Sung Kwangjae
Department of Software, Sangmyung University, Cheonan-si 31066, Republic of Korea.
Sensors (Basel). 2023 Aug 2;23(15):6870. doi: 10.3390/s23156870.
Due to the unavailability of GPS indoors, various indoor pedestrian positioning approaches have been designed to estimate the position of the user leveraging sensory data measured from inertial measurement units (IMUs) and wireless signal receivers, such as pedestrian dead reckoning (PDR) and received signal strength (RSS) fingerprinting. This study is similar to the previous study in that it estimates the user position by fusing noisy positional information obtained from the PDR and RSS fingerprinting using the Bayes filter in the indoor pedestrian positioning system. However, this study differs from the previous study in that it uses an enhanced state estimation approach based on the ensemble transform Kalman filter (ETKF), called QETKF, as the Bayes filer for the indoor pedestrian positioning instead of the SKPF proposed in the previous study. The QETKF estimates the updated user position by fusing the predicted position by the PDR and the positional measurement estimated by the RSS fingerprinting scheme using the ensemble transformation, whereas the SKPF calculates the updated user position by fusing them using both the unscented transformation (UT) of UKF and the weighting method of PF. In the field of Earth science, the ETKF has been widely used to estimate the state of the atmospheric and ocean models. However, the ETKF algorithm does not consider the model error in the state prediction model; that is, it assumes a perfect model without any model errors. Hence, the error covariance estimated by the ETKF can be systematically underestimated, thereby yielding inaccurate state estimation results due to underweighted observations. The QETKF proposed in this paper is an efficient approach to implementing the ETKF applied to the indoor pedestrian localization system that should consider the model error. Unlike the ETKF, the QETKF can avoid the systematic underestimation of the error covariance by considering the model error in the state prediction model. The main goal of this study is to investigate the feasibility of the pedestrian position estimation for the QETKF in the indoor localization system that uses the PDR and RSS fingerprinting. Pedestrian positioning experiments performed using the indoor localization system implemented on the smartphone in a campus building show that the QETKF can offer more accurate positioning results than the ETKF and other ensemble-based Kalman filters (EBKFs). This indicates that the QETKF has great potential in performing better position estimation with more accurately estimated error covariances for the indoor pedestrian localization system.
由于室内无法使用全球定位系统（GPS），人们设计了各种室内行人定位方法，利用从惯性测量单元（IMU）和无线信号接收器测量的传感数据来估计用户位置，如行人航位推算（PDR）和接收信号强度（RSS）指纹识别。本研究与之前的研究类似，都是在室内行人定位系统中使用贝叶斯滤波器，通过融合从PDR和RSS指纹识别获得的有噪声的位置信息来估计用户位置。然而，本研究与之前的研究不同之处在于，它使用一种基于集合变换卡尔曼滤波器（ETKF）的增强状态估计方法，即QETKF，作为室内行人定位的贝叶斯滤波器，而不是之前研究中提出的SKPF。QETKF通过使用集合变换融合PDR预测的位置和RSS指纹识别方案估计的位置测量值来估计更新后的用户位置，而SKPF则通过使用UKF的无迹变换（UT）和PF的加权方法融合它们来计算更新后的用户位置。在地球科学领域，ETKF已被广泛用于估计大气和海洋模型的状态。然而，ETKF算法没有考虑状态预测模型中的模型误差；也就是说，它假设模型是完美的，没有任何模型误差。因此，ETKF估计的误差协方差可能会被系统地低估，从而由于观测值权重不足而产生不准确的状态估计结果。本文提出的QETKF是一种将ETKF应用于应考虑模型误差的室内行人定位系统的有效方法。与ETKF不同，QETKF可以通过考虑状态预测模型中的模型误差来避免误差协方差的系统低估。本研究的主要目标是研究在使用PDR和RSS指纹识别的室内定位系统中，QETKF进行行人位置估计的可行性。在校园建筑中使用智能手机实现的室内定位系统进行的行人定位实验表明，QETKF比ETKF和其他基于集合的卡尔曼滤波器（EBKF）能提供更准确的定位结果。这表明QETKF在为室内行人定位系统提供更准确估计的误差协方差从而实现更好的位置估计方面具有巨大潜力。
