Research on Combined Navigation Algorithm Based on Adaptive Interactive Multi-Kalman Filter Modeling
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摘要: 在组合导航系统中,信息融合和定位精度取决于惯性系统和传感器的特性,然而在实际应用中获取先验知识仍然具有挑战性。为解决车辆导航中卫星信号质量的变化及系统非线性降低组合导航系统性能的问题,该文提出一种基于多卡尔曼滤波器的模糊自适应交互式多模型算法(FAIMM-MKF),将基于卫星信号质量的模糊控制器(Fuzzy Controller)与自适应交互多模型(AIMM)相结合,通过组合无迹卡尔曼滤波(UKF)、迭代扩展卡尔曼滤波(IEKF)和平方根容积卡尔曼滤波(SRCKF)3种不同的滤波器,适配车辆动力学模型,并通过车载半实物仿真实验验证该方法的性能。结果表明,在卫星信号质量发生改变的情况下,与传统的交互式多模型算法相比,该方法显著提高了车辆在复杂环境中的定位精度。Abstract: Practical applications struggle to obtain prior knowledge about inertial systems and sensors, affecting information fusion and positioning accuracy in combined navigation systems. To address the degradation of integrated navigation performance due to satellite signal quality changes and system nonlinearity in vehicle navigation, a Fuzzy Adaptive Interactive Multi-Model algorithm based on Multiple Kalman Filters (FAIMM-MKF) is proposed. It integrates a Fuzzy Controller based on satellite signal quality (Fuzzy Controller) and an Adaptive Interactive Multi-Model (AIMM). Improved Kalman filters such as Unscented Kalman Filter (UKF), Iterated Extended Kalman Filter (IEKF), and Square-Root Cubature Kalman Filter (SRCKF) are designed to match vehicle dynamics models. The method’s performance is verified through in-vehicle semi-physical simulation experiments. Results show that the method significantly improves vehicle positioning accuracy in complex environments with varying satellite signal quality compared to traditional interactive multi-model algorithms.
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表 1 模糊规则表
HDOP 模型权重调整因子 卫星拒止 卫星较差 卫星良好 SP SP SP EP MP SP EP SP LP EP SP SP 表 2 传感器误差参数
性能指标 陀螺仪 加速度计 零偏 随机游走 零偏 随机游走 更新频率 参数 5°/h 0.15°/$ \sqrt h $ 0.2 mg 800 ug/$ \sqrt {{\mathrm{Hz}}} $ 125 Hz 表 3 最大误差和标准误差对比
算法 东向速度(m/s) 北向速度(m/s) 纬度误差(m) 经度误差(m) 最大误差 标准差 最大误差 标准差 最大误差 标准差 最大误差 标准差 IMM-UKF 1.080 0 0.048 5 0.347 1 0.055 2 1.151 5 0.395 6 –2.919 1 0.835 6 IMM-SRCKF 0.623 7 0.042 4 0.288 3 0.045 1 0.974 2 0.311 6 –2.439 4 0.705 3 IMM-MKF 0.483 5 0.038 6 0.280 6 0.043 8 0.941 0 0.291 5 –2.193 6 0.678 7 FAIMM-MKF 0.467 1 0.028 4 0.205 3 0.040 2 0.760 8 0.220 1 –1.962 5 0.589 3 表 4 平均绝对误差和均方根误差对比
算法 东向速度(m/s) 北向速度(m/s) 纬度(m) 经度(m) MAE RMSE MAE RMSE MAE RMSE MAE RMSE IMM-UKF 0.030 2 0.055 0 0.038 1 0.059 6 0.309 3 0.397 1 0.568 3 0.873 6 IMM-SRCKF 0.024 9 0.043 0 0.027 9 0.048 8 0.235 2 0.311 7 0.481 5 0.743 3 IMM-MKF 0.024 6 0.039 9 0.026 1 0.047 6 0.190 4 0.303 4 0.440 1 0.729 5 FAIMM-MKF 0.018 3 0.029 1 0.025 6 0.043 6 0.164 5 0.226 9 0.364 0 0.642 0 -
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