## 5 Conclusions  
This paper presents the H-IPS, a novel indoor positioning system that leverages the combination of acoustic ranging, Wi-Fi fingerprinting, and low-cost sensors to achieve accurate and robust localization. The proposed system offers several contributions, including the DMDD model for MEMS sensor-based trajectory estimation under complex pedestrian motion modes. Additionally, we propose the use of ADE and WAI-based quality evaluation algorithms for the error evaluation of acoustic signals and Wi-Fi fingerprinting in NLOS environments. Finally, an adaptive unscented Kalman filter is applied for intelligent fusion of different location sources and error evaluation results.  
Our comprehensive experimental results demonstrate that the proposed H-IPS significantly improves the performance of multi-source fusion-based localization. With an accuracy of 2.01 m achieved in 75% of the trials, the final integrated accuracy reaches meter-level precision, even under NLOS scenes and complex motion modes. Compared to other existing indoor positioning systems, the proposed H-IPS offers superior performance in terms of accuracy, robustness, and adaptability.  
The DMDD model used in our approach is a significant innovation as it considers both the data-driven and model-driven approaches to address the challenges of pedestrian motion modes in complex indoor environments. The proposed quality evaluation algorithms also play a crucial role in improving the accuracy of the H-IPS by accounting for the impact of NLOS environments on the acoustic signals and Wi-Fi fingerprinting. Moreover, the adaptive unscented Kalman filter enables intelligent fusion of different location sources and error evaluation results, further enhancing the overall accuracy of the system.  
The proposed H-IPS also has disadvantages in real-world applications. First of all, the estimation of ranging and location errors of acoustic signal and Wi-Fi matching needs to be enhanced using a more robust network and dataset in order to adapt to the changeable environments. In addition, the deep learning-based walking speed prediction model can use more robust networks, such as a transformer, while the capacity of the training dataset also needs to be expanded to improve the accuracy.  
In summary, the proposed H-IPS offers a promising solution for accurate and robust indoor localization. By leveraging the advantages of multiple sources, integrating the  
Figure 13: Positioning errors of different structures.  
DMDD model, developing quality evaluation algorithms, and applying the adaptive unscented Kalman filter, the H-IPS achieves meter-level accuracy in challenging indoor environments. The results of this study provide valuable insights for the development of future indoor positioning systems, opening new opportunities for the use of such systems in smart city applications.  
This paper is a collaborative work by all the authors. [PERSON] proposed the idea and implemented the system. [PERSON] performed the experiments, analyzed the data, and wrote the manuscript. [PERSON] and [PERSON] aided in proposing the idea, provided suggestions, and revised the rough draft. All authors have read and agreed to the published version of the manuscript.  
This work was supported by The Hong Kong Polytechnic University (P0045937, 1-ZYN6, 4-BCF7); the State Bureau of Surveying and Mapping, P.R. China (1-ZVE8); and the Hong Kong Research Grants Council (T22-505/19-N).  
Data Availability Statement: Data sharing is not applicable to this article.  
The authors declare no conflict of interest.