We present a comprehensive framework for fusing measurements from multiple and generally placed accelerometers and gyroscopes to perform inertial navigation. Using the angular acceleration provided by the accelerometer array, we show that the numerical integration of the orientation can be done with second-order accuracy, which is more accurate compared to the traditional first-order accuracy that can be achieved when only using the gyroscopes. Since orientation errors are the most significant error source in inertial navigation, improving the orientation estimation reduces the overall navigation error. The practical performance benefit depends on prior knowledge of the inertial sensor array, and therefore we present four different state-space models using different underlying assumptions regarding the orientation modeling. The models are evaluated using a Lie Group Extended Kalman filter through simulations and real-world experiments. We also show how individual accelerometer biases are unobservable and can be replaced by a six-dimensional bias term whose dimension is fixed and independent of the number of accelerometers.
翻译:我们提出了一个全面框架,用于从多级和一般位置的加速计和陀螺仪进行引信测量,以进行惯性导航。我们利用加速计阵列提供的角加速度,显示方向的数值整合可以用二阶精确度进行,这比只有使用陀螺仪才能达到的传统一阶精确度更准确。由于方向错误是惯性导航中最重要的错误源,改进方向估计减少了总体导航错误。实际性能效益取决于先前对惯性传感器阵列的了解,因此我们采用关于定向模型的不同基本假设,提出了四种不同的状态空间模型。这些模型是通过模拟和现实世界实验,使用利奥集团扩展的卡尔曼过滤器进行评估的。我们还表明个人加速仪偏差如何不易观察,并且可以由六维偏差取而代之,六维偏差的尺寸是固定的,独立于加速计数。