In various pedestrian motion scenarios, data-driven pedestrian dead reckoning (PDR) methods have demonstrated strong localization performance, significantly enhancing system adaptability and robustness. However, most existing deep learning models have high computational costs. This makes them difficult to deploy on energy-constrained mobile devices for long-term use. Current lightweight PDR networks adopt a single-input-single-output structure. Although effective in complex dynamics, this design becomes redundant in low-dynamic conditions such as static or slow walking. To address this challenge, we propose a lightweight network architecture named Motion-aware Early-exit Inertial Odometry Network (MEIO-Net). The model adopts a multi-exit design and incorporates a dedicated training scheme. Displacement covariance is used to assess motion difficulty. An early exit mechanism is then introduced to adaptively adjust the inference depth based on dynamic complexity. With a slight improvement in accuracy, MEIO-Net significantly reduces computational cost across multiple datasets. In the self-collected data set, it achieves a 48.9% reduction in FLOPs compared to state-of-the-art lightweight models. In typical simple pedestrian motion scenarios, the computational cost of the proposed model is reduced by 50.5%–80.5%. Ablation studies further confirm that MEIO-Net can flexibly terminate inference according to the intensity of the pedestrian motion. These results demonstrate its strong generalizability and practical potential for real-world deployment. Code and dataset are available at: https://github.com/SunXuehang/MEIO-Net.git.

Ultra-wideband rigid body localization (RBL) represents a pivotal technology for achieving high-precision indoor localization. However, it encounters significant challenges, particularly non-line-of-sight (NLOS) propagation errors and reduced vertical observability in complex environments, both of which considerably undermine the robustness of existing three-dimensional(3D) RBL techniques. To mitigate these challenges, this paper introduces a novel, robust 3D-RBL methodology based on hierarchical constraint fusion, referred to as HC-RBL. This approach enhances performance with a three-tier optimization framework: 1) at the sensor layer, the Particle Swarm Optimization algorithm is used to derive the globally optimal geometric configuration of sensor anchor nodes and tag nodes, thereby maximizing spatial observability; 2) at the signal layer, a hybrid robust M-estimation coupled with multiple outlier detection techniques is leveraged to effectively suppress the localization error; 3) at the trajectory layer, the Rauch-Tung-Striebel smoothing algorithm is applied, incorporating rigid-body kinematic constraints to ensure the physical consistency of 3D motion trajectory. The dataset was obtained from indoor wheeled robot experiments, covering approximately 500 m of trajectory over 16 min. In indoor NLOS environments, with a ranging accuracy of 0.31 m, experimental results from multiple wheeled robot localization tests demonstrate that HC-RBL significantly outperforms conventional RBL methods, achieving an 76% reduction in root mean square error to 0.103 m and an 80% reduction in the 95% cumulative error to 0.182 m. The proposed HC-RBL method exhibits remarkable robustness in complex indoor environments against pronounced NLOS effects.

This paper considers downlink power allocation and beamforming (PABF) for integrated sensing and communications (ISAC) against multipath interference. Yet, ISAC-oriented PABF is of great difficulty, due to its parameter-coupling structure and non-convex problem nature. A novel PABF method is proposed to address this issue. Firstly, in order to handle its complex problem structure, the PABF problem is divided into three subproblems, where communication-end beamformer, sensing-end beamformer and multipath power vector are decoupled. Secondly, structured models of the complex problem are extracted to address the non-convexity challenge. An efficient alternating optimization-based PABF algorithm with closed-form iterations is obtained. At the sensing receiver end, our PABF method can focus beams at the line-of-sight direction, while form null beams at reflection directions for suppressing multipath interference. Simultaneously, at the communication transceiver ends, it can smartly adjust beam gains and transmitting power over multiple paths to maximize the communication performance while ensuring a promised sensing performance. The proposed PABF algorithm can strike an on-demand communication and sensing performance tradeoff, via adjusting the sensing performance requirement. We have verified the efficiency of our PABF method by numerical simulations.

