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Indoor navigation is essential to a wide spectrum of applications in the era of mobile computing. Existing vision-based technologies suffer from both start-up costs and the absence of semantic information for navigation. We observe an opportunity to leverage pervasively deployed surveillance cameras to deal with the above drawbacks and revisit the problem of indoor navigation with a fresh perspective. In this paper, we propose \sysname, a system that enables public surveillance cameras, as indoor navigating satellites, to locate users on the floorplan, tell users with semantic information about the surrounding environment, and guide users with navigation instructions. However, enabling public cameras to navigate is non-trivial due to 3 factors: absence of real scale, disparity of camera perspective, and lack of semantic information. To overcome these challenges, \sysname leverages POI-assisted framework and adopts a novel coordinate transformation algorithm to associate public and mobile cameras, and further attaches semantic information to user location. Extensive experiments in 4 different scenarios show that \sysname achieves a localization accuracy of 0.48m and a navigation success rate of 90.5%, outperforming the state-of-the-art systems by over 30%. Benefitting from our solution, all areas with public cameras can upgrade to smart spaces with visual navigation services.

Automated debugging techniques, including fault localization and program repair, have been studied for over a decade. However, the only existing connection between fault localization and program repair is that fault localization computes the potential buggy elements for program repair to patch. Recently, a pioneering work, ProFL, explored the idea of unified debugging to unify fault localization and program repair in the other direction for the first time to boost both areas. In this way, ProFL also extends the application scope of automated repair to all possible bugs (not only the small ratio of bugs that repair systems can automatically fix). However, ProFL only considers one program repair system, and it is not clear how other repair systems contribute to unified debugging. In this work, we perform an extensive study of the unified debugging approach on 16 state-of-the-art program repair systems for the first time. Our initial experimental results on the Defects4J benchmark reveal various practical guidelines for unified debugging, such as (1) nearly all 16 studied repair systems positively contribute to unified debugging despite their varying repair capabilities, (2) repair systems targeting multi-edit patches can bring extraneous noise, (3) repair systems with more executed/plausible patches tend to perform better, (4) unified debugging effectiveness does not rely on the availability of correct patches, and (5) we propose a new technique, UniDebug++, which localizes over 20% more bugs within Top-1 than state-of-the-art technique ProFL. Furthermore, we extend the above experiments to make the following additional contributions: we (6) further perform an extensive study on 76.3% additional bugs and confirm that UniDebug++ again outperforms ProFL by localizing 185 (out of 395) bugs within Top-1, (7) investigate the impact of 33 SBFL formulae and observe UniDebug++ consistently improving upon all formulae, (8) demonstrate that UniDebug++ can substantially boost state-of-the-art learning-based method-level fault localization techniques, (9) extend unified debugging to the statement level for first time and observe that UniDebug++ localizes 78 (out of 395) bugs within Top-1 and outperforms state-of-the-art learning-based fault localization techniques by 30%, and finally (10) propose a new technique, UniDebug+*, based on detailed patch statistics, to further improve upon UniDebug++.

The goal of this paper is to investigate Ultra WideBand (UWB) localization with Time Difference of Arrival (TDoA) processing at the anchors. We consider scenarios where the anchors are placed very close to each other and the target to be localized is around the group of anchors. All target-anchor communications are assumed to be in Line-Of-Sight (LOS). Since our analysis shows that symmetries in anchors' placement, with respect to the target position, degrade the positioning accuracy of standard algorithms, we propose to use a Subset Selection (SS) strategy, where position estimates obtained with properly selected subsets of asymmetric anchors are fused together to get the final localization output. Our results show improved localization accuracy with respect to the use of all anchors, especially in estimating the angle of arrival. Finally, we analyze the impact of an inaccurate time synchronization among the anchors, deriving guidelines for hardware implementation.

