Location-based services are increasingly vital to urban digital transformation, with indoor positioning technologies enabling applications such as intelligent transportation and smart logistics. Leveraging the comprehensive indoor coverage of 5G cellular networks, this paper introduces FIVPos, a novel multi-beam fusion indoor positioning system designed for single-base-station scenarios. Unlike conventional approaches, FIVPos relies solely on transmission signals from a single commercial 5G base station, allowing simultaneous extraction of energy and distance information through a single receiving antenna. The proposed system builds on a multi-beam collaborative ranging model that combines carrier phase measurements with Reference Signal Received Power (RSRP). A lightweight stacked IMPos fingerprinting method is then employed to enhance dynamic positioning. To further improve robustness, a particle filter–based fusion framework integrates beam-based ranging with fingerprint-based matching, ensuring accurate and resilient positioning in complex indoor environments. Evaluation conducted in two representative indoor environments shows that FIVPos achieves sub-1.5-meter Root Mean Square Error (RMSE) in dynamic scenarios, while delivering over 33% reduction in Maximum Error (MAXE) compared with conventional fingerprint-only approaches. These results confirm the effectiveness of multi-beam fusion as a practical and scalable solution for high-precision indoor positioning in 5G networks.

Inertial navigation and WiFi are two common approaches for pedestrian localization. However, conventional pedestrian dead reckoning (PDR) and WiFi fingerprinting suffer from limited adaptability to different users and poor robustness to environmental changes, respectively. Recent deep-learning-based methods address pedestrian localization by modeling sequential dependencies in inertial data, but they typically rely on a single inertial measurement unit (IMU), which is insufficient to capture the spatial correlations of human skeletal motion. In parallel, the Fine Time Measurement (FTM) procedure in IEEE 802.11mc enables round-trip time (RTT)–based ranging and localization, yet the coordinates of WiFi routers still require labor-intensive prior surveying, limiting deployment flexibility. This paper presents a pedestrian and router colocalization framework that jointly estimates pedestrian trajectories and WiFi router positions. The proposed framework employs multiple body-worn IMUs and a long short-term memory (LSTM) network to learn both spatial and temporal dependencies in human motion, thereby enabling velocity dead reckoning (VDR). The VDR-estimated pedestrian velocity is then fused with WiFi RTT measurements through factor graph optimization (FGO), in which both pedestrian and router coordinates are treated as unknown variables. Experimental results demonstrate that the multi-IMU-based VDR effectively models pedestrian velocity, while WiFi RTT ranging constrains the long-term drift of VDR. The combined VDR/WiFi RTT framework achieves meter-level positioning accuracy in both indoor and outdoor environments, without requiring pre-surveyed router coordinates, and thus provides a promising solution for pedestrian localization in the Internet of Things (IoT) era.

With the development of society, autonomous mobile robots have become an important part of the Internet of Things, and providing a valid and low-cost positioning service for the mobile robots in indoor environments has become an important issue. To solve this challenge, we developed a factor graph optimization (FGO) coupled with adaptive unscented Kalman filter (AUKF) method for real-time indoor localization. Firstly, we utilized the AUKF to fuse the inertial measurement unit (IMU) and wheel odometer to obtain the coarse pose estimation. Simultaneously, the IMU’s acceleration and gyroscope zero biases, the mounting angle, and the lever arm length are estimated. The introduction of adaptive factors improves the robustness of the filter. Secondly, we presented the FGO for fusing AUKF information and 2D LiDAR information to enhance the accuracy and robustness of the LiDAR-based pose estimation in the LiDAR-degradation environments. Finally, the FGO-optimized information is utilized to correct the state of the AUKF for further improving the accuracy of the pose estimation. The field experiment results show that the proposed method improved the positioning accuracy by approximately 25.5% and 37.2%, respectively, compared with AUKF and AUKF+PL-ICP. In the LiDAR-degradation environments, the proposed method improved the positioning accuracy by about 85.5%. This confirms the method’s effectiveness for accurate and reliable positioning in indoor environments.