Accurate positioning and navigation capabilities are essential for Internet of Things (IoT) devices. Event cameras, inspired by biological vision sensors, exhibit robust performance in high-dynamic and low-texture environments and are particularly suitable for IoT applications. However, it faces challenges with accuracy and scale in conventional slow-motion scenarios. Conversely, light detection and ranging (LiDAR) offers high precision in normal motion conditions but degrades significantly under high-dynamic motion. To integrate the advantages of both sensors, this article introduces the EVLINS algorithm, a multisource elastic fusion method based on an extended Kalman filter (EKF). This algorithm combines event-visual-inertial odometry (EVIO), LiDAR-inertial odometry (LIO), and an inertial measurement unit (IMU), utilizing a loosely coupled trajectory layer post-processing technique. This algorithm leverages the robustness of event cameras in highly dynamic environments and the precision of LiDAR in conventional settings, utilizing normalized uncertainty and nonholonomic constraint (NHC) strategies to address LIO’s degradation and EVIO’s accuracy issues. Thorough testing in various indoor and outdoor scenarios with real-world data demonstrates that EVLINS exhibits significantly improved accuracy and robustness compared to both LIO and EVIO algorithms. In large-scale, high-dynamic outdoor environments, EVLINS achieves a 3-D position accuracy of 0.68% over 1333.58 m, improving by 33.21% over LIO and 96.10% over EVIO, which diverged mid-way. In extreme indoor dynamic scenarios, EVLINS reduces maximum position error by 41.55% compared to LIO and improves overall position accuracy by 43.48%, and 22.96% compared to EVIO.

Accurate visual localization is essential in IoT applications, particularly for robotics, autonomous systems, and augmented reality. Traditional feature-based methods struggle with efficiency and robustness against environmental variations. To enhance the robustness of visual localization algorithms against these variations, state-of-the-art (SOTA) methods have incorporated semantic information as an advanced dimension into their models, but still suffer from several shortcomings. These methods often embed semantic information implicitly, which limits their extensibility and interpretability. Moreover, the introduction of some unstable semantic labels may, on the contrary, degrade the localization accuracy. Therefore, modularity, quantization, and filtering semantic labels by their stability become critical. To address these gaps, this article proposes a method that explicitly and quantitatively integrates semantic information through a plug-and-play module. This module scores image-to-image and feature-to-feature correspondences based on semantic similarity and stability, with a particular focus on improving smartphone-based visual localization in high-crowd indoor scenarios. This module is introduced into two key stages of visual hierarchical localization: 1) visual place recognition (coarse localization) and 2) 6-Degree-of-Freedom pose estimation (fine localization). Specifically, correspondences with low scores imply a higher probability of matching errors and are therefore suppressed. To validate the proposed approach, a novel dataset designed for semantic visual localization tasks is collected, rich with dynamic objects and scene variations. The method demonstrates superior accuracy and robustness, particularly in environments with significant scene appearance changes, with 13.6% and 5.4% improvement in localization accuracy in Cafds and Libds datasets, respectively, compared to the SOTA approach. The code and dataset are available athttps://github.com/1da1da/SEVL.

Multisensor integrated navigation, combining the global navigation satellite system (GNSS), inertial navigation system (INS), and light detection and ranging (LiDAR), is at the forefront of high-precision positioning technology. However, existing integration methods rely on raw point cloud data, which cannot be obtained in some projects due to geospatial data privacy concerns. For this problem, this work introduces the trajectory-layer-enhanced GNSS/INS/LiDAR integrated navigation system (TL-GILNS), which operates without raw point cloud data. This novel approach faces two primary challenges: 1) the difficulty in resolving the divergence of LiDAR positioning results due to cumulative errors and 2) the challenge of accurately assigning weights to LiDAR data in the integrated system. To overcome these obstacles, we propose a trajectory-layer LiDAR positioning enhancement method to reduce cumulative errors and a trajectory-layer LiDAR positioning accuracy quantification method to determine the weight of LiDAR. Finally, the performance of TL-GILNS is verified through experiments conducted in semi-open and large-scale complex scenes. In these scenes, TL-GILNS achieves horizontal accuracy better than 0.3 m, vertical accuracy better than 0.7 m, and yaw angle accuracy better than 1°. These results demonstrate the potential of TL-GILNS as a leading approach for high-precision navigation and positioning in complex environments.

