A decision-tree based multiple-model UKF for attitude estimation using low-cost MEMS MARG sensor arrays
A decision-tree based multiple-model UKF for attitude estimation using low-cost MEMS MARG sensor arrays
复制标题
基于决策树的多模型 UKF，使用低成本 MEMS MARG 传感器阵列进行姿态估计
DOI：
10.1016/j.measurement.2018.11.062
复制
发表时间：
2019-03-01
期刊：
影响因子：
5.6
通讯作者：
Li, Yibin
中科院分区：
文献类型：
--
作者：
Xu, Xiaolong;Tian, Xincheng;Li, Yibin
微电子机械系统（MEMS）作为一种低成本、小尺寸、高精度、快速响应的姿态估计系统，在各种应用中得到了广泛的应用。提出了一种新的基于决策树的多模型无迹卡尔曼滤波器（DTMM-UKF）的姿态估计方法。它是一种基于四元数的姿态估计器，融合了相关的捷联式磁、角速率和重力（MARG）传感器阵列。提出了一套新的磁力计和加速度计可靠性检验标准。为了提高抗干扰性能，我们定义了四种不同的滤波器模型的UKF。特别地，建立了一个决策树，根据这些可靠性测试标准自动切换过滤器模型。利用陀螺数据从过程模型中获得先验姿态估计。将加速度计和磁强计数据融合，根据滤波模型确定的目标函数和雅可比矩阵求解观测姿态。在UKF框架下，通过融合先验估计和观测姿态，最终确定最优姿态。实验测试表明，DTMM-UKF算法具有较好的鲁棒性和较高的实时估计精度。(C)2018爱思唯尔有限公司版权所有。
Micro-electronic-mechanical system (MEMS) is widely used in various applications, especially as a lowcost and small size system for attitude estimation which requires high accuracy and fast response. This work proposes a novel decision-tree based multiple-model unscented Kalman filter (DTMM-UKF) for attitude estimation. It is a quaternion-based attitude estimator that fuses related strap-down magnetic, angular rate, and gravity (MARG) sensor arrays. A set of novel criteria for testing whether the magnetometer and accelerometer are reliable is developed. To improve the anti-interference performance, we define four different filter models for the UKF. Particularly, a decision tree is established to automatically switch filter model based on these reliability test criteria. The priori attitude estimation is obtained from the process model using gyroscope data. Fusing the accelerometer and magnetometer data together, the observation attitude could be solved based on corresponding objective function and Jacobian matrix determined by the filter model. Under the UKF frame, the final optimal attitude could be determined by fusing priori estimation and observed attitude. Experimental tests show that the DTMM-UKF algorithm has better robustness and higher real-time estimation accuracy. (C) 2018 Elsevier Ltd. All rights reserved.