Edge computing provides cloud services at the edge of the network for Internet of Things (IoT) devices. It aims to address low latency of the network and alleviates data processing of the cloud. This “cloud-edge-device” paradigm brings convenience as well as challenges for location-privacy protection of the IoT. In the edge computing environment, the fixed edge equipment supplies computing services for adjacent IoT devices. Therefore, edge computing suffers location leakage as the connection and authentication records imply the location of IoT devices. This article focuses on the location awareness in the edge computing environment. We adopt the “deniability” of authentication to prevent location leakage when IoT devices connect to the edge nodes. In our solution, an efficient deniable authentication based on a two-user ring signature is constructed. The robustness of authentication makes the fixed edge equipment accept the legal end devices. Besides, the deniability of authentication cannot convince any third party that the fact of this authentication occurred as communication transcript is no longer an evidence for this connection. Therefore, it handles the inherent location risk in edge computing. Compared to efficient deniable authentications, our protocol saves 10.728% and 14.696% computational cost, respectively.

In this article, we propose Double Edged (DE)-Sword, an incentivized verifiable tag path query scheme. DE-Sword queries tag records stored across a path of participants within an RFID-enabled supply chain in a verifiable way. Different with previous works, DE-Sword works in a dishonest-data owner model in which participants are the owners of tag records and may be dishonest. DE-Sword introduces a novel double-edged reputation incentive mechanism to encourage participants to behave honestly; and couples it with cryptographic primitives to ensure query verifiability. We evaluate DE-Sword through game theory, security analysis, and performance evaluation. The game theory and security analysis shows that DE-Sword guarantees query verifiability. The evaluation results show that DE-Sword incurs low overhead in supply chain systems.

Selecting reliable beacon nodes plays a significant role in relocalizing unknown nodes in a wireless sensor network. When the position of a beacon node is drifted or is spoofed, it becomes an unreliable beacon node, which would lead to a large relocalization deviation of unknown nodes in its neighbor. However, when selecting reliable beacon nodes, most relocalization algorithms only screen either drifting beacon nodes or malicious beacon nodes whose position is drifted or spoofed. This article proposes an algorithm that can simultaneously screen drifting beacon nodes and malicious beacon nodes. The algorithm is divided into four steps. First, three indicators are introduced, where two are for describing position drifting and one is for describing position spoofing. Second, the entropy method is used to weight the contributions of three indicators. Third, a technique for order preference by similarity to an ideal solution is used to construct a reliability evaluation model. Finally, using the reliability evaluation model select reliable beacon nodes. Experimental results illustrate that the detection accuracy of drifting beacon nodes and malicious beacon nodes of the proposed algorithm is 7.5% and 8.2% higher than that of the state-of-the-art algorithms, respectively.

Autonomous exploration is a fundamental task for mobile robots. However, a major limitation is that in practical applications, robots usually face dynamic obstacles such as pedestrian crowds. These dynamic objects create significant challenges for both collision-free navigation and accurate localization/mapping, which can compromise the safety and exploration performance of the robot. In this work, a hierarchical approach is proposed for both effective exploration and collision-free navigation in crowded environments. The central idea of our approach is to combine local and global information to ensure the safety and efficiency of the exploration planner. Besides, our planning method utilizes a reinforcement learning (RL)-based obstacle avoidance algorithm that allows the robot to safely follow the exploration planner's path through the pedestrian crowd. The proposed system is thoroughly evaluated in simulation environments, and the results show that it outperforms existing methods in terms of not only the exploration efficiency but also the localization and mapping accuracy.

Prior results on target localization and circumnavigation with bearing measurements in $\mathbb {R}^{2}$ are extended with integral action, resulting in a control system that is robust to bounded load disturbances on the control inputs. Such disturbances may arise in practice due to modeling errors and need to be considered to ensure small tracking errors. The control inputs are modeled as the system velocities in the bearing and orthogonal bearing directions, facilitating an implementation on both aerial and ground vehicles. This is demonstrated in a simulation example, followed by an experimental application with ground-based vehicle circumnavigation, highlighting the performance and importance of using the proposed controllers.