The widespread adoption of 5G wireless communication devices has significantly increased the demand for precise 5G-based positioning services. This study presents a beam-switching time-of-arrival (TOA) ranging technique that leverages commercial 5G new radio (NR) signals and channel state information (CSI) extracted from the physical broadcast channel (PBCH). Initially, theoretical conditions for consistent multi-beam TOA estimation are derived, along with a multi-beam demodulation method designed to meet these conditions. To overcome the challenge of unknown base station (BS) radiation patterns, a signal quality-based beam-switching strategy is developed. Furthermore, a TOA tracking framework is introduced, integrating orthogonal matching pursuit (OMP) for multipath resolution, second-order frequency-locked loop (FLL)-assisted third-order delay-locked loop (DLL) for robust TOA tracking, and total variation regularization for anomaly removal. A software-defined radio (SDR) 5G receiver tailored for commercial beamforming BSs is implemented to validate the proposed system. With clock effects removed, the proposed system yields root-mean-square ranging errors of 3.73m and 4.99m in two distinct complex scenarios, corresponding to an average improvement of 69% in ranging accuracy over single-beam methods.

The proliferation of 5G wireless devices creates a pressing need for high-precision positioning services. This paper presents an integrated acquisition–and–tracking framework that leverages physical broadcast channel (PBCH) transmissions from commercial 5G New Radio (NR) base stations (BSs). To select the strongest downlink beam without prior knowledge of the BS radiation pattern, we use an adaptive beam-switching strategy based on reference signal received power (RSRP), reference signal received quality (RSRQ), and signal-to-noise ratio (SNR). For coarse acquisition, direction-of-arrival (DOA) and time-of-arrival (TOA) estimates are produced by a two-stage procedure that combines three-dimensional sparse Bayesian learning (SBL) with gradient-ascent off-grid refinement. A closed-loop tracker then continuously refines these estimates through azimuth-locked loop (ALL), elevation-locked loop (ELL), and delay-locked loop (DLL) modules. We further derive information-theoretic limits that bound acquisition and tracking and elucidate the key factors shaping these limits. The complete framework is implemented on a software-defined radio (SDR) 5G receiver and validated in outdoor field trials. Results indicate robust performance, with an average TOA root-mean-square error (RMSE) of 3.58 m, an azimuth RMSE of 5.77°, and an elevation RMSE of 2.74°, while requiring only 2.64 switching events per minute.

The proliferation of 5G Internet of Things (IoT) devices in daily life has significantly increased the demand for 5G-based positioning services. This article presents a methodology for accurate time-of-arrival (TOA) estimation of commercial 5G new radio (NR) signals, specifically addressing the severe interference commonly encountered in challenging environments. To mitigate high noise levels, we first apply a raised cosine filter for initial denoising of channel state information (CSI), followed by frequency-domain averaging and normalization to further suppress residual noise. For precise TOA estimation, we develop an algorithm that leverages a delay lock loop (DLL) for robust signal tracking, enhanced by initialization and relocking mechanisms supported by space-alternating generalized expectation-maximization (SAGE). Additionally, we adapt several classic global positioning system (GPS) delay discriminators for compatibility with 5G NR signals, aiming to identify DLLs that perform effectively under adverse conditions. Comprehensive validation through simulations and field tests demonstrates the proposed system’s robustness and practical applicability. Furthermore, we identify delay discriminators that are particularly well-suited for deployment in challenging environments.

