Cooperative positioning enhances positioning performance but suffers significantly from unstable communication and measurement biases. To address these challenges, this work proposes a communication and measurement robust message passing (CMR-MP) method. First, we establish a multicentralized framework that decouples communication from measurement processes. Accordingly, a communication-robust factor graph is designed with constraints adapting to varying communication statuses, enabling neighbor state propagation without control information. This allows full data exploitation even when communication status changes. Second, to mitigate biases, we incorporate a general robust kernel into the Gaussian message passing process. This adaptive robust factor dynamically fits bias distributions to properly weight measurements. Experimental results demonstrate that CMR-MP outperforms state-of-the-art methods; specifically, its circular error probability is at least two times better than conventional approaches under unstable communication and mixed bias conditions.

We focus on joint multi-beam optimization (MBO) on both transmitter and receiver of 6G integrated sensing and communication (ISAC) systems, for achieving on-demand communication and sensing (C&S) performance tradeoff for diverse users. However, MBO is of great challenge due to inevitable self-interference (SI) of full-duplex antenna arrays and its non-convex optimization problem nature. Firstly, in order to address the SI challenge, we absorb SI alleviation requirements into problem modeling, and develop a novel SI-alleviated MBO framework. Secondly, in order to handle the non-convex optimization challenge, we resort to Lagrange dual transformation and fractional transformation for problem simplification, and extract structured models to yield an efficient alternating optimization-type MBO algorithm. We establish the convergence of the proposed MBO algorithm to justify our closed-form iterative optimization design. The proposed SI-alleviated MBO method can address different C&S requirements of diverse users, via joint transmitter and receiver beam steering, which paves the way for on-demand ISAC services. It is corroborated by simulations that our SI-alleviated MBO method outperforms state-of-the-art ISAC beamforming baselines, due to our problem-specific algorithm design.

We focus on self-interference (SI) alleviated beamforming of 6 G full-duplex integrated sensing and communication (ISAC) systems, for achieving an on-demand sensing and communication performance tradeoff with suppressed SI for diverse user devices. However, SI-alleviated ISAC beamforming is of great challenge due to its complex problem structures and nonconvex optimization problem nature. In order to address this challenge, we propose to use the dual transformation framework for problem simplification, and exploit structured components of the problem model, such as convexity, linearity and fraction, for yielding an efficient iterative optimization solution. The proposed SI-alleviated beamforming method can gracefully take care of communication and sensing requirements with suppressed SI for diverse user devices, thus paving the way for an on-demand ISAC service. It is corroborated by numerical simulations that the proposed SI-alleviated beamforming method outperforms state-of-the-art ISAC beamforming baselines, due to our specially-tailored problem modeling and problem-specific algorithm design.

Visible light positioning (VLP) is one of the most promising technologies for providing high-precision, low-cost indoor positioning and navigation services. However, the traditional calibration methods of VLP are complex and unfriendly to users, which hinders the large-scale commercial deployment of VLP systems. In this paper, a fast and robust calibration method for received signal strength (RSS)-based VLP system is proposed, which dispenses with geometric measurement and greatly simplifies the calibration procedures. The proposed method calibrates the Lambert model through two steps, firstly using a ratio method to calibrate the Lambert order, and then estimating the constant term. The actual processes only require moving a robot equipped with a photo-detector (PD) along a rectangular trajectory once, and then the program will automatically estimate the required parameters by analyzing the RSS during this period. Experimental results show a good stability of the calibrated parameters, as well as a excellent distance measuring accuracy within 12 cm. The proposed method is 3.7 times more efficient than traditional methods while the positioning accuracy is close, which will greatly reduce the time and labor costs of large-scale deployment.

cooperative localization (CL) is a promising approach to improve the localization performance. However, many existing CL methods inadequately utilize the correlations within multiagent systems and underperform in scenarios involving nonlinear system model. To address this issue, this work proposes an unified CL (UCL) estimator for both cooperative self-localization (CSL) and cooperative relative-localization (CRL) processes. By incorporating an additional pseudo-CRL process, a consensus strategy and relative motion constraints, a novel augmented factor graph (FG) framework is designed to fully exploit the potential constraints in a multiagent system. Additionally, a novel error state message passing (ES-MP) scheme in error state domain is employed to improve the validity of linearization process when dealing with nonlinear system models, thereby further improving the estimation accuracy. The simulation and experimental results demonstrate that UCL outperforms many existing CL methods in both CSL and CRL accuracy. Moreover, UCL achieves a better performance compared to particle sampling-based methods with significant lower computational load, making it a computationally efficient choice for CL systems.

