一种贝叶斯目标选择算法

A Bayesian Target Selection Algorithm

为了解决多假目标干扰环境下的舰艇编队目标选择问题,在线性高斯假设下,建立了编队目标运动模型和量测模型,并基于贝叶斯方法推导了复合假设后验概率,提出了一种反舰导弹目标选择算法。该算法充分利用了导弹导航位置误差、编队机动和队形等信息,并且适用于量测噪声方差不同的实际情况。同时,为了降低算法的计算复杂度,设计了基于队形的预滤波器,大大削减了复合假设数量。算例仿真表明,该算法具有优良的性能,显著提高了编队目标正确选择概率。

To solve the problem of selecting a target from naval ships formation under jamming environment full of decoy targets, an anti-ship missile target selection algorithm is put forward, in which the motion of naval ships formation model and the measurement model are constructed under the linear-Gaussian hypothesis and the posterior probability of composite hypotheses is derived based on the Bayesian method. The algorithm makes use of the information of the navigation errors of position of anti-ship missile, the pattern and the bulk motion of naval ships formation. It applies to the actual problem with different variances of measurement noise, To depress the computational complexity of the algorithm, a pre-filter based on the pattern of naval ships formation is designed and the total number of composite hypotheses is reduced dramatically. Simulation results show that the algorithm is excellent and the right probability of selecting a target from naval ships formation is improved observably.