LiDAR is widely used in Simultaneous Localization and Mapping (SLAM) and autonomous driving. The LiDAR odometry is of great importance in multi-sensor fusion. However, in some unstructured environments, the point cloud registration cannot constrain the poses of the LiDAR due to its sparse geometric features, which leads to the degeneracy of multi-sensor fusion accuracy. To address this problem, we propose a novel real-time approach to sense and compensate for the degeneracy of LiDAR. Firstly, this paper introduces the degeneracy factor with clear meaning, which can measure the degeneracy of LiDAR. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering method adaptively perceives the degeneracy with better environmental generalization. Finally, the degeneracy perception results are utilized to fuse LiDAR and IMU, thus effectively resisting degeneracy effects. Experiments on our dataset show the method’s high accuracy and robustness and validate our algorithm’s adaptability to different environments and LiDAR scanning modalities.

LiDAR-Inertial Odometry (LIO) demonstrates outstanding accuracy and stability in general low-speed and smooth motion scenarios. However, in high-speed and intense motion scenarios, such as sharp turns, two primary challenges arise: firstly, due to the limitations of IMU frequency, the error in estimating significantly non-linear motion states escalates; secondly, drastic changes in the Field of View (FOV) may diminish the spatial overlap between LiDAR frame and pointcloud map (or between frames), leading to insufficient data association and constraint degradation.To address these issues, we propose a novel Adaptive Sliding window LIO framework (AS-LIO) guided by the Spatial Overlap Degree (SOD). Initially, we assess the SOD between the LiDAR frames and the registered map, directly evaluating the adverse impact of current FOV variation on pointcloud alignment. Subsequently, we design an adaptive sliding window to manage the continuous LiDAR stream and control state updates, dynamically adjusting the update step according to the SOD. This strategy enables our odometry to adaptively adopt higher update frequency to precisely characterize trajectory during aggressive FOV variation, thus effectively reducing the non-linear error in positioning. Meanwhile, the historical constraints within the sliding window reinforce the frame-to-map data association, ensuring the robustness of state estimation. Experiments show that our AS-LIO framework can quickly perceive and respond to challenging FOV change, outperforming other state-of-the-art LIO frameworks in terms of accuracy and robustness.

As a commonly used indoor Internet of Things (IoT) positioning method, fingerprinting is frequently carried out by fusing inertial and magnetic data. However, magnetic signals may exhibit high similarity in extensive indoor environments, leading to increased mismatching in magnetic fingerprinting positioning results. It negatively impacts the overall accuracy of positioning outcomes, leading to inaccuracies. To address this challenge, this article presents a multilevel error elimination approach that refines magnetic positioning outcomes from coarse to fine-grained adjustments. It marks the inaugural research on error detection and elimination specifically focused on magnetic field positioning. The method employs velocity information, sliding median filtering, and neighborhood filtering to rapidly, accurately, and effectively detect and eliminate the errors of magnetic positioning results. This methodology primarily employs velocity information to rapidly and comprehensively eliminate coarse errors. Subsequently, sliding median filtering addresses scenarios where velocity-based error correction is unreliable, effectively facilitating the intermediate removal of errors in magnetic field positioning outcomes. Ultimately, neighborhood filtering addresses unreliable situations in the above processes, enabling small-scale and detailed elimination of errors in magnetic field positioning results. Within a 100 m$\times $60 m indoor parking lot, the proposed approaches retained 51% to 80% of magnetic positioning results and enhanced the accuracy of magnetic positioning by over 80%.