Visible Light Positioning (VLP) has attracted significant attention due to its low cost, low power consumption and high accuracy. However, challenges such as noise interference and limited coverage still affect the system’s performance. On the other hand, Inertial Measurement Units (IMUs) can provide position measurements that are unaffected by environmental factors, but traditional methods tend to diverge over time. To solve these issues, a method for robust fusion positioning of a deep learning-enhanced pedestrian trolley odometer and VLP has been proposed. Firstly, a deep learning-based inertial odometry (DIO) is introduced to capture gyroscope noise and hidden motion features, which can achieve precise displacement estimation within a specified window size. Secondly, a tightly coupled fusion filter that integrates IMU-based odometry with received signal strength (RSS)-based VLP is designed, it significantly enhances the system’s robustness under conditions of signal sparsity and serious signal noise interference. Finally, a method for calculating the observation error covariance based on RSS that considers measurement uncertainty has been proposed, it ensures that the system remains robust even in the presence of weak signal strength or environmental noise. Experimental evaluations demonstrate that the proposed DIO improves accuracy by 33.4% compared to existing methods, with only an 0.76% increase in average computation time. Furthermore, compared to the DIO, VLP, and particle filter fusion systems, the proposed fusion-based localization system achieves accuracy improvements of 78.1%, 23.4%, and 9.6%, respectively, while maintaining significantly lower average computational time than the particle filter system. These results indicate that the proposed fusion method significantly outperforms existing techniques and effectively addresses the limitations of individual positioning methods.

Visible Light Positioning (VLP) has emerged as a promising technique in the Internet of Things landscape and gained increasing attention worldwide due to its widely existing infrastructure, high precision, and cost-effectiveness. Recently, ratio and difference-based VLP systems have been used to reduce errors from environmental noise, ambient light, and device differences. However, there may be intricate interference patterns that simple ratios and differences struggle to address. Moreover, a single LED often has limited capability to achieve self-diagnosis and self-correction. In fact, the information from other LEDs can be used to refine the signal and suppress interference. Thus, we propose to organize the VLP system in a graph and use the Graph Neural Network to model the interrelationships among LED lamps. This allows us to optimize the signals and further efficiently suppress interferences by simultaneously considering multiple LED lamps. In addition, the precisions of LEDs’ measurements is different due to various factors (e.g., distances and powers), and low-precision measurements may reduce the performance of the VLP system. To address this issue, we incorporate an attention layer to allow our model to give higher weights to high-precision measurements. Finally, the long short-term memory network is used to model the temporal dependencies between adjacent positions in a trajectory. Taking these modules together, we develop a robust VLP system called DeepVLP. The comprehensive experiments demonstrate that DeepVLP achieves better performance than state-of-the-art methods.

The escalating demand for indoor location-based services has propelled advancements in indoor positioning technologies in which visible light positioning (VLP) standing out for its accuracy and eco-friendliness. Current VLP systems are typically categorized into multi-anchor-based and single-anchor-based methods. The former encounters challenges like high wiring costs, limited adaptability in narrow spaces, and the segregation of lighting and positioning. Current single-anchor methods using cameras or photodiode (PD) arrays face complex hardware design costs and application limitations. To address these issues, a deep learning-enhanced VLP method with a light-emitting diode (LED) array is proposed. Firstly, a cost-effective single-anchor VLP system is designed, utilizing a LED array and a single PD, which dramatically reduces the costs. Secondly, to mitigate the impact of various noises on the received signal, a transformer-based signal-denoising network is designed, resulting in a significant reduction in the large ranging errors. Furthermore, a distance error estimation network based on graph neural network (GNN) is introduced to address the issue of poor anchor configuration caused by LED arrays. The GNN aggregates inherent correlations among LEDs, significantly reducing ranging errors. Finally, a weighted particle swarm optimization (PSO)-based positioning algorithm is introduced to further optimize results, and GNN’s outputs are converted into weights for PSO using a self-designed weight function, enhancing accuracy and robustness. Experimental evaluations of the hardware and proposed methods demonstrate sub-meter-level positioning accuracy. Moreover, the effective coverage range is comparable to that of a regular LED fixture of the same size, suggesting its broad applications prospects in the Internet of Things (IoT) after it integrates lighting and positioning capabilities.