Smartphones provide good outdoor positioning services through the global navigation satellite system (GNSS), playing an important role in various fields, such as the Internet of Things (IoT) and smart logistics. However, since GNSS are blocked by buildings in indoor environments, there is currently no general technology or solution for indoor positioning in outdoor environments like GNSS. As a supplement to GNSS, it has been verified that pseudolite systems can make full use of the GNSS chips embedded in smartphones to provide raw observations for indoor positioning services. Aiming at the problem of indoor positioning of smartphones, a low-cost distributed asynchronous pseudolite system and an indoor positioning method based on Doppler observations are proposed. The asynchronous pseudolite system consists of multiple dual-channel transmitters and uses Doppler raw observations to reduce the need for precise synchronization of signal transmission time. To evaluate the feasibility and positioning accuracy of the method, static and dynamic experiments were carried out in a large underground garage of an building using commercial smartphones. The field experiment shows that the indoor pseudolite positioning method proposed in this article achieves static decimeter-level and dynamic meter-level positioning accuracy for smartphones. Compared with the indoor positioning technology of smartphones based on radio frequency (RF) signals, such as Wi-Fi and Bluetooth, this study explores a new indoor positioning system for smartphones, enriches the observation information, and brings more possibilities for smartphones indoor positioning. Moreover, this study discusses an on-the-fly solution for initialization without known points using only Doppler observations and motion diversity.

The Internet of Vehicles (IoV) has witnessed a significant growth in the number of participants. This rapid expansion has increased demands for computing resources and quality of service (QoS), posing challenges for mobile edge computing (MEC) in the IoV domain. Efficiently allocating computing power to meet these service demands has become a crucial concern. Therefore, joint optimization of offloading decisions and power allocation is required to achieve the tradeoff between task latency and energy consumption. To address the above challenge, we propose a multi-agent reinforcement learning (MARL) method called multi-agent twin delayed deep deterministic policy gradient (MA-TD3) in this paper. Compared to its predecessor, multi-agent deep deterministic policy gradient (MADDPG), this algorithm improves performance and execution speed. It solves the slow convergence problem caused by Q-value overestimation and reduces the computational cost. The experimental results illustrate that the proposed algorithm reaches an observable performance improvement.

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.

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.

Precise positioning is fundamental to the internet of things that delivers insights into everything from large-scale business to ordinary smart life. Accurate localization and positioning in global navigation satellite system (GNSS) denied environments, such as indoor-, underground-spaces, and forests, is one of the most prosperous research fields because of the great complexity prompted by various challenging application scenarios. Different sensors, algorithms, and combinations of those have been developed in past decades, which provided a great variety of possible solutions that deliver different positioning accuracies. However, a rigorous evaluation of the positioning accuracy of different mainstream solutions is missing, mainly because of the difficulties in acquiring reliable ground truth for referencing and the lack of comparable test/application conditions. A comprehensive benchmarking was carried out in this study based on the comparisons of six solutions that consist of different combinations of five positioning technologies, i.e., 1) ultra-wideband (UWB) and inertial measurement unit (IMU); 2) UWB, IMU, and camera; 3) UWB and light detection and ranging (LIDAR); 4) UWB and radio detection and ranging (RADAR); 5) IMU, camera and LIDAR; and 6) UWB, IMU, camera and LIDAR. The five technologies, i.e., UWB, IMU, camera, RADAR, and LIDAR, were commonly regarded as those that are with high applicability, accuracy, and robustness. New anchors self-positioning algorithm and integrity monitoring algorithm were proposed to further aid the compared solutions and the benchmark. High-precision survey (millimeter) -level ground truth references were acquired at indoor and outdoor test locations and applied in the evaluations, to assist reliable quantitive benchmarks about the positioning accuracies and stabilities of the compared solutions. The strengths, limitations, and potentials of each solution were analyzed. It was revealed that all relative positioning solutions accumulate positioning errors over time. Such accumulation was of the highest significance for RADAR, followed by camera. LIDAR is presented to be the most robust solution for relative positioning. Compared to camera, LIDAR, and RADAR alone, the integration of different technologies clearly improved the performance. The tight-coupling performed slightly superior to loose-coupling, and the unscented Kalman filter with tight-coupling had a higher positioning accuracy in most cases.

