A Heading Estimation Algorithm for Wrist Device Assisted by Sequential Geomagnetic Observations
A Heading Estimation Algorithm for Wrist Device Assisted by Sequential Geomagnetic Observations
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DOI：
10.1109/jsen.2021.3098847
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发表时间：
2022-03-15
影响因子：
4.3
通讯作者：
Zhang, Weicheng
中科院分区：
文献类型：
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作者：
Tian, Ye;Peng, Ao;Zhang, Weicheng
基于可穿戴设备的行人定位是姿态跟踪的重要应用。它实时跟踪设备的姿态（其中最重要的部分是航向角），并根据行人死角估计（PDR）提供的步长信息为用户提供定位服务，这是目前一种廉价、高效的定位方法。然而，在一系列定位方法中，跟踪的联合估计是由一系列基于重力方向和地球磁场方向的方法给出的。针对重力加速度计在行走过程中由于人体摆臂姿势和前进速度均匀而产生的复杂运动噪声较大的重力测量问题，提出了一种基于多状态约束的卡尔曼滤波和低成本传感器的估计方法，通过对磁场的序次观测来实现重力加速度计的估计。与其他相关工作相比，本文提出的方法消除了对重力方向的依赖，避免了运动状态下额外线加速度带来的重噪声的影响，减少了单独使用磁场观测时观测不足的影响。通过实际实验结果对该方法的性能进行了评价。
Pedestrian positioning with wearable devices is a significant application of attitude tracking. It tracks the attitude (with heading angle being the most important part) of the device in real time and provides positioning services for users based on the information of step length provided by Pedestrian Dead-Reckon (PDR), which is a cheap and efficient positioning method at present. However, amid a train of positioning methods, the joint estimate of tracking is given by a train of methods based on the direction of gravity and the earths magnetic field direction. Considering the measurement of gravity that the gravity accelerometer is exposed to heavy noise due to the complex movement of human body during walking with uniform swing arm posture and forward speed, this paper proposed a novel estimate method based on the Kalman filter with multi-state constraints and the usage of low-cost sensors, which fulfills the estimation with the sequential observation of magnetic field. Compared with other related work, this method proposed in this paper eliminates the dependence on gravity direction, avoiding the influence of heavy noise caused by additional linear acceleration in motion state, and reduces the influence of insufficient observation when using magnetic field observation alone. The performance of the proposed method is evaluated by real-world experimentation results.