Visible light positioning (VLP), characterized by high accuracy, low power consumption, cost effectiveness, and eco-friendliness, has garnered substantial attention in Internet of Things (IoT) positioning applications. However, the commonly used received signal strength (RSS)-based multianchor methods exhibit limitations in addressing certain challenging scenarios, such as tilted receivers, nonuniform light intensity, and narrow spaces, while incurring additional expenses for wiring and controllers. To tackle these challenges, we propose a novel VLP system utilizing a custom-designed light-emitting diode (LED) array. Initially, we conduct an in-depth analysis of the array’s size, layout, and specialized LED design, referencing prevalent household lamps to ensure that the devised array fulfills both illumination and positioning functions. Subsequently, a phase-difference-based ranging and localization method is introduced to supplement the array. By assuming equidistance among the specialized LEDs, a base distance is established for global distance calculations. Our proposed method not only eliminates the need for model calibration in RSS-based approaches but also excels in addressing challenging situations that RSS-based methods fail to resolve. Furthermore, we incorporate the Cramer–Rao lower bound and the geometric dilution of precision to assess the theoretical performance and efficacy of the proposed method. Simulation experiments are performed to evaluate the proposed array and method, taking into account various error sources, such as time synchronization errors and receiver noise. Results demonstrate that our proposed method’s average localization error is less than 10 cm under various influences and diverse scenarios, which can fulfill a wide range of IoT applications’ requirements. Moreover, it holds the potential to replace commonly used household lamps.

Ultrawideband (UWB)-based localization is widely used in environments inaccessible to global navigation satellite system signals. To improve the precision of UWB-based localization, a robust distributed adaptive extended unbiased finite impulse response (EFIR) filtering algorithm is developed. The algorithm is designed to reduce round-off errors and adaptively adjust noise covariances using the expectation-maximization (EM) approach. Based on extensive experimental testing, the EFIR algorithm is shown to outperform the distributed extended Kalman filter-based algorithm and distributed EFIR filter-based algorithm under harsh conditions.

A belief propagation (BP) based cooperative integrity monitoring (BP-CIM) algorithm is proposed in this work. BP is widely adopted as the cooperative positioning (CP) estimator, however, the corresponding integrity problem is not solved which restricts its practical application. To guarantee the reliability of a BP-based CP system, our proposed BP-CIM can detect the faulty observations in a distributed approach. Meanwhile, error analysis for BP is performed to derive the CP estimation error bound, which is subsequently used to determine the protection level (PL) of BP-CIM. The simulation and experimental results show that BP-CIM outperforms many existing fault-tolerant CP algorithms in the sides of accuracy and robustness, and the calculated PL can provide a conservative error bound for the estimated CP states. BP-CIM framework can be further extended to many other multi-sensor CP systems to improve the system reliability.

For the SLAM system in robotics and autonomous driving, the accuracy of front-end odometry and back-end loop-closure detection determine the whole intelligent system performance. But the LiDAR-SLAM could be disturbed by current scene moving objects, resulting in drift errors and even loop-closure failure. Thus, the ability to detect and segment moving objects is essential for high-precision positioning and building a consistent map. In this paper, we address the problem of moving object segmentation from 3D LiDAR scans to improve the odometry and loop-closure accuracy of SLAM. We propose a novel 3D Sequential Moving-Object-Segmentation (3D-SeqMOS) method that can accurately segment the scene into moving and static objects, such as moving and static cars. Different from the existing projected-image method, we process the raw 3D point cloud and build a 3D convolution neural network for MOS task. In addition, to make full use of the spatio-temporal information of point cloud, we propose a point cloud residual mechanism using the spatial features of current scan and the temporal features of previous residual scans. Besides, we build a complete SLAM framework to verify the effectiveness and accuracy of 3D-SeqMOS. Experiments on SemanticKITTI dataset show that our proposed 3D-SeqMOS method can effectively detect moving objects and improve the accuracy of LiDAR odometry and loop-closure detection. The test results show our 3D-SeqMOS outperforms the existing state-of-the-art methods. We extend the proposed method to the SemanticKITTI: Moving Object Segmentation competition and achieve the 3rd in the leaderboard, showing its effectiveness.