With the widespread deployment of the fifth-generation (5G) network indoors, commercial 5G signals are highly attractive in the field of indoor positioning because of their ubiquity. Considering the user equipment (UE) requirements for user privacy protection, low computational resource consumption, and the need for location services in mobile conditions, this study developed a low-cost indoor mobile positioning system based on 5G downlink multi-beam signals, termed IMPos. In particular, this research only uses the multi-beam reference signal received power as data source, which is derived from a single commercially deployed base station (BS) and received by a single receiving antenna. Based on this data source, a machine learning method is first proposed for floor-level recognition. Thereafter, a G2Bi network based on stacked recurrent neural networks is designed to achieve UE mobile self-positioning. To evaluate the performance of IMPos, field tests are carried out in different floor scenarios. Results show that even with just one BS, IMPos achieves a floor-level recognition accuracy exceeding 95% and a mobile positioning root-mean-square error of below 1.5 m in various scenarios.

In this work, we investigate the property of the multi-beam of 5G new radio (NR) signals for indoor localization. Specifically, the 5G NR signals are firstly sampled by a self-developed software-defined receiver, and the multi-beam is extracted via detecting the multiple synchronization signal blocks (SSBs). Secondly, with the assistance of the pilots in the multiple SSBs, the reference signal received power (RSRP) and reference signal received quality (RSRQ) of the multi-beam are calculated. Thirdly, by stacking the RSRP and RSRQ of the multi-beam as the observables, a fingerprint database is constructed. With the aim to efficiently process the fingerprint features and improve the accuracy of indoor localization, a CatBoost-based algorithm is proposed, and the parameters are further optimized by tree-structured parzen estimator (TPE). To verify the effectiveness of the proposed method, indoor field tests are carried out in an office scenario, where real 5G signals are transmitted from a commercial 5G NR base station indoors. The field tests demonstrate that, by taking the advantages of the multi-beam of 5G NR, the localization accuracy can be able to achieve the accuracy of 1.06 m in the metric of root mean squared error (RMSE), even when only one base station is heard indoors. By comparison with the single-beam, the accuracy of multi-beam has improved 48%.

Modelling error of smartphone-reported Global Navigation Satellite System (GNSS) locations plays an important role in urban navigation under mixed LOS/NLOS environments. In the case of pedestrian navigation, the performance of GNSS error modeling significantly affects the precision of final multi-source fusion. In this work, a novel Long Short-Term Memory (LSTM) network is developed for error modeling of smartphone-reported GNSS locations combined with the detected human motion information. The LSTM network is applied to adaptively combine multi-level observations provided by GNSS and built-in sensors-based location sources under a specific time period instead of considering only adjacent timestamps. The motion features extracted from multi-level observations is then modeled as the input vector of LSTM for training and prediction purposes, and the predicted errors under two axis in the n-frame are finally modeled as the error covariance matrix and applied in the multi-sources fusion structure. The comprehensive experiments indicate the effectivity and significant improvement for integrated localization after GNSS error modeling.

Existing visible light positioning systems suffer from high computational complexity or cannot output rotation estimation results, making it difficult to support indoor navigation. This paper introduces an indoor positioning system with two beacon light-emitting diodes (LEDs) and a photodiode array at the receiver. The photodiode array can estimate the angles of arrival of the light signals from the beacon LEDs, and the user coordinates can be expressed as closed-form functions of the LED coordinates and the measured light directional vectors. We also carry out asymptotic error analysis for the positioning algorithm, and the analytical results reveals important insights for the system design. Simulation results show that the system can achieve centimeter-level accuracy and low average rotation estimation error.

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.

Indoor wireless localization toward the next generation Wi-Fi access point has attracted considerable attention due to the presentation of the state-of-art Wi-Fi fine time measurement (FTM) protocol. In order to improve the autonomy, accuracy, and universality of wireless positioning based on the Internet of Things (IoT) terminals, this article proposes a hybrid wireless positioning system which contains the integration of Wi-Fi FTM, crowdsourced received signal strength indicator (RSSI) fingerprinting and micro-electro-mechanical-system (MEMS) sensors (H-WPS). A light-weight pedestrian aimed inertial navigation system (PINS) is proposed, which contains multilevel constraints and a global optimization model in order to eliminate the cumulative error caused by INS update. A deep-learning-based Wi-Fi fingerprinting database generation framework is developed for crowdsourced trajectories evaluation and selection. In addition, three different multisource integration models are applied to fuse the information of PINS, Wi-Fi FTM and RSSI fingerprinting, and calibrate the Wi-Fi ranging bias in real time, which is further enhanced by a novel misclosure check and the multilayer perceptron contained signal quality evaluation strategy. The comprehensive experiments demonstrate that the proposed H-WPS achieves much more precise and universal indoor positioning performance compared with the single location source, and meter-level localization precision can be realized in the Wi-Fi FTM-covered indoor scenes.

