基于改进的卡尔曼滤波无人机抗欺骗BDS导航方法

An Adaptive Kalman Filter in UAV Anti-Spoofing BDS Navigation

无人机的BDS定位抗欺骗技术关系到无人机作业安全。无人机飞行作业具有高动态性,传统的定位解算不具备识别和处理欺骗信号的能力,并且测速结果会发散。本文针对无人机高动态性带来的定位发散和欺骗识别困难的问题,基于传统卡尔曼滤波定位模型,通过引入M-估计和消减因子来保证定位结果可靠收敛,从而建立了一种改进的卡尔曼滤波定位模型。仿真实验表明,在引入欺骗信号的情况下,基于改进的卡尔曼滤波模型性能稳定,并且具备抗欺骗的能力,可以有效地保证无人机飞行定位的准确性和稳定性。

The anti-deception technology of BDS positioning of UAV is related to the safety of UAV operation.The UAV flying operation is highly dynamic,and the traditional positioning algorithm can not recognize and process the deception signal,and the velocity measurement results will be divergent.In order to solve the problem of localization divergence and deception identification of UAV,this paper introduces M-estimation and reduction factor based on traditional Kalman filter localization model to ensure the reliable convergence of localization results,this creates an improved Kalman filter model.The simulation results show that the improved Kalman filter model is stable and has the ability of anti-deception,which can effectively ensure the accuracy and stability of UAV flight positioning.