Visible Light Positioning (VLP), a promising indoor positioning technique, has gained wide popularity worldwide because of its ubiquitous infrastructure, low power consumption, and high positioning precision. However, VLP systems based on photodiodes (PDs) often suffer from varying ambient light with time and space, which seriously degrades their positioning precision and robustness. In this article, we carefully evaluate the influence of the ambient light on the VLP system, which includes the reduction of positioning accuracy by varying ambient light with time and the inaccurate parameter calibration by unevenly distributed ambient light. Then, we figure out that the influence of ambient light on the Received Signals Strength (RSS) values is determined by the ambient light intensity and PD, which is independent of external factors, including distance, frequency, LED, etc. Next, we propose a new positioning framework, RatioVLP, where a ratio model that is more robust to varying ambient light with time is used. However, the ratio model is severely dependent on the Lambert parameters that are vulnerable to ambient light, which reduces the framework's precision when the calibration area is unevenly covered by ambient light. Thus, we design new parameters that are less sensitive to ambient light, calledR parameter, to connect the RSS ratio and its corresponding distance ratio, which can strengthen the ratio model's robustness and effectively reduce the influence of ambient light on the parameter calibration process. Experimental results show that the positioning precision of the proposed method is improved by more than 50 % when compared to the conventional Lambert model in scenes influenced by ambient light.

The SPiForest, a new isolation-based approach to outlier detection, constructs iTrees on the space containing all attributes by probability density-based inverse sampling. Most existing iForest (iF)-based approaches can precisely and quickly detect outliers scattering around one or more normal clusters. However, the performance of these methods seriously decreases when facing outliers whose nature "few and different" disappears in subspace (e.g., anomalies surrounded by normal samples). To solve this problem, SPiForest is proposed, which is different from existing approaches. First, SPiForest uses the principal component analysis (PCA) to find principal components and estimate each component's probability density function (pdf). Second, SPiForest utilizes the inv-pdf, which is inversely proportional to the pdf estimated from the given dataset, to generate support points in the space containing all attributes. Third, the hyperplane decided by these support points is used to isolate the outliers in the space. Next, these steps are repeated to build an iTree. Finally, many iTrees construct a forest for outlier detection. SPiForest provides two benefits: 1) it isolates outliers with fewer hyperplanes, which significantly improves the accuracy and 2) it effectively detects the outliers whose nature "few and different" disappears in subspace. Comparative analyses and experiments show that the SPiForest achieves a significant improvement in terms of area under the curve (AUC) when compared with the state-of-the-art methods. Specifically, our method improves by at most 17.7% on AUC when compared to iF-based algorithms.

Indoor positioning system (IPS) plays an important role in the applications of Internet of Things (IoT), including intelligent hospital, logistics, and warehousing. Ultrawideband (UWB)-based IPS has shown superior performance due to its strong multipath resistance and high temporal resolution. However, the non-line-of-sight (NLOS) situations noticeably degrade both the positioning accuracy and the communication reliability. To address this issue, we first propose a support vector machine (SVM)-based channel detection method to distinguish the line-of-sight (LOS) and NLOS conditions. Then, one base station (BS)-based distance and angle positioning algorithm with extended Kalman filter (DAPA-EKF) in NLOS environment is proposed. For the LOS environment, least squares (LSs) with EKF processing of acceleration (LS-AEKF) and velocity (LS-VEKF) are developed. To further improve the performance, the combination of time difference of arrival (TDOA) and KF in LOS environment is proposed. Simulation results show that the positioning accuracy of the proposed algorithm is improved in various environments. Finally, validated using more than 1000 testing positions, the positioning accuracy of LS-AEKF is 73.8%–74.1% higher than that of LS-VEKF among the two proposed algorithms in terms of three or four BSs metrics.

The emerging applications of the Internet of Things (IoT), such as driverless cars, have an increasing need for precise vehicle positioning. Inertial navigation systems (INSs) became a possible component of autonomous driving systems due to low computational load, fast response, and high autonomy. However, error accumulation presents a significant challenge. Although nonholonomic constraints (NHCs) and odometry (ODO) have been demonstrated to improve INS, NHC is not always reliable, and ODO is often inaccessible in many applications. To address these issues, we propose a novel untethered pseudo-odometry, SdoNet, a convolutional neural network that estimates vehicle velocity from raw inertial measurement unit (IMU) observations to extend NHC as a 3-D velocity constraint without needing a hardware-wheeled ODO. To eliminate the influence of interference features on the accuracy of SdoNet, we improve the SdoNet by incorporating a residual module, attention mechanism, and soft threshold to guide the network to eliminate interference features. Moreover, a lightweight noise adapter network is proposed to adjust the constraint measurement noise covariance dynamically to apply the velocity constraint properly. The proposed approach is validated on the KITTI data set, demonstrating that SdoNet enhances the network’s learning ability and achieves robust and accurate velocity regression, especially in noisy IMU observations. The mean absolute speed regression error of SdoNet is lower than the two types of long short-term memory networks by 52.52% and 71.86%, respectively. Compared to the process using only NHC, the absolute translation error is reduced by approximately 44.00% after employing the pseudo-ODO velocity constraint and further reduced by around 11.31% after employing the noise adapter.