Indoor positioning is one of the core technologies of Internet of Things (IoT) and artificial intelligence (AI) and is expected to play a significant role in the upcoming era of AI. However, affected by the complexity of indoor environments, it is still highly challenging to achieve continuous and reliable indoor positioning. Currently, 5G cellular networks are being deployed worldwide, the new technologies of which have brought the approaches for improving the performance of wireless indoor positioning. In this article, we investigate the indoor positioning under the 5G new radio (NR), which has been standardized and being commercially operated in massive markets. Specifically, a solution is proposed and a software-defined radio (SDR) receiver is developed for indoor positioning. With our SDR indoor positioning system, the 5G NR signals are first sampled by universal software radio peripheral (USRP), and then, coarse synchronization is achieved via detecting the start of the synchronization signal block (SSB). Then, with the assistance of the pilots transmitted on the physical broadcasting channel (PBCH), multipath acquisition and delay tracking are sequentially carried out to estimate the Time of Arrival (ToA) of received signals. Furthermore, to improve the ToA ranging accuracy, the carrier phase of the first arrived path is estimated. Finally, to quantify the accuracy of our ToA estimation method, indoor field tests are carried out in an office environment, where a 5G NR base station (known as gNB) is installed for commercial use. Our test results show that, in the static test scenarios, the ToA accuracy measured by the 1-$\sigma $error interval is about 0.5 m, while in the pedestrian mobile environment, the probability of range accuracy within 0.8 m is 95%.

The rapid development of the Bluetooth technology offers a possible solution for indoor localization scenarios. Compared with other indoor localization technologies, such as vision, light detection and ranging, ultrawide band, etc., Bluetooth has been characterized by low cost, easy deployment, low energy consumption, and potentially high localization accuracy, which enable itself to be a competitive technology in indoor location-based services, the Internet of Things, and many other fields. In this article, we first present a comprehensive survey of Bluetooth localization technology, including the measurements for localization, working principles, and method comparison. We highlight the learning-based methods and integrated localization methods. Then, we review the applications and existing commercial solutions, revealing the possible directions for the industrialization of Bluetooth localization. Finally, this article proposes several open issues of Bluetooth localization (e.g., multichannel difference, multipath, co-channel interference, and device heterogeneity) and projects several future trends.

Indoor wireless localization using Bluetooth low energy (BLE) beacons has attracted considerable attention due to its extensive distribution and low cost properties. This article proposes a novel 3-D indoor localization algorithm which uses the combination of BLE and multiple sensors (3D-LBMS). The inertial navigation system (INS) and pedestrian dead reckoning (PDR) mechanizations are combined for accurate heading and speed estimation, which contains a multilevel constraints-based quasistatic magnetic field (QSMF) detection algorithm. In addition, dynamic-time-warping (DTW)-based BLE landmark detection algorithm is proposed to provide absolute 3-D location reference to multiple sensors-based positioning method, and the detected BLE landmark points are also used to calibrate the parameter of step-length calculation. Finally, the adaptive unscented Kalman filter (AUKF) is applied to fuse the results of INS/PDR mechanizations, QSMF and locations of detected BLE landmarks to achieve accurate and concrete multisource-based 3-D indoor localization performance. The experimental results show that the proposed 3-D-LBMS is proved to achieve meterlevel 2-D positioning accuracy and submeter level 3-D altitude estimation accuracy in typical indoor environments.

