential losses. Then, a non-myopic objective function based on multi-step risk prediction value is built and the objective is to obtain the minimal sum of threat assessment risk and radiation risk. Furthermore, to efficiently obtain the optimal solution, the sensor management problem is transformed into a decision tree search problem, and a branch-and-bound-based uniform cost search algorithm is designed. The simulation results show that the proposed algorithm can find high-quality solution while greatly reducing the computational time and memory consumption compared with the classical algorithms. The proposed management approach can accurately predict the risk, and has better risk control effect compared with the existing sensor management methods.