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.

中文翻译：

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基于创新粒子滤波器的预测偏差，用于在存在GPS欺骗攻击的情况下估计无人机的真实位置

本文提出了一种基于创新粒子滤波（PDIPF）的新型预测偏差算法，以解决全球定位系统（GPS）欺骗攻击存在下的无人机定位问题。形式为未知但有界的错误。为了应对GPS欺骗攻击（系统状态变量的未知突变），在PDIPF算法的两个基本部分（包括粒子加权和协方差矩阵自适应）中自适应地完成了GPS欺骗效果的补偿。此外，开发了一个定理，该定理在自适应协方差矩阵的帮助下，验证输出估计误差是否在给定概率的上限之内。除了，PDIPF中的颗粒重量计算是针对GPS测量中生成的颗粒的预测差异进行的。提出的PDIPF用于减少具有不同概率密度函数的任何GPS欺骗错误的影响，并在存在GPS欺骗攻击的情况下估计无人机的真实位置。该算法应用于惯性导航系统/ GPS / Loran-C集成系统。仿真结果证明了所提出的PDIPF算法在准确性和冗余性方面的有效性。该算法应用于惯性导航系统/ GPS / Loran-C集成系统。仿真结果证明了所提出的PDIPF算法在准确性和冗余性方面的有效性。该算法应用于惯性导航系统/ GPS / Loran-C集成系统。仿真结果证明了所提出的PDIPF算法在准确性和冗余性方面的有效性。