More and more applications of location-based services lead to the development of indoor positioning technology. As a part of the Internet-of-Things ecosystem, most existing indoor positioning algorithms are applied to specific situations, e.g., pedestrian navigation and target detection. To meet the high-precision indoor localization requirement, IEEE 802.11 included the Wi-Fi fine-time measurement (FTM) protocol in 2016, which provides a novel approach for Wi-Fi ranging between the mobile terminal and Wi-Fi access point (AP). This article proposes a precise 3-D indoor localization algorithm based on Wi-Fi FTM and smartphone built-in sensors (3D-WFBS). The adaptive extended Kalman filter (AEKF) is used to estimate the pedestrian's real-time heading and walking speed, and the received signal strength indication and round-trip time collected from Wi-Fi APs are combined for proximity detection and providing more accurate ranging results. In addition, the unscented particle filter is applied to fuse the results of AEKF, proximity detection, and Wi-Fi ranging. The experimental results show that compared with the existing dead reckoning method and the other fusion methods, the proposed 3D-WFBS algorithm is proved to achieve meter-level indoor positioning accuracy in typical indoor scenes.

Tactical situational awareness for military applications should be based on infrastructure-free systems and should be able to form knowledge of the previously unknown environment. Simultaneous Localization and Mapping (SLAM) is a key technology for providing an accurate and reliable infrastructure-free solution for indoor situational awareness. However, indoor environments and the requirements , especially the size and weight limits of the system, make the implementation of SLAM using existing algorithms challenging. In particular, we aim to implement SLAM using a monocular camera, due to size limitations, whereas most existing algorithms use stereo images. The two major obstacles to be overcome are the unknown scale of translation observed using a monocular camera and the shortage of features indoors, complicating visual perception. Herein, a Kalman filter based SLAM solution is discussed, utilizing a concept called visual odometry that provides absolute translation information with a reduced number of features. The results show that our solution is feasible for performing SLAM indoors using a monocular camera.

Microphone arrays, also known as acoustic antennas, have been extensively used for sound localization. Small-scale microphone arrays have especially been used in teleconferences and game consoles due to their small dimension and easy deployment. In this article, we present an approach to locating a sound source using a small linear microphone array. We describe the fundamentals of linear microphone arrays and analyze the impact of geometry in terms of positioning accuracy using the dilution of precision (DOP) concept. The generalized cross-correlation (GCC) based on the phase transform (PHAT) weighting function is used to estimate the time difference of arrivals in a microphone array. Given the time differences, we use both closed-form and iterative optimization solutions to calculate the coordinates of the sound source. In order to evaluate the performances of the solutions applied in this paper, simulations and field tests were conducted. Simulation results show that the closed-form algorithm gives a positioning error of less than 5 cm in a 10-by-10 meter room when the geometry of a microphone array is good and the signal to noise ratio (SNR) is high. Linear small microphone arrays have lower performances compared to a non-linear distributed array. When the scale of a linear array is reduced, the positioning accuracy decreases dramatically. With a small linear array, the iterative optimization algorithm gives much better performance compared to the closed-form algorithm. Field tests were conducted in an 11-by-5.6 meter room using a linear array with a length of 0.23 meters. Positioning results show an average error of 0.25 meters along the axis parallel to the linear array and 0.53 meters error along the axis which is perpendicular to the linear array.

Pseudolites provide a means for bridging the gap between outdoors and indoors when GNSS (Global Navigation Satellite System) positioning is concerned. This paper presents a ubiquitous outdoor/indoor GNSS navigation platform that utilizes GPS (Global Positioning System), GLONASS, and pulsed pseudolite (PL) signals for seamless positioning. When a pseudolite signal is pulsed to efficiently transmit the GNSS-like signal only at particular time instants, interference problems between the terrestrial pseudo-satellite signals and the space-based satellite signals are significantly reduced. Pulsed pseudolites are strategically placed indoors at known locations at the ends of building corridors to assist high-sensitivity GPS and GLONASS positioning. A particle filtering solution is implemented to combine the high-sensitivity GNSS and the pseudolite proximity information in order to provide a seamless outdoor/indoor positioning platform. As demonstrated with real-life experiments, pseudolites provide a convenient navigation aid indoors for a GNSS receiver without the need for using additional hardware.