模糊自适应滤波在水下航行器组合导航系统中的应用

Application of fuzzy adaptive filter to integrated navigation system of underwater vehicle

为了提高水下航行器组合导航系统精度和可靠性,针对水下航行器组合导航系统量测噪声统计特性随实际工作环境的不同而变化的特点,提出了基于模糊自适应联邦卡尔曼滤波的水下组合导航算法。通过监测理论残差与实际残差的协方差的一致程度,应用模糊系统不断调整滤波器的增益系数,对子滤波器进行在线自适应调整,从而实现导航状态的最优估计滤波。通过对联邦滤波器信息分配系数模糊自适应调整,减少了滤波计算量,提高了滤波实时性。软件仿真实验结果表明:模糊自适应滤波可以有效地提高水下航行器组合导航系统的精度和可靠性,提高导航滤波实时性,克服传统的滤波算法的缺点与不足。

To improve the navigation precision and stability of the underwater vehicle, a fuzzy adaptive federated Kalman filtering algorithm was proposed according to the statistical feature of the system measurement noise which varied with environments. By monitoring the coincidences of actual residual with the theoretical residual, the filter can be adapted automatically, and optimal filtering results can be obtained. Information sharing coefficients were adaptively adjusted, and the filtering time was decreased sharply and the real-time ability was greatly improved. The simulation experiments demonstrate that the fuzzy adaptive filter can improve the integrated navigation accuracy, stability and the filtering real-time ability, and overcome the shortcomings of the traditional filtering method.