Huang Yuansheng, Cao Bo, Wang Ao
School of Art Design, Zhejiang Guangsha Vocational and Technical University of Construction, Dongyang, 322100, China.
School of Mechanical Engineering, Anhui Science and Technology University, Chuzhou, 233100, China.
Sci Rep. 2024 Oct 12;14(1):23895. doi: 10.1038/s41598-024-74773-y.
Accurate indoor positioning is the key to the development of the Internet of Things and intelligent devices. In view of GPS-denied indoor environments, we propose to build the indoor local positioning system by using ultra-wide band (UWB) system. In order to enhance the localization accuracy of UWB system, we propose a novel algorithm which integrates the Maximum Correntropy Criterion (MCC) and unscented Kalman filter (UKF) method to reconstruct the measurement distance by using the maximum entropy principle to reduce the influence of outliers and unknown process noise on the smooth effect. Subsequently, the least square (LS) method is implemented to attain the target node (TN) initial position coordinates, and the Taylor algorithm is then performed to further optimize the localization results of the LS method. Lastly, the experimental investigation is conducted to assess the effectiveness and applicability of the developed method via the UWB system in indoor scenarios. The experimental outcomes demonstrate that the developed MCCUKF-LS method can achieve the lowest root mean square error (RMSE), and enhance the positioning accuracy of the TN compared with the LS, KF-LS, and UKF-LS methods. The overall average RMSE of MCCUKF-LS method is reduced by 45.7% contracted with the LS algorithm. The average error of x-, y- and z-axis orientation for the LS method is reduced from 0.074 m, 0.067 m, 0.098 m to 0.036 m, 0.034 m, 0.044 m, and the achieved accuracy in the orientation of the three axes is increased by 51.4%, 49.3% and 55.1% respectively, which reveals that the designed fusion technique is capable of enhancing the positioning accuracy of the TN effectively, providing a new positioning methodology and reference for indoor positioning in GPS-denied environments.
精确的室内定位是物联网和智能设备发展的关键。鉴于存在GPS信号受限的室内环境，我们建议利用超宽带（UWB）系统构建室内定位系统。为了提高UWB系统的定位精度，我们提出了一种新颖的算法，该算法将最大相关熵准则（MCC）和无迹卡尔曼滤波器（UKF）方法相结合，利用最大熵原理重构测量距离，以减少异常值和未知过程噪声对平滑效果的影响。随后，采用最小二乘法（LS）获得目标节点（TN）的初始位置坐标，然后执行泰勒算法进一步优化LS方法的定位结果。最后，进行实验研究，以评估所开发方法在室内场景中通过UWB系统的有效性和适用性。实验结果表明，所开发的MCCUKF-LS方法能够实现最低的均方根误差（RMSE），与LS、KF-LS和UKF-LS方法相比，提高了TN的定位精度。与LS算法相比，MCCUKF-LS方法的总体平均RMSE降低了45.7%。LS方法在x、y和z轴方向的平均误差从0.074 m、0.067 m、0.098 m分别降至0.036 m、0.034 m、0.0４4 m，三个轴方向的定位精度分别提高了51.4%、49.3%和55.1%，这表明所设计的融合技术能够有效地提高TN的定位精度，为GPS信号受限环境下的室内定位提供了一种新的定位方法和参考。
