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.

Existing acoustic-based positioning solutions face difficulties achieving precise ranging and positioning, especially in dynamic situations, due to Doppler frequency shift (DFS). In this article, we present a solution that achieves precise ToF/distance measurements between the kinematic receiver and a stationary transmitter with chirp-based Doppler shift compensation (DSC). In the solution, specific chirp signals with an upchirp and downchirp branch are transmitted by the stationary transmitter. The kinematic receiver receives and detects these signals, accordingly corrects the measurements with the proposed DSC method, and estimates the real-time velocity based on a corresponding model. After obtaining the compensated ToF/distance measurements and real-time velocities of the kinematic receiver, the initial and subsequent locations of the kinematic receiver can be precisely determined with the extended Kalman filter (EKF) and Rauch-Tung-Striebel smoother (RTS). To verify the performance of our solution, experiments in ranging and positioning were conducted in an indoor open space. The results show that the developed DSC is able to achieve an average ranging accuracy of 0.1 m for the kinematic receiver with a motion velocity of larger than 1.5 m/s in line-of-sight (LOS) situations and achieves an average positioning accuracy of 0.46 m for the kinematic receiver with motion velocity up to approximately 2 m/s. Therefore, the developed approach is sufficient for realizing acoustic-based positioning in both static and dynamic situations.

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.

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.