With the development of Internet of Things (IoT), people’s demand for location-based services is increasingly urgent. Based on magnetometer, accelerometer, and rate gyro, i.e., MARG sensors, the position and attitude estimation methods such as complementary filter (CF), Kalman filter (KF), and their various modifications have been the research hot spot. However, the CF-based methods are empirical and lack robustness; the KF-based methods are memory-free observers, whose solution may diverge when the filter lacks uniform observability. In this article, a virtual-measurement-combined extended KF (VMC-EKF) method is proposed by fusing the carrier’s motion state with EKF method. Similar to the graph optimization, the proposed method can measure the key information in VMC phase, and thus remove the requirement of uniform observability. The number of virtual measurements can be estimated based on carrier’s motion state and its gradient, which determines the number of iterations of the prediction phase and the correction phase. In order to verify the performance of the proposed method, a series of numerical simulation experiments, turntable experiments, and foot-mounted experiments are carried out. The corresponding testing platform is set up based on MPU9250, which is a typical and low-cost motion tracking integrated circuit (IC) of MARG sensors. The raw data of MARG sensors can communicate with the host computer via wired or wireless communication, and then be imported into MATLAB for processing and analysis by the compared methods. The test results show that the proposed method can achieve fast convergence of attitude estimation and avoid the divergence of position estimation compared with the state-of-the-art methods.

中文翻译：

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基于MARG传感器的快速鲁棒的位置姿态估计方法

随着物联网（IoT）的发展，人们对基于位置的服务的需求越来越迫切。基于磁力计、加速度计和速率陀螺，即MARG传感器，互补滤波器（CF）、卡尔曼滤波器（KF）等位置姿态估计方法及其各种改进一直是研究热点。然而，基于CF的方法是经验性的，缺乏鲁棒性；基于 KF 的方法是无记忆观测器，当滤波器缺乏统一的可观测性时，其解决方案可能会发散。在本文中，通过将载体运动状态与EKF方法融合，提出了一种虚拟测量组合扩展KF（VMC-EKF）方法。与图优化类似，所提出的方法可以测量VMC阶段的关键信息，从而消除了对均匀可观察性的要求。可以根据载波的运动状态及其梯度估计虚拟测量的次数，这决定了预测阶段和校正阶段的迭代次数。为了验证所提出方法的性能，进行了一系列数值模拟实验、转台实验和脚踏实验。基于MPU9250搭建了相应的测试平台，MPU9250是MARG传感器典型的低成本运动跟踪集成电路（IC）。MARG传感器的原始数据可以通过有线或无线通信方式与上位机进行通信，然后导入MATLAB进行比较方法的处理和分析。