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.

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.

Home exercise and self-served gyms allow a larger population to exercise regularly without the cost of hiring private coaches. In absence of professional guidance, however, exercisers can suffer from injuries to muscles and joints. High-precision, affordable arm tracking with commercial, off-the-shelf (COTS) wearable devices has become an urgent need to prevent workout injuries and improve exercise performance. Recent studies with inertial measurement units (IMUs) or audio signals are neither computationally feasible for real-time motion tracking with satisfactory accuracy using COTS devices nor practically usable due to the interference with noisy ambient environments. In this paper, we propose UltraMotion, a real-time, high-precision ultrasonic arm motion tracking system designed for practical use. UltraMotion performs point cloud queries based on hidden Markov models (HMMs), a novel ultrasonic acoustic ranging method, and an extended Kalman filter (EKF) to predict the locations of all three arm joints, making it the first system offering shoulder locations. Experimental results with only a smartphone and a smartwatch demonstrate the effectiveness of UltraMotion in tracking shoulder, elbow, and wrist locations with impressively small median errors of 6.4 cm, 7.1 cm, and 8.5 cm in real-world environments, outperforming all previous systems, making UltraMotion an ideal choice for daily exercise.

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.

Location-based service in the indoor environment is playing a crucial role in different application scenarios. The introduction of technologies, such as ultradense network and massive multiple-input multiple-output enables fifth-generation (5G) cellular signals, as a new generation of cellular network signals, to show unique advantages in indoor positioning. This article describes 5G reference signal structures that can be used for navigation. A high-precision time-of-arrival estimation method based on 5G downlink signal is proposed that can be realized by edge computing. A software-defined receiver (SDR) based on machine learning to extract navigation observations from 5G signals is then developed. In simulation, the error sources of SDR in additive white gaussian noise channel and multipath channel were analyzed, and the possible ranging accuracy achieved by 5G signals in the developed SDR was evaluated. In field experiments, commercial 5G signals deployed by operators were collected, and the performance of SDR in practical applications was evaluated. The feasibility in practical applications of the proposed SDR is demonstrated, and high pseudorange measurement accuracy can be achieved.

The fifth-generation (5G) networks have been massively deployed in commerce. The new features introduced by 5G networks are beneficial to wireless positioning. In this study, the performance of indoor positioning with commercial 5G new radio (NR) signals is investigated, and the channel state information (CSI) extracted from the downlink synchronization signal block is utilized. Considering the limited 5G NR base station (known as gNodeB) is hearable indoors, the fingerprint method is used, and an indoor positioning system termed iPos-5G is developed. The system consists of four components. First, a module of quality control is applied for CSI preprocessing. Second, an unsupervised deep-autoencoder network is utilized to reconstruct CSI features. Third, by supervised learning, a radial basis function is improved to optimize the probability model for similarity calculations. Finally, an amplitude-phase probability fusion function is proposed for positioning by weighting the coordinates of reference points. To verify the effectiveness of iPos-5G, indoor field tests are carried out in the scenarios of an office and a corridor. The test results show that iPos-5G achieves mean absolute errors of 2.14 and 2.81 m and standard deviation of the errors of 1.07 and 1.66 m, which outperforms the compared CSI fingerprint methods in terms of positioning accuracy and stability.

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.