A security issue with multi-sensor unmanned aerial vehicle(UAV)cyber physical systems(CPS)from the viewpoint of a false data injection(FDI)attacker is investigated in this paper.The FDI attacker can employ attacks on ...A security issue with multi-sensor unmanned aerial vehicle(UAV)cyber physical systems(CPS)from the viewpoint of a false data injection(FDI)attacker is investigated in this paper.The FDI attacker can employ attacks on feedback and feed-forward channels simultaneously with limited resource.The attacker aims at degrading the UAV CPS's estimation performance to the max while keeping stealthiness characterized by the Kullback-Leibler(K-L)divergence.The attacker is resource limited which can only attack part of sensors,and the attacked sensor as well as specific forms of attack signals at each instant should be considered by the attacker.Also,the sensor selection principle is investigated with respect to time invariant attack covariances.Additionally,the optimal switching attack strategies in regard to time variant attack covariances are modeled as a multi-agent Markov decision process(MDP)with hybrid discrete-continuous action space.Then,the multi-agent MDP is solved by utilizing the deep Multi-agent parameterized Q-networks(MAPQN)method.Ultimately,a quadrotor near hover system is used to validate the effectiveness of the results in the simulation section.展开更多
针对传统恒虚警(Constant False-Alarm Rate,CFAR)检测器在非均匀噪声环境下检测性能较差的问题,本文提出了一种基于排序的自动剔除Switching-CFAR(Automatic Censoring Switching-CFAR Detector Based on Sorting,ACS-CFAR)检测器.选...针对传统恒虚警(Constant False-Alarm Rate,CFAR)检测器在非均匀噪声环境下检测性能较差的问题,本文提出了一种基于排序的自动剔除Switching-CFAR(Automatic Censoring Switching-CFAR Detector Based on Sorting,ACS-CFAR)检测器.选择参考窗中间单元为测试单元,其余单元按照幅值升序排列,根据两个分界点位置参数,选择合适的参考单元集进行背景噪声功率估计以及结合参考单元数和目标恒虚警率计算相关系数,得到最优检测门限.经过仿真对比,ACS-CFAR检测器在均匀噪声环境下检测率为98.73%,接近于单元平均恒虚警(CA-CFAR)检测器;在非均匀噪声环境下检测率为98.16%,优于可变索引恒虚警(VI-CFAR)和自动删除平均恒虚警(ACCA-CFAR)检测器,虚警率误差均控制在0.10%以内.结果表明,本文提出的ACS-CFAR检测器在均匀噪声环境以及杂波和多目标干扰环境下均具有较好的检测性能.展开更多
文摘A security issue with multi-sensor unmanned aerial vehicle(UAV)cyber physical systems(CPS)from the viewpoint of a false data injection(FDI)attacker is investigated in this paper.The FDI attacker can employ attacks on feedback and feed-forward channels simultaneously with limited resource.The attacker aims at degrading the UAV CPS's estimation performance to the max while keeping stealthiness characterized by the Kullback-Leibler(K-L)divergence.The attacker is resource limited which can only attack part of sensors,and the attacked sensor as well as specific forms of attack signals at each instant should be considered by the attacker.Also,the sensor selection principle is investigated with respect to time invariant attack covariances.Additionally,the optimal switching attack strategies in regard to time variant attack covariances are modeled as a multi-agent Markov decision process(MDP)with hybrid discrete-continuous action space.Then,the multi-agent MDP is solved by utilizing the deep Multi-agent parameterized Q-networks(MAPQN)method.Ultimately,a quadrotor near hover system is used to validate the effectiveness of the results in the simulation section.
文摘针对传统恒虚警(Constant False-Alarm Rate,CFAR)检测器在非均匀噪声环境下检测性能较差的问题,本文提出了一种基于排序的自动剔除Switching-CFAR(Automatic Censoring Switching-CFAR Detector Based on Sorting,ACS-CFAR)检测器.选择参考窗中间单元为测试单元,其余单元按照幅值升序排列,根据两个分界点位置参数,选择合适的参考单元集进行背景噪声功率估计以及结合参考单元数和目标恒虚警率计算相关系数,得到最优检测门限.经过仿真对比,ACS-CFAR检测器在均匀噪声环境下检测率为98.73%,接近于单元平均恒虚警(CA-CFAR)检测器;在非均匀噪声环境下检测率为98.16%,优于可变索引恒虚警(VI-CFAR)和自动删除平均恒虚警(ACCA-CFAR)检测器,虚警率误差均控制在0.10%以内.结果表明,本文提出的ACS-CFAR检测器在均匀噪声环境以及杂波和多目标干扰环境下均具有较好的检测性能.