As a critical topic of Internet of Things applications, smartphone-based indoor navigation has a rapidly growing need in various applications. However, indoor navigation technology is unreliable when facing a challenge in complex indoor environments. This article presents a tightly coupled (TC) integration of pedestrian dead reckoning (PDR) and Bluetooth for indoor pedestrian navigation and enhances it from three approaches. We first establish a Gaussian-based distance model (GDM) that improves the signal path-loss model to incorporate the prior information on the variation of signal volatility with distance. Then, the use of map information and a back-off strategy to optimize the particle transfer strategy further improves the positioning accuracy and rationality of the system. Moreover, we leverage behavioral landmarks, signal landmarks, and distance information to build a graph optimization model to optimize the proposed navigator. We have extensively verified the proposed navigator and compared it with the existing solutions and systems. Experimental results demonstrated that the average errors of the proposed solutions in three scenes were 34.71% of Bluetooth, 14.04% of PDR, 45.13% of the extended Kalman filter, 57.83% of the unscented Kalman filter, and 56.10% of PF, respectively. The results showed that our proposed solution has apparent advantages, especially when addressing the issues of incorrect trajectory updating and divergence of the system in a complex environment.

In indoor robot localization by using ultra-wideband (UWB), the extended Kalman filter (EKF)-based algorithms suffer from the colored measurement noise (CMN) that degrades the localization accuracy and causes the divergence. To overcome this issue, we develop a hybrid colored EKF and colored extended unbiased finite impulse response (EFIR) filter (cEKF/EFIR filter) employing measurement differences. We also develop this algorithm using a filter bank on merged averaging horizons to be adaptive to time-varying CMN and call it the adaptive EKF/EFIR (aEKF/EFIR) filter. Experimental testing is provided in UWB-based indoor mobile robot localization environments. It is shown that the end-to-end colored EKF/EFIR and aEKF/EFIR filtering algorithms have better performances than the EKF, EFIR filter, and their modifications for CMN.

This paper proposes a hybrid robust Gaussian belief propagation (HRGBP) as a fault-tolerant cooperative positioning (CP) system that can be used to support cooperative intelligent transportation applications. For fault-tolerant state estimation, it is well known that fault detection and exclusion (FDE) based methods and Huber’s M-estimation based methods have their own drawbacks when facing different forms of observation outliers, or faults. To solve this problem, our proposed HRGBP uses an interactive multiple model (IMM) framework to fuse these two strategies, which combines the advantages of both methods without their drawbacks. HRGBP can fully exploit the message passing process to mitigate the biased estimates, which further improves the system’s fault-tolerant robustness. HRGBP can be further adapted to fuse more fault-tolerant strategies to improve the robustness of the CP system, and be extended to other factor graph-based methods. Here, we evaluate HRGBP for observations from visual landmark range and bearing, neighboring vehicle range and bearing, and odometer. Our evaluations show that HRGBP outperforms other state-of-the-art CP methods.

Due to vehicle mobility, the topology of Vehicle Ad-hoc Networks (VANETs) may change dynamically. High mobility, limited bandwidth, and dynamic network topology pose challenges for communication in the Internet of Vehicles (IoVs). Literature works have attempted to promote efficient (e.g., lower end-to-end latency) message forwarding. However, due to the uncertain direction of message forwarding and vehicle mobility, they suffer from unreachable destinations and unstable connections. This paper explores the efficient method of message forwarding to alleviate network congestion in IoVs. We propose a Spatio-Temporal domain Autonomous Load Balancing (STALB) routing protocol. Specifically, STALB is a trajectory-based method for controlling the direction of message forwarding. STALB can significantly reduce the end-to-end latency and overload ratio, since it considers the local status of network relay devices (i.e., buffer score, congestion status) from the spatio-temporal domain. Then, we present a path reconstruction mechanism, which ensures that messages are forwarded to destinations within limited Time-To-live (TTL). Extensive simulation results show that STALB significantly outperforms other baseline methods (BSaW, TDOR, and TBHGR) regarding overhead ratio, average delivery latency, and average buffer time. Especially, the delivery rate of STALB can reach 99.9% under the sparse network scenario (4,500 messages), at least 0.7% higher than other baseline methods. Similarly, the average delivery delay of STALB is at least 84.31% lower than that of other baseline methods under the dense network scenario (18,000 messages).