Foot-mounted inertial navigation system (Foot-INS) with the zero velocity update (ZUPT) has become one of the indispensable technical means in professional pedestrian positioning fields due to the advantages of self-constraint and immune to environmental factors. The dual-Foot-INS can provide more excellent autonomous positioning performance than a single-Foot-INS because it utilizes more opportunities for zero velocity correction and additional distance constraint information. However, the classical dual-Foot-INS does not fully exploit the distance constraint potential for positioning improvement. In this article, we proposed a novel minimum distance constraint (MDC) method that achieves higher positioning accuracy than the traditional dual-Foot-INS methods. To obtain an accurate and consistent state estimation under the nonlinear distance constraint problem, we propose an iterative distance constraint (IDC) algorithm. The IDC is transformed into an approximate linear constraint model, and an alternative estimate is obtained by the estimation projection method. To solve the problem that the distance constraint moment in the traditional method is affected by the recursive foot positions, we propose a more reasonable and reliable minimum distance moment detection (MDMD) method. The proposed MDMD method maximizes the positioning performance improvement of the dual-foot pedestrian system. Two rigorous experimental tests with a long walking trajectory without turn around and closed loop were conducted to verify the effectiveness of the proposed method, the positioning error of the proposed method is reduced by 83.5% and 62.9% compared to the classical ZUPT and MDC methods, respectively.

The third generation BeiDou Navigation Satellite System (BDS-3) can provide high-accuracy service for vehicle-borne positioning and navigation. The performance of BDS-3 in terms of accuracy, continuity, and reliability, however, are significantly degraded in environments with weak satellite observability. To improve the positioning performance of BDS-3 around the partial and complete signal-blocked areas, this paper presents a multi-sensor and analytical constraints tightly augmented BDS-3 Real-time Kinematic (RTK). The Non-holonomic Constraint (NHC), vehicle-odometer, and dual-antenna-based attitude constraints applied to restrain the drift of the position estimate from Inertial Measurement Units (IMU). Compared to previous work, it’s the first time to reveal that the Ambiguity Resolution (AR) performance of RTK can be augmented by integrating these measurements. To validate the performance of this proposed method, the drifts of position and attitude during the poor satellite observability and the impacts of those sensors and constraints on ambiguity fixing are analyzed based on an urban vehicle-borne test. The test results illustrate that the presented model brought 52.73%, 55.56%, and 49.44% positioning accuracy improvements in the north, east, and vertical components compared to the RTK mode. Meanwhile, 10.87% and 57.20% attitude accuracy enhancements in roll and heading were obtained compared to the traditional RTK/IMU tight integration mode. In addition, this presented model can retain the accuracy of position and attitude during the partial and complete satellite signal outage periods, and can also improve the ambiguity resolution performance significantly.

In recent years, indoor positioning has drawn intensive attention for both pedestrian and mobile robot applications. Among various indoor positioning technologies, visible light positioning has many advantages due to its high localization accuracy, high bandwidth, energy efficiency, long lifetime, and cost efficiency. For postprocessing or semi-real-time applications, researchers often use smoothers to improve location accuracy. However, smoothers are always local estimators and lack integrity when calculating locations. To globally optimize the positioning results and further improve the accuracy, we propose a nonlinear optimization model based on the idea of graph optimization. Innovatively, the model adds the acceleration as a constraint to become one part of the residuals and regularize the trajectory. We design a signal-to-noise ratio-based weighting strategy to suppress the outliers and better assess the errors. Moreover, we design a loop constraint to further improve the positioning accuracy. The experimental results show that our proposed model significantly improves the accuracy by 71%, which is suitable for indoor positioning.

