## 1 Introduction
The MEMS sensors-based positioning algorithm usually contains the INS and pedestrian dead reckoning (PDR); both of these methods are easily affected by the cumulative error, the complex handheld modes of smartphones, and the changing motion features of different users [22]. To solve the above challenges, previous researchers have made a lot of effort. [PERSON] et al. [5] developed a hybrid WPS that considers the merits of crowdsourced Wi-Fi fingerprinting, RTT ranging, and MEMS sensors to achieve a 3D indoor positioning function among complex environments and realizes accurate results among different scenes and meter-level accuracy under Wi-Fi RTT-supported indoor scenarios. [PERSON] et al. [11] developed a wide-area navigation frame that uses the combination of BLE, floor plan, sound source, and low-cost sensors, in which a hybrid acoustic signal transmission solution is applied, and sub-meter level positioning accuracy can be applied in real-world scenes. [PERSON] et al. [19] developed a novel time difference of arrival (TDOA) calibration algorithm for ranging drift error estimation by integrating acoustic ranging and inertial sensors data, and the final realized localization precision reached 0.2 m (RMS). [PERSON] et al. [22] proposed a novel MEMS sensors-based positioning framework by combining INS mechanization with multi-level observations, including the PDR-originated step-length and location results, a quasi-static magnetic vector, and extra acceleration, which effectively improved the performance of the attitude estimation and final location update. According to the solutions provided by previous researchers, none of them applied both Wi-Fi and acoustic ranging together. Both WPS and APS have their own advantages and disadvantages, and the innovation and necessity of using the combination of Wi-Fi and acoustic for indoor positioning in large and diversified scenes are reflected in the following aspects: (1) the need for ubiquitous navigation, supporting all intelligent terminals, and achieving full-scene coverage. (2) The complementary advantages of Wi-Fi and acoustic signals in terms of spatial distribution and precision indicators, allowing for large-scale and controllable precision positioning. (3) The complementary advantages of Wi-Fi and acoustic signals in terms of signal transmission characteristics. Acoustic positioning is greatly affected by non-line-of-sight and multipath interference; therefore, it is not suitable for densely obstructed small-scale scenes but is more appropriate for open environments. Wi-Fi fingerprint positioning has a relatively high accuracy in situations where non-line-of-sight and multipath interference are strong, but in open areas, signal feature degradation can lead to a lower accuracy. (4) The long-term accuracy of combined Wi-Fi and acoustic positioning can effectively suppress the divergence error of MEMS sensors in multi-source fusion architectures, and when combined with uncertainty analysis models, it can effectively improve the effectiveness of the intelligent fusion algorithm. To realize a hybrid indoor localization system and comprehensively consider the advantages of each location source, this work presents a hybrid indoor positioning system that uses a combination of acoustic ranging and Wi-Fi fingerprinting (H-IPS), which efficiently integrate the advantages of Wi-Fi fingerprinting, acoustic ranging, and MEMS sensors. The contribution of this paper is described as:  
(1) This paper presents a novel data and model dual-driven framework (DMDD) for pedestrian motion information estimation, which combines the INS mechanization with a deep learning-based velocity estimator and can effectively increase the localization precision of MEMS sensors under complex handheld modes and indoor magnetic interference. (2) This work proposed a double-weighted K-nearest neighbor (DW-KNN) based Wi-Fi fingerprinting algorithm and added the specific scene indexes.