Indoor localization techniques based on acoustic signals have been a focus of research community in the past decades due to their high accuracy and ubiquity. However, there are still some limitations that need to be overcome, such as multipath and non-line-of-sight (NLOS). To achieve robust and high precision acoustic ranging for practical applications on most smartphones, we propose a dual-step chirp signal detection algorithm consisting of coarse and fine searches. For robustness, the coarse search extracts the acoustic data segment containing the direct path by monitoring the energy changes based on the time-frequency (TF) analysis methods. For improving the accuracy and stability, adaptive slack and strict thresholds are introduced in cross-correlation function (CCF)-based fine search. Meanwhile, an extremum normalization method is proposed to alleviate the smartphones differences and near-far effects. A thresholds determination experiment and two practical applications are implemented on the proposed algorithm. Threshold determination experimental results show that in multipath and NLOS scenarios, the proposed coarse search can reach a success rate of more than 99.9% and an error rate of less than 0.4%. Furthermore, the proposed fine search offers a ranging accuracy with an average error and root-mean-square-error (RMSE) of less than 0.25 m and 0.35 m, respectively. For practical applications, ranging accuracies of 0.17 m and 0.14 m at 50%, and 0.59 m and 0.54 m at 95% are achieved in two typical indoor environments, which are superior to those achieved by two conventional CCF-based detection algorithms.

The increasing interest in loss prevention devices using the Internet of Things, has been driven by the convenience, low cost, and low-power consumption of these devices. However, the existing technologies cannot achieve a balance between high availability over a wide area with a single smartphone and precise location awareness. In this article, a novel precise-location-aware method that integrates acoustic technology and pedestrian dead reckoning (PDR), ChirpTracker, is proposed which most smartphones support without auxiliary equipment. This system can provide wide coverage (30 m) and is suitable for many scenarios, such as searching for cars in underground parking or finding items indoors. ChirpTracker can detect the distance between the smartphone and a lost tag in real time using acoustic signals, it can monitor the relative position change of the smartphone based on deep learning-based PDR and update relative positioning of the lost tag though the observation from single base-station in motion. A technology that combines the local least squares method (LSM) and particle filter (PF) improves the convergence and the robustness of ChirpTracker through an identification strategy for a mirror position. This method was validated in experiments conducted in actual environments. The results demonstrate the effectiveness and positioning accuracy of ChirpTracker.

The smart upgrade of large indoor venues, such as airports, exhibition centers, etc., and the rapid expansion of urban underground spaces demand modern indoor positioning technologies. However, most good positioning technologies need to fuse the inertial measurement unit (IMU) sensors to enhance the localization robustness and accuracy. In this work, an indoor audio chirp-based localization system (IALoc), which is no longer relying on the IMU sensors, is developed. We designed dedicated anchors based on embedded hardware, between which stable measurements is provided via synchronous audio networks and broadcasting strategies. The proposal distribution is improved by an empirical model of human motion in the proposed improved unscented particle filter (IUPF). The experimental results show that IALoc is able to cover the full scene with 0.6 m tracking accuracy and 1 Hz update rate in both typical indoor office and exhibition hall scenarios. As compared to UPF that carried the same number of particles, the IUPF saves 17.74% of computation time and improves the positioning accuracy by 14.29%. Since there is no need to consider the attitude of smartphones when using IUPF, it could show a considerable value of applications, such as security working and emergency rescue.

With continuous and accelerated urbanization, a large number of location-based services (LBSs) have shifted from outdoor to indoor. The pervasive smartphone-based localization has been the subject of extensive work, including signals, algorithms, technologies, solutions, and applications. However, no single ubiquitous technology or solution exists for performing indoor positioning similar to the global navigation satellite system (GNSS) in the outdoor environment. The aim of this work is to develop a practical, precise, and economic smartphone-based localization solution. In order to address the challenges of utilizing the limited audible-band acoustic signal in pervasive smartphone localization, i.e., signal detection, correction, and evaluation, we present a low-cost anchor hardware, two-step signal detection method, data-driven pedestrian dead reckoning (PDR), and robust positioning algorithm. Moreover, we further propose acoustic measurement compensation approaches and measurement quality evaluation and control strategy (MQECS) to improve the performance of position estimation. Six phones, including Huawei Mate9, P9 Plus, OnePlus 6, Honor 8, Mi 10, and Google Pixel 3 are used to evaluate the localization performance in three typical wide-area indoor scenarios (i.e., convention center, parking lot, and dining-hall). The total testbed area is accumulated to more than 8800 square meters. The experimental results demonstrate that the proposed method achieves average positioning accuracies of 0.34 m (static) and 0.67 m (dynamic). In addition, the results show that the overall performance, repeatability, and stability are superior for different scenarios and devices.

As the cornerstone of indoor location-based services (ILBSs), the smartphone-based real-time locating and tracking technologies are now becoming the key for implementing seamless indoor/outdoor location-based applications. The Wi-Fi received signal strength (RSS)-based positioning system is widely used because of the widespread deployment of Wi-Fi access points in the indoor environment. Correspondingly, the positioning performance of the RSS-based method is limited significantly by the complex and time-varying indoor environment. Contrary to the conventional RSS-based techniques, based on the introduction of a two-way ranging approach in the IEEE 802.11-REVmc2protocol, the Wi-Fi round trip time (RTT) ranging technique provides high-resolution and low-latency ranging observation on smartphones. In this work, a robust integration platform and related positioning algorithms of tightly coupled heterogeneous observables from Wi-Fi and MEMS-IMU are developed for smartphone positioning. The proposed framework optimizes the relative and absolute positioning observables in the integration process and improves the accuracy and stability as compared to the solutions, which are based on a single positioning technology. Moreover, the OQECS is established to evaluate the quality of each observation in real time before feeding the data to the adaptive filter. The experimental results demonstrate that the proposed platform achieves improvement in accuracy and robustness in both real-time tests and simulation tests performed using the polluted data. The average positioning accuracy is 0.572 m, which is 20.22% better than the results obtained from a standard EKF method.