This paper proposes a Scale-consistent Attention Part Network (SCAPNet) to address that issue, which seamlessly integrates three novel modules: grid gate attention unit (gGAU), scale-consistent attention part selection (SCAPS), and part relation modeling (PRM). The gGAU module represents the grid region at a certain fine-scale with middle layer CNN features and produces hard attention maps with the lightweight Gumbel-Max based gate. The SCAPS module utilizes attention to guide part selection across multi-scales and keep the selection scale-consistent. The PRM module utilizes the self-attention mechanism to build the relationship among parts based on their appearance and relative geo-positions. SCAPNet can be learned in an end-to-end way and demonstrates state-of-the-art accuracy on several publicly available fine-grained recognition datasets (CUB-200-2011, FGVC-Aircraft, Veg200, and Fru92).

Both weakly supervised single object localization and semantic segmentation techniques learn an object's location using only image-level labels. However, these techniques are limited to cover only the most discriminative part of the object and not the entire object. To address this problem, we propose an attention-based dropout layer, which utilizes the attention mechanism to locate the entire object efficiently. To achieve this, we devise two key components, 1) hiding the most discriminative part from the model to capture the entire object, and 2) highlighting the informative region to improve the classification power of the model. These allow the classifier to be maintained with a reasonable accuracy while the entire object is covered. Through extensive experiments, we demonstrate that the proposed method effectively improves the weakly supervised single object localization accuracy, thereby achieving a new state-of-the-art localization accuracy on the CUB-200-2011 and a comparable accuracy existing state-of-the-arts on the ImageNet-1k. The proposed method is also effective in improving the weakly supervised semantic segmentation performance on the Pascal VOC and MS COCO. Furthermore, the proposed method is more efficient than existing techniques in terms of parameter and computation overheads. Additionally, the proposed method can be easily applied in various backbone networks.

This paper proposes a method for localization of avalanche victims by multiple UAVs. The method consists of three main parts. First, assuming that the UAVs and the victim are equipped with ARTVA receivers and a transmitter, respectively, we introduce a mathematical model of the receiver, which is used to estimate the position of the victim. Second, we derive a closed-form expression indicating the performance of this estimator. In particular, we show that the victim's observability index is captured by the persistency of excitation of a function of the UAVs trajectories. Third, we design and implement a motion planning algorithm that uses the estimation and the estima-tor's performance function for the (near) real-time generation of trajectories that guarantee feasible, safe, and time-efficient localization of avalanche victims.

The underwater glider (UG) is a new type of long-endurance marine vehicle that is currently widely used in marine environment surveillance. However, the limited capability of underwater communication and the uncertainty of the marine environment leads to a large deviation between the dead-reckoned trajectory and the real trajectory of UG. Therefore, a localization and navigation method based on ocean current data is proposed in this study. Firstly, the working principle and configuration of UG are illustrated, and the dynamic model is also analyzed. Then, the depth-averaged current is derived with motion parameters of UG, and the ocean current data from the Hybrid Coordinate Ocean Model is also introduced and processed. Meanwhile, to make an accurate localization, the DAC is stratified according to the layer weight extracted from the processed ocean current model to obtain layered DAC (LDAC). Subsequently, because the sample intervals of sensors are asynchronous with LDAC, a new matching algorithm based on sample time difference is presented to match the information of sensors and LDAC. Furthermore, the matched data is applied to localization for accurate dead reckoning. Finally, based on the data collected from two experiments, the LDAC-aided localization method proposed in this paper is compared with other benchmark positioning methods, including dynamic model-aided localization, sea current-aided localization, and acoustic beacon-aided localization methods. The results indicate that the proposed new method achieves better localization performance than other benchmark methods. In addition, the proposed method could be applied to other autonomous vehicles with communication constraints.

Visible light positioning (VLP) is a promising technology since it can provide high accuracy indoor localization based on the existing lighting infrastructure. However, existing approaches often require