多传感器信息融合理论在无人机相对导航中的应用

为提升无人机的作战效能和作战指标，提升无人机的相对导航精度和导航系统可靠性，以无人机编队的相对导航系统为研究背景，基于容积卡尔曼滤波算法和信息滤波算法，研究容积信息滤波算法。此外，还采用多传感器信息融合理论，利用分布式信息融合结构构建了无人机相对导航滤波器，对来自惯导、视觉和卫星的信息进行融合，获取无人机间的相对位置、速度和姿态信息。该方法提升了无人机相对导航的导航精度、导航可靠性和滤波稳定性，容积信息滤波算法的应用避免了传统滤波算法在高维系统中出现的数值不稳定以及精度降低等问题。数学仿真结果表明，该方法提高了无人机编队之间相对导航的精度和可靠性，证明了算法的有效性。
In order to improve the operational effectiveness and operational indicators of the UAV (unmanned aerial vehicle), and to enhance the accuracy and reliability of the UAV relative navigation system, a novel relative navigation method was proposed. Under the background of relative navigation system, the cubature information filter based on the cubature Kalman filter and information filter was studied. Moreover, an INS/GPS/VisNav relative navigation filter was designed by making use of the multi-sensor information fusion theory and distributed information fusion structure to fuse the information from INS, VisNav and GPS, and then the relative position, velocity and attitude were obtained. By making use of this algorithm, the accuracy, reliability and stability of the reliability navigation system were all improved. In addition, the accuracy decrease and numerical instability which often occur to traditional filter were avoided by cubature information filter. Mathematical simulation results indicate that the method can improve the accuracy and reliability of the UAV relative navigation system, and the proposed algorithm is verified.