Smartphone-based pedestrian dead reckoning (PDR) has been widely used indoors for continuous localization. However, the specific tracking solutions under different modes are vulnerable to mode transition and thus degrading performance. The robustness of pedestrian navigation may be weakened due to the mix of smartphone motions and walking patterns. Due to this challenge, most existing PDR methods assume that the smartphone is carried in a certain pose, such as handheld horizontally, swinging, calling, and pocketed, ignoring the short period but negative transition impact on tracking, which limits its flexibility when applying in the Internet of Things (IoT) services. To achieve a fully flexible PDR (i.e., FlexPDR), this article enhances the robustness and smoothness of pedestrian tracking during the transition between several phone poses and regular motion modes for the first time. We propose a Bayesian-based real-time multimode recognition method that does not require any posterior information, together with a time-series decomposition approach for adaptively tracking scheme-switching. The proposed FlexPDR system achieved a real-time smartphone indoor positioning with a high position accuracy of 98.11% on the specific situation that mixed mode-switching happens, which outperformed other state-of-the-art methods.

The wireless capsule endoscope (robot) has become more extensively used due to its comprehensive detection and patient-friendly experience. However, to provide better diagnostic information to medical staff, there is an urgent need for high-accuracy position information of capsule endoscopes during their working inside the human body. In this article, a capsule endoscopy positioning system using a magnetic sensor array is designed. It has two advantages. 1) Most of the existing magnetic positioning method needs to initialize the magnetic moment accurately, which is difficult to meet in practical applications. To solve this issue, this article proposes a method to determine the magnetic moment direction based on an inertial measurement unit. The proposed method can accurately estimate the direction of the magnetic moment even when the roll angle is singular. 2) This article proposes a nonlinear least-squares algorithm for capsule magnetic positioning based on the three-axis magnetometer observation. The algorithm is more robust than the Levenberg–Marquardt (LM) method that is widely used in capsule endoscopy positioning. Furthermore, its computation speed is over 100 times faster than the LM method, which successfully meets the real-time requirements. In this research, a three-axis mechanical platform and a six-axis robot arm are used to build a capsule magnetic positioning evaluation system. Preliminary results show the accuracy (RMS) of the proposed capsule endoscope positioning algorithm was better than 6 mm.

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.

The inertial navigation system (INS) has been widely used to provide self-contained and continuous motion estimation in intelligent transportation systems. Recently, the emergence of chip-level inertial sensors has expanded the relevant applications from positioning, navigation, and mobile mapping to location-based services, unmanned systems, and transportation big data. Meanwhile, benefit from the emergence of big data and the improvement of algorithms and computing power, machine learning (ML) has become a consensus tool that has been successfully applied in various fields. This article reviews the research on using ML technology to enhance inertial sensing from various aspects, including sensor design and selection, calibration and error modeling, navigation and motion-sensing algorithms, multi-sensor information fusion, system evaluation, and practical application. It summarizes the state of the art, advantages, and challenges on each aspect, and points out future research directions.

Localization techniques are becoming key to add location context to the Internet-of-Things (IoT) data without human perception and intervention. Meanwhile, the newly emerged low-power wide-area network (LPWAN) and 5G technologies have become strong candidates for mass-market localization applications. However, various error sources have limited localization performance by using such IoT signals. This article reviews the IoT localization system through the following sequence: IoT localization system review, localization data sources, localization algorithms, localization error sources and mitigation, and localization performance evaluation. Compared to the related surveys, this article has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors.

Location is key to spatialize Internet of Things (IoT) data. However, it is challenging to use low-cost IoT devices for robust unsupervised localization (i.e., localization without training data that have known location labels). Thus, this article proposes a deep-reinforcement-learning (DRL)-based unsupervised wireless-localization method. The main contributions are as follows: 1) this article proposes an approach to model a continuous wireless-localization process as a Markov decision process and process it within a DRL framework; 2) to alleviate the challenge of obtaining rewards when using unlabeled data (e.g., daily life crowdsourced data), this article presents a reward-setting mechanism, which extracts robust landmark data from unlabeled wireless received signal strengths (RSS); and 3) to ease requirements for model retraining when using DRL for localization, this article uses RSS measurements together with agent location to construct DRL inputs. The proposed method is tested by using field testing data from multiple Bluetooth 5 smart ear tags in a pasture. Meanwhile, the experimental verification process reflects the advantages and challenges for using DRL in wireless localization.