Positioning technology is essential for promoting intelligence in many residential, commercial, and industrial application scenarios. To improve the accuracy of indoor positioning, researchers have proposed many localization schemes based on the fusion of sensors. However, most existing methods focus on integrating more sensors instead of further extracting the original data. In this paper, we propose an efficient cooperative positioning algorithm for None-Line-of-Sight (NLOS) environments. Firstly, a multi-scenarios NLOS detection approach is introduced based on the channel impulse response of ultra-wideband. Secondly, a cycle least-squares positioning algorithm is proposed to maximize the utilization of the original ranging information. Thirdly, we propose a cooperative positioning algorithm based on location information sharing to minimize the impact of NLOS propagation. The simulation results demonstrate that our method outperforms all baseline methods with a large margin in terms of both stability and accuracy.

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.

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.

This letter proposes a message passing enhanced distributed Kalman filter (MP-KF) for cooperative localization (CL). By simplifying the factor graph (FG) model of the traditional belief propagation (BP) algorithm, MP-KF replaces part of the message passing (MP) process in BP with the distributed Kalman filtering. According to the analysis, the computational complexity of MP-KF is lower than that of the traditional BP estimator. The results based on the experimental data set verify the effectiveness and advantages of MP-KF, it outperforms KF-based methods by fully exploiting the correlation inside a CL system, and is better than the BP-based methods by avoiding the performance loss caused by data smoothing. Results also show that MP-KF is a cost-effective approach for CL systems with an acceptable real-time performance, which is suitable for practical CL systems.

Noise and inconsistency commonly exist in real-world information networks, due to the inherent error-prone nature of human or user privacy concerns. To date, tremendous efforts have been made to advance feature learning from networks, including the most recent graph convolutional networks (GCNs) or attention GCN, by integrating node content and topology structures. However, all existing methods consider networks as error-free sources and treat feature content in each node as independent and equally important to model node relations. Noisy node content, combined with sparse features, provides essential challenges for existing methods to be used in real-world noisy networks. In this article, we propose feature-based attention GCN (FA-GCN), a feature-attention graph convolution learning framework, to handle networks with noisy and sparse node content. To tackle noise and sparse content in each node, FA-GCN first employs a long short-term memory (LSTM) network to learn dense representation for each node feature. To model interactions between neighboring nodes, a feature-attention mechanism is introduced to allow neighboring nodes to learn and vary feature importance, with respect to their connections. By using a spectral-based graph convolution aggregation process, each node is allowed to concentrate more on the most determining neighborhood features aligned with the corresponding learning task. Experiments and validations, w.r.t. different noise levels, demonstrate that FA-GCN achieves better performance than the state-of-the-art methods in both noise-free and noisy network environments.

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.

Camera–inertial measurement unit (IMU) sensor fusion has been extensively studied in recent decades. Numerous observability analysis and fusion schemes for motion estimation with self-calibration have been presented. However, it has been uncertain whether the intrinsic parameters of both the camera and the IMU are observable under general motion. To answer this question, by using the Lie derivatives, we first prove that for a rolling shutter (RS) camera–IMU system, all the intrinsic and extrinsic parameters, camera time offset, and readout time of the RS camera are observable with an unknown landmark. To our knowledge, we are the first to present such a proof. Next, to validate this analysis and to solve the drift issue of a structureless filter during standstills, we develop a keyframe-based sliding window filter (KSWF) for odometry and self-calibration, which works with a monocular RS camera or stereo RS cameras. Though the keyframe concept is widely used in vision-based sensor fusion, to our knowledge, the KSWF is the first of its kind to support self-calibration. Our simulation and real data tests have validated that it is possible to fully calibrate the camera–IMU system using the observations of opportunistic landmarks under diverse motion. Real data tests confirmed previous allusions that keeping landmarks in the state vector can remedy the drift in standstill and showed that the keyframe-based scheme is an alternative solution.

Extensive efforts have been applied to develop efficient feature extraction algorithms, which aim to achieve optimal results in many fundamental tasks such as Web-based software service clustering, recommendation and composition. However, one common issue for existing methods is that mined features are problem dependent, causing poor generalization ability across different applications. Recent studies show that we can represent networked data (e.g., citation networks and social networks) as low-dimensional vectors with rich structure and content information preserved, which can then greatly facilitate many downstream tasks such as classification and clustering. In this article, we focus on the problem of Web service network embedding, which aims to learn low-dimensional vectors to represent services by encoding both Mashup-API composition structure and service functional content. We first propose a novel probabilistic topic model to predict potential links between Mashups and APIs in the service network. Then, we develop a Service Graph Convolutional Network (Service-GCN) to learn vector representations of services, where each service (e.g., Mashup or API) forms its representation through message passing between neighborhood services over the network. We evaluate the network embedding quality on two real-world datasets for downstream classification and clustering tasks. Experimental results show that the average performance of our method improves 20.7 percent (Micro-F1) in service classification and 19.0 percent (Accuracy) in Mashup clustering compared to the state-of-the-art, which verified the effectiveness of the proposed approach for learning vector representations of Web services.

