Prediction-discrepancy based on innovative particle filter for estimating UAV true position in the presence of the GPS spoofing attacks
Prediction-discrepancy based on innovative particle filter for estimating UAV true position in the presence of the GPS spoofing attacks
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DOI：
10.1049/iet-rsn.2019.0520
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发表时间：
2020-06-01
影响因子：
1.7
通讯作者：
Khaloozadeh, Hamid
中科院分区：
文献类型：
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作者：
Majidi, Mohammad;Erfanian, Alireza;Khaloozadeh, Hamid
针对无人机在全球定位系统(GPS)欺骗攻击下的定位问题，假设GPS欺骗效应为未知但有界误差的形式，提出了一种新的基于预测偏差的粒子滤波算法(PDIPF)。针对系统状态变量未知突变时的GPS欺骗攻击，在PDIPF算法的粒子加权和协方差矩阵自适应两个基本部分中，自适应地对GPS欺骗效应进行补偿。此外，利用自适应协方差矩阵证明了输出估计误差是以给定概率为上界的。此外，针对GPS测量生成的颗粒物的预测偏差，对PDIPF中的颗粒物重量进行了计算。提出的PDIPF用于减小不同概率密度函数的GPS欺骗误差的影响，并在存在GPS欺骗攻击的情况下估计无人机的真实位置。将该算法应用于惯性导航系统/GPS/罗兰-C组合系统。仿真结果表明，该算法在精确度和冗余度方面是有效的。
In this paper, a novel prediction-discrepancy based on innovative particle filter (PDIPF) is proposed to solve the unmanned aerial vehicle (UAV) positioning problem in the presence of the global positioning system (GPS) spoofing attack, supposing that the GPS spoofing effects are in the form of unknown but bounded errors. To cope with the GPS spoofing attacks as unknown sudden changes of system state variables, the compensation of the GPS spoofing effects is adaptively done in two basic parts of PDIPF algorithm including particle weighting and covariance matrix adaption. In addition, a theorem is developed which verifies that the output estimation error is upper bounded by a given probability with the help of the adapted covariance matrix. Besides, the particle weight calculation in PDIPF is done with respect to the prediction discrepancy of generated particles from the GPS measurements. The proposed PDIPF is used to decrease the effects of any GPS spoofing errors with different probability density functions and estimate true position of UAV in the presence of the GPS spoofing attacks. The algorithm is applied to the inertial navigation system/GPS/Loran-C integration systems. Simulation results demonstrate the effectiveness of the proposed PDIPF algorithm in terms of accuracy and redundancy.