针对Wi-Fi信号与行人航位推算（pedestrian dead reckoning, PDR）的室内无迹卡尔曼滤波（unscented Kalman filter, UKF）融合定位算法中，Wi-Fi信号易受环境波动、PDR系统存在累计误差以及UKF无法传递加性的非线性过程噪声协方差的问题，提出了一种改进Sage-Husa滤波的新息增广无迹卡尔曼滤波（innovation augmented unscented Kalman filter, IAUKF）融合算法。Sage-Husa滤波通过使用新息向量实时调整来监督协方差值，进而提升了算法对于信号奇异值的抗干扰性。增广形式滤波在保证高阶信息能被传递的同时，还能对过程噪声协方差进行自主评估。改进Sage-Husa增广滤波精简了运算步骤，相比于假设统计估计，协方差的自主评估更具合理性，同时提高了算法的整体精度。实验结果表明：IAUKF的室内行人定位精度相较于UKF算法提升了30.7%，其步均误差为0.523 m。

Addressing the challenge of Wi-Fi signal susceptibility to environmental fluctuations, cumulative errors in pedestrian dead reckoning (PDR) system and the inability of unscented Kalman filter (UKF) to convey additive nonlinear process noise covariance in the indoor UKF fusion localization algorithm of Wi-Fi signal and PDR, an innovation augmented unscented Kalman filter(IAUKF) fusion algorithm was introduced. The Sage-Husa filtering enhanced the algorithm's resistance to signal singularities by real-time adjustment of the supervisory covariance using innovation vectors. While ensuring that high-order moment information can be transmitted, the augmented form of filtering also autonomously assessed the process noise covariance. This augmentation not only streamlined computational processes but also enhanced the autonomy of covariance estimation compared to hypothetical statistical estimations, thereby improving the overall algorithm accuracy. The final experimental results demonstrate that the indoor pedestrian positioning accuracy of IAUKF improves by 30.7% compared to UKF algorithm, with an average step error of 0.523 m.