State estimation problems without absolute position measurements routinely arise in navigation of unmanned aerial vehicles, autonomous ground vehicles, etc., whose proper operation relies on accurate state estimates and reliable covariances. Unaware of absolute positions, these problems have immanent unobservable directions. Traditional causal estimators, however, usually gain spurious information on the unobservable directions, leading to over-confident covariance inconsistent with actual estimator errors. The consistency problem of fixed-lag smoothers (FLSs) has only been attacked by the first estimate Jacobian (FEJ) technique because of the complexity to analyze their observability property. But the FEJ has several drawbacks hampering its wide adoption. To ensure the consistency of a FLS, this paper introduces the right invariant error formulation into the FLS framework. To our knowledge, we are the first to analyze the observability of a FLS with the right invariant error. Our main contributions are twofold. As the first novelty, to bypass the complexity of analysis with the classic observability matrix, we show that observability analysis of FLSs can be done equivalently on the linearized system. Second, we prove that the inconsistency issue in the traditional FLS can be elegantly solved by the right invariant error formulation without artificially correcting Jacobians. By applying the proposed FLS to the monocular visual inertial simultaneous localization and mapping (SLAM) problem, we confirm that the method consistently estimates covariance similarly to a batch smoother in simulation and that our method achieved comparable accuracy as traditional FLSs on real data.

Real-world networked systems often show dynamic properties with continuously evolving network nodes and topology over time. When learning from dynamic networks, it is beneficial to correlate all temporal networks to fully capture the similarity/relevance between nodes. Recent work for dynamic network representation learning typically trains each single network independently and imposes relevance regularization on the network learning at different time steps. Such a snapshot scheme fails to leverage topology similarity between temporal networks for progressive training. In addition to the static node relationships within each network, nodes could show similar variation patterns (e.g., change of local structures) within the temporal network sequence. Both static node structures and temporal variation patterns can be combined to better characterize node affinities for unified embedding learning. In this paper, we propose Graph Attention Evolving Networks (GAEN) for dynamic network embedding with preserved similarities between nodes derived from their temporal variation patterns. Instead of training graph attention weights for each network independently, we allow model weights to share and evolve across all temporal networks based on their respective topology discrepancies. Experiments and validations, on four real-world dynamic graphs, demonstrate that GAEN outperforms the state-of-the-art in both link prediction and node classification tasks.

Bluetooth low energy (BLE) has been significantly contributed to the Internet of Things applications due to its advantages, such as low power consumption and scalability. To further expand its applications, BLE has adopted the Angle of Arrival (AoA) and the angle of departure (AoD) to improve the accuracy in direction finding (DF). The AoA/AoD is calculated using the phase difference (PD) between slots in the received BLE data. The PD performance of BLE receiver (RX) in all directions should be evaluated in detail because it directly determines the accuracy of AoA/AoD. In the traditional test method, the reference PD is converted by an antenna array with switches, which is inaccurate due to the conversion errors and path mismatches. Moreover, because of the mechanical rotation of the turntable, the test processing is very time consuming. This article proposes a BLE test waveform generation method that directly includes the reference PD. A commonly used vector signal generator (VSG) is the only required instrument in the evaluation. The compact test setup not only eliminates the requirement of antenna array, switch, turntable, and anechoic chamber but also eliminates the measurement errors caused by these devices. After creating and downloading all BLE test waveforms with reference PD in the range of −180° to 180° by user-defined steps, the PD performance of BLE RX can be quickly evaluated by switching the test waveforms using commands or running them in the sequence mode. Furthermore, three enhanced test cases with automatic test procedures are proposed to evaluate the detailed PD accuracy of BLE RX under the low signal-to-noise ratio (SNR), carrier frequency offset, and modulation frequency deviation environments. Experimental results show that the PD error of the proposed method is 0.19°, and it needs 360 ms to evaluate the PD accuracy of CC2640R2F in all directions in 1° steps.

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.

