基于标签多伯努利跟踪器的对手风险动态评估方法

Dynamic Adversarial Risk Estimation Based on Labeled Multi-Bernoulli Tracker

在诸多的军事和民用领域都存在对手目标蓄意入侵我方重要区域从事恶意伤害活动的场景。对手风险评估是基于我方传感器获取的量测数据,在线评估和预测对手行动对我方资产造成的潜在伤害和损失。为了评估随机且动态变化的对手风险,该文提出一种基于标签多伯努利(LMB)跟踪器的统计对手风险动态评估方法。首先,在LMB跟踪器的框架下,基于加性模型和乘性模型,分别推导了统计对手风险最小均方误差估计的表达式。其次,针对所涉及的非线性函数积分问题,结合混合高斯近似和抽样近似方法,提出统计对手风险最小均方误差估计的数值计算方法;最后,将统计对手风险估计方法与LMB跟踪器的迭代过程有机结合,可实现入侵的多目标对我方重要资产期望损失的动态在线评估。模拟多个具有杀伤能力的目标攻击我方雷达阵地的场景,利用雷达获取的实时点迹量测数据,验证了提出算法的有效性和性能优势。

In many military and civilian areas,there exists a scenario in which multiple intruders from an adversary attempt to enter important region of our own to carry out intentional malign activity.Adversarial Risk(AR)estimation is used to assess and predict the expected damage to our valuable assets from the actions of online adversaries based on measurements performed by sensors.To evaluate random and time-varying AR,this study proposes a stochastic AR estimation approach based on a Labeled Multi-Bernoulli(LMB)tracker.First,in the formulation of LMB filtering,expressions of the minimum mean squared error estimation of the stochastic AR are derived for the additive and multiplying model.Second,by combining the Gaussian mixture and sampling approximations,we devise a numerical calculation approach for the proposed AR estimations.Third,we achieve an online evaluation of the expected damage to our valuable assets from the adversary by embedding the proposed AR estimation and LMB filtering.The effectiveness and performance advantage of the proposed estimation algorithms are verified using measurements from radars,considering a simulated scenario wherein multiple lethal targets hit the radar positions.