置信检验自适应联邦卡尔曼滤波及其水下机器人组合导航应用

Confidence check-adaptive federated Kalman filter and its application in underwater vehicle integrated navigation

    摘要
  • 摘要:
      目的  为解决载体受到扰动时组合导航精度下降的问题，提出一种基于置信检验自适应联邦卡尔曼滤波（CC-AFKF）框架。
      方法  首先，将电子罗盘（EC）、全球定位系统（GPS）与惯性导航系统（INS）相结合；其次，构建置信检验模型，有效滤除INS/GPS和INS/EC子系统中低置信度的量测值，保证量测值的准确性；最后，提出INS/GPS和INS/EC系统自适应调节因子策略，有效调整更新过程中系统噪声协方差。
      结果  通过对具备INS/GPS/EC组合导航系统的水下机器人开展大量相关试验，结果表明，CC-AFKF算法相较于典型的卡尔曼滤波（KF）和联邦卡尔曼滤波（FKF）算法在位置和速度的融合精度上均能至少提高29%。
      结论  研究成果可为松耦合组合导航系统的研究提供相应的方向和思路。

     

    Abstract:
      Objectives  In order to solve the problem of the reduced accuracy of integrated navigation when a carrier is disturbed, a confidence check-adaptive federated Kalman filter (CC-AFKF) framework is proposed.
      Methods  First, the electronic compass (EC), global positioning system (GPS) and inertial navigation system (INS) are combined. Second, a confidence check model is constructed to effectively filter out low-confidence measurements in the INS/GPS and INS/EC subsystems, and ensure the accuracy of the measured value. Finally, an adaptive adjustment factor strategy for the INS/GPS and INS/EC systems is proposed to effectively adjust system noise covariance during the update process.
      Results  A large number of related tests are carried out through an underwater vehicle equipped with INS/GPS/EC integrated navigation systems. The test results show that the CC-AFKF algorithm proposed in this paper can improve the integrated accuracy of position and velocity by at least 29% compared with typical KF and FKF algorithms.
      Conclusions  The results of this study can provide corresponding directions and ideas for research on loosely coupled integrated navigation systems.

     

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