To further enhance the positioning accuracy and stability of the INS/GNSS integrated navigation system, we present a new fuzzy strong tracking cubature Kalman filter (FSTCKF) algorithm for data fusion. A fuzzy logic controller is designed for the strong tracking cubature Kalman filter (STCKF), which aims at strengthening the filter’s ability to identify and respond to the dynamics. Chi-square tests are separately conducted on the innovation vector in order to reveal the dynamic properties inside the velocity and position states. Thereafter, parallel fuzzy inferences are conducted to generate a time-varying smoothing factor matrix, which helps the STCKF obtain the multiple fading factors distributed to each state variable. Numerical simulations and real data testing results demonstrate the superiority and robustness of the proposed FSTCKF algorithm. Not only can the proposed algorithm maintain the accuracy and stability in steady conditions, but further increase the dynamic tracking ability as well. Finally, the positioning performances of the INS/GNSS can be improved.

为了进一步提高INS/GNSS组合导航系统的定位精度和稳定性，我们提出了一种新的模糊强跟踪容积卡尔曼滤波器（FSTCKF）数据融合算法。针对强跟踪容积卡尔曼滤波器(STCKF)设计了模糊逻辑控制器，旨在增强滤波器对动态的识别和响应能力。对创新向量分别进行卡方检验，以揭示速度和位置状态内部的动态特性。此后，进行并行模糊推理以生成时变平滑因子矩阵，这有助于 STCKF 获得分布到每个状态变量的多个衰落因子。数值模拟和真实数据测试结果证明了所提出的FSTCKF算法的优越性和鲁棒性。所提出的算法不仅可以在稳定条件下保持精度和稳定性，还可以进一步提高动态跟踪能力。最后，可以提高 INS/GNSS 的定位性能。