Accurate indoor localization information of the quadrotor plays an important role in many Internet-of-Things applications. To improve the estimation accuracy and robustness, a fixed-lag extended finite impulse response smoother (FEFIRS) algorithm is proposed for fusing the inertial navigation system (INS) and ultra wideband (UWB) data tightly, which employs a distance between the UWB reference nodes and a blind node measured by the INS and UWB. The FEFIRS algorithm consists of an extended unbiased finite impulse response (EFIR) filter and a fixed-lag unbiased FIR (UFIR) smoother. The EFIR filter is employed to improve the robustness, and the fix-lag UFIR smoother is capable of improving the accuracy. Based on extensive test investigations employing real data, the proposed FEFIRS has higher accuracy and robustness than the Kalman-based solutions in the tightly integrated INS/UWB-based indoor quadrotor localization.

The emerging Internet of Things (IoT) applications, such as smart manufacturing and smart home, lead to a huge demand on the provisioning of low-cost and high-accuracy positioning and navigation solutions. Inertial measurement unit (IMU) can provide an accurate inertial navigation solution in a short time but its positioning error increases fast with time due to the cumulative error of accelerometer measurement. On the other hand, ultrawideband (UWB) positioning and navigation accuracy will be affected by the actual environment and may lead to uncertain jumps even under line-of-sight (LOS) conditions. Therefore, it is hard to use a standalone positioning and navigation system to achieve high accuracy in indoor environments. In this article, we propose an integrated indoor positioning system (IPS) combining IMU and UWB through the extended Kalman filter (EKF) and unscented Kalman filter (UKF) to improve the robustness and accuracy. We also discuss the relationship between the geometric distribution of the base stations (BSs) and the dilution of precision (DOP) to reasonably deploy the BSs. The simulation results show that the prior information provided by IMU can significantly suppress the observation error of UWB. It is also shown that the integrated positioning and navigation accuracy of IPS significantly improves that of the least squares (LSs) algorithm, which only depends on UWB measurements. Moreover, the proposed algorithm has high computational efficiency and can realize real-time computation on general embedded devices. In addition, two random motion approximation model algorithms are proposed and evaluated in the real environment. The experimental results show that the two algorithms can achieve certain robustness and continuous tracking ability in the actual IPS.

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.

In practice, visible light signals undergo non-line-of-sight (NLOS) propagation, and in visible light-based positioning (VLP) methods, the NLOS links are usually treated as disturbance sources to simplify the associated signal processing. However, the impact of NLOS propagation on VLP performance is not fully understood. In this paper, we aim to reveal the performance limits of VLP systems in an NLOS propagation environment via Fisher information analysis. Firstly, the closed-form Cramer-Rao lower bound (CRLB) on the estimation error of user detector (UD) location and orientation is established to shed light on the NLOS-based VLP performance limits. Secondly, the information contribution from the NLOS channel is quantified to gain insights into the effect of the NLOS propagation on the VLP performance. It is shown that VLP can gain additional UD location information from the NLOS channel via leveraging the NLOS propagation knowledge. In other words, the NLOS channel can be exploited to improve VLP performance in addition to the line-of-sight (LOS) channel. The obtained closed-form VLP performance limits can not only provide theoretical foundations for the VLP algorithm design under NLOS propagation, but also provide a performance benchmark for various VLP algorithms.

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.

How to provide a low-cost but accurate localization solution for the indoor mobile robots are essential in many Internet of Things applications, such as smart home and asset tracking. To achieve this goal, this paper originally proposes a modified two-filter smoother based on ensemble Kalman filter (KF) (denoted as EnKS) for the localization of indoor mobile robots. The proposed EnKS algorithm consists of both a forward part of an ensemble KF (EnKF) with statistical linear regression and a backward part of a modified information KF with state error vector. The EnKS based on stochastic sampling with ensemble members can achieve better positioning accuracy than other Kalman smoothers. When compared to EnKF, the proposed EnKS combines a backward filter to compensate for the estimation error of EnKF and further improves the accuracy. Furthermore, the implementation of the proposed EnKS is conducted in the real world visible light positioning (VLP) system using pre-existing LED lights for low-cost robot localization. To make a performance comparison, this paper also uses baseline smoothers based on extended KF and central difference KF in the VLP system. Preliminary experimental results imply that the proposed EnKS is able to achieve the best positioning accuracy, as high as 11.18 cm on average, but with a comparable computational complexity, which enables to meet the demands of many robot applications.

In the past decade, technological advancements have facilitated the manufacturing of compact, inexpensive, and low-power consuming receivers and sensors for smart devices (e.g., GPS, WiFi, MEMS sensors, RFID, UWB, BLE, etc.). This led to the fast development of positioning, navigation, and location-based services (PNLBS), and much broader new applications than just providing a location or navigation.

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.