The next-generation Internet of Things (IoT) systems have an increasingly demand on intelligent localization which can scale with big data without human perception. Thus, traditional localization solutions without accuracy metric will greatly limit vast applications. Crowd sourcing-based localization has been proven to be effective for mass-market location-based IoT applications. This paper proposes an enhanced crowd sourcing-based localization method by integrating inertial, wireless, and magnetic sensors. Both wireless and magnetic fingerprinting accuracy are predicted in real time through the introduction of fingerprinting accuracy indicators (FAIs) from three levels (i.e., signal, geometry, and database). The advantages and limitations of these FAI factors and their performances on predicting location errors and outliers are investigated. Furthermore, the FAI-enhanced extended Kalman filter (EKF) is proposed, which improved the dead-reckoning (DR)/WiFi, DR/Magnetic, and DR/WiFi/Magnetic integrated localization accuracy by 30.2%, 19.4%, and 29.0%, and reduced the maximum location errors by 41.2%, 28.4%, and 44.2%, respectively. These outcomes confirm the effectiveness of the FAI-enhanced EKF on improving both accuracy and reliability of multisensor integrated localization using crowd sourced data.

Aimed at the challenge of generating indoor localization databases with daily life crowdsourcing-based inertial sensor data, this paper proposes an anchor point-based forward–backward smoothing method to obtain reliable localization solutions. More importantly, a quantitative framework is proposed to evaluate the quality of smartphone-based inertial sensor data automatically without user intervention. Through this framework, the reliability of each inertial sensor data can be evaluated and sorted. Tests with multiple people and multiple smartphones in a public office building and a shopping mall illustrate that the proposed method can provide a WiFi fingerprinting database that has similar accuracy to that generated by a supervised map-aided database-generation method. Therefore, the proposed method and framework can guide the promotion of crowdsourcing-based Internet of Things applications in the context of big data.

Location service is fundamental to many Internet of Things applications such as smart home, wearables, smart city, and connected health. With existing infrastructures, wireless positioning is widely used to provide the location service. However, wireless positioning has the limitations such as highly depending on the distribution of access points (APs); providing a low sample-rate and noisy solution; requiring extensive labor costs to build databases; and having unstable RSS values in indoor environments. To reduce these limitations, this paper proposes an innovative integrated platform for indoor localization by integrating low-cost microelectromechanical systems (MEMS) sensors and wireless signals. This proposed platform consists of wireless AP localization engine and sensor fusion engine, which is suitable for both dense and sparse deployments of wireless APs. The proposed platform can automatically generate wireless databases for positioning, and provide a positioning solution even in the area with only one observed wireless AP, where the traditional trilateration method cannot work. This integration platform can integrate different kinds of wireless APs together for indoor localization (e.g., WiFi, Bluetooth low energy, and radio frequency identification). The platform fuses all of these wireless distances with low-cost MEMS sensors to provide a robust localization solution. A multilevel quality control mechanism is utilized to remove noisy RSS measurements from wireless APs and to further improve the localization accuracy. Preliminary experiments show the proposed integration platform can achieve the average accuracy of 3.30 m with the sparse deployment of wireless APs (1 AP per 800 m2).

Current WiFi positioning systems (WPSs) require databases - such as locations of WiFi access points and propagation parameters, or a radio map - to assist with positioning. Typically, procedures for building such databases are time-consuming and labour-intensive. In this paper, two autonomous crowdsourcing systems are proposed to build the databases on handheld devices by using our designed algorithms and an inertial navigation solution from a Trusted Portable Navigator (T-PN). The proposed systems, running on smartphones, build and update the database autonomously and adaptively to account for the dynamic environment. To evaluate the performance of automatically generated databases, two improved WiFi positioning schemes (fingerprinting and trilateration) corresponding to these two database building systems, are also discussed. The main contribution of the paper is the proposal of two crowdsourcing-based WPSs that eliminate the various limitations of current crowdsourcing-based systems which (a) require a floor plan or GPS, (b) are suitable only for specific indoor environments, and (c) implement a simple MEMS-based sensors' solution. In addition, these two WPSs are evaluated and compared through field tests. Results in different test scenarios show that average positioning errors of both proposed systems are all less than 5.75 m.

This paper introduces a new sensor fusion approach for using multiple MEMS sensor-based pedestrian navigation systems (PNSs) to enhance the performance of each individual navigation system. First, we propose a novel single IMU-based PNS which integrates both the inertial navigation system (INS) mechanization and the pedestrian dead reckoning (PDR) mechanization. When two identical PNSs are used by a user at the same time, the output of each PNS is then shared within a Kalman filter (KF) with the state-constrained approach, which, in turn, feeds the state error correction information back to each PNS. Several real experiments are done to assess the proposed methodology for the integration of multiple PNSs. The experimental studies clearly indicate that through applying the proposed state-constrained approach, using motion sensor data from multiple mobile/wearable devices could provide more accurate navigation information for a pedestrian in all indoor and outdoor environments.

This paper provides a real-time attitude determination algorithm using gyros, accelerometers, and magnetometers on consumer portable devices. The main advantage of this algorithm is that uses a Kalman filter algorithm and utilizes multi-level constraints, including pseudo-observation updates, measurements from accelerometers and magnetometers, and the quasi-static attitude updates. Walking tests with different brands of smartphones showed that the algorithm provided promising absolute heading results outdoors, and provided smooth relative heading results indoors with different phone places such as handheld, at an ear, dangling with hand, and in a pants pocket.

This paper outlines an indoor navigation algorithm that uses multiple kinds of sensors and technologies, such as 9-axis sensors (i.e., 3D gyros, accelerometers, and magnetometers), WiFi, and magnetic matching. The corresponding real-time software on smartphones includes modules such attitude determination and gyro bias estimation, pedestrian dead-reckoning (PDR), WiFi positioning, and magnetic matching. The heading from the attitude-determination module is fed into the PDR-based position-tracking module. Then, PDR is used for providing continuous position solutions and for the blunder detection of both WiFi fingerprinting and magnetic matching. Meanwhile, WiFi fingerprinting utilizes a point-by-point matching technology, while magnetic matching is based on profile-matching. Finally, WiFi and magnetic matching results are passed into the position-tracking module as updates. This algorithm was tested with two smartphones in two indoor environments. The results indicates the proposed navigation algorithm provided better navigation results than those of PDR, WiFi, or magnetic matching by itself, and better than the results of PDR/WiFi and PDR/magnetic matching (MM) in challenging indoor environment. The proposed using off-the-shelf sensors available in consumer portable devices and existing WiFi infrastructures, and have been realized on smartphones.

This paper presents an indoor navigation algorithm that uses multiple kinds of sensors and technologies, such as MEMS sensors (i.e., gyros, accelerometers, magnetometers, and a barometer), WiFi, and magnetic matching. The corresponding real-time software on smartphones includes modules such dead-reckoning, WiFi positioning, and magnetic matching. DR is used for providing continuous position solutions and for the blunder detection of both WiFi fingerprinting and magnetic matching. Finally, WiFi and magnetic matching results are passed into the position-tracking module as updates. Meanwhile, a barometer is used to detect floor changes, so as to switch floors and the WiFi and magnetic databases. This algorithm was tested during the 5th EvAAL indoor navigation competition. Position errors on three quarters (75 %) of test points (totally 62 test points were selected to evaluate the algorithm) were under 6.6 m.

A novel calibration architecture that calculates the gyro biases without any external equipment and without any user intervention is proposed. This architecture uses a Kalman filter algorithm and utilizes multi-level constraints, such as pseudo-observation updates and the accelerometer and magnetometer measurements. Walking tests with smartphones show that the proposed architecture is effective and accurate when being used under various scenarios with different phone contexts.