Estimation of the system state is an important task in the field of control, localization, and navigation. Many sensors are used to extract the attitude information. GPS (Global Positioning System) is widely used to provide position and velocity for ground and aerial vehicles. However, only a few research works have addressed the problem of attitude estimation using multiple GPS receivers. The main contribution of this paper consists on proposing a robust algorithm for attitude estimation of an unmanned aerial vehicle using INS/³GPS integration. First, we propose two alternative methods for attitude determination with three GPS receivers: Direct Computing Method (DCM) and Least Square Method (LSM) based on Singular Value Decomposition (SVD). Second, the attitude given by GPS receivers is fused with the measurement of a three-axis gyroscope using a robust Smooth Variable Structure Filter (SVSF). The optimal geometric configuration of GPS antennas is determined based on a deep performance analysis of the proposed system. Then, robustness analysis is assessed against measurement noise and parameters uncertainties. Simulation results are presented to demonstrate the robustness of the proposed approach. A more realistic evaluation of the proposed solution is obtained using an aerial vehicle type “Helicopter” from the Virtual Robot Experimentation Platform (VREP simulator). The proposed filter is compared with an Extended Kalman Filter (EKF). The obtained results have confirmed how much the SVSF is robust for attitude estimation in presence of the considered disturbances.

Estimation of the system state is an important task in the field of control, localization, and navigation. Many sensors are used to extract the attitude information. GPS (Global Positioning System) is widely used to provide position and velocity for ground and aerial vehicles. However, only a few research works have addressed the problem of attitude estimation using multiple GPS receivers. The main contribution of this paper consists on proposing a robust algorithm for attitude estimation of an unmanned aerial vehicle using INS/³GPS integration. First, we propose two alternative methods for attitude determination with three GPS receivers: Direct Computing Method (DCM) and Least Square Method (LSM) based on Singular Value Decomposition (SVD). Second, the attitude given by GPS receivers is fused with the measurement of a three-axis gyroscope using a robust Smooth Variable Structure Filter (SVSF). The optimal geometric configuration of GPS antennas is determined based on a deep performance analysis of the proposed system. Then, robustness analysis is assessed against measurement noise and parameters uncertainties. Simulation results are presented to demonstrate the robustness of the proposed approach. A more realistic evaluation of the proposed solution is obtained using an aerial vehicle type “Helicopter” from the Virtual Robot Experimentation Platform (VREP simulator). The proposed filter is compared with an Extended Kalman Filter (EKF). The obtained results have confirmed how much the SVSF is robust for attitude estimation in presence of the considered disturbances.

系统状态估计是控制、定位和导航领域的一项重要任务。许多传感器用于提取姿态信息。GPS（全球定位系统）广泛用于为地面和飞行器提供位置和速度。然而，只有少数研究工作解决了使用多个 GPS 接收器进行姿态估计的问题。^{本文的主要贡献在于提出了一种使用 INS/ 3}的无人机姿态估计鲁棒算法GPS 集成。首先，我们提出了两种使用三个 GPS 接收器进行姿态确定的替代方法：直接计算方法 (DCM) 和基于奇异值分解 (SVD) 的最小二乘法 (LSM)。其次，GPS 接收器给出的姿态与使用稳健的平滑可变结构滤波器 (SVSF) 的三轴陀螺仪测量相融合。GPS 天线的最佳几何配置是基于对所提出系统的深入性能分析确定的。然后，针对测量噪声和参数不确定性评估稳健性分析。仿真结果展示了所提出方法的鲁棒性。使用来自虚拟机器人实验平台（VREP 模拟器）的飞行器类型“直升机”对所提出的解决方案进行更真实的评估。将所提出的滤波器与扩展卡尔曼滤波器 (EKF) 进行比较。所获得的结果证实了 SVSF 在存在所考虑的干扰的情况下对于姿态估计的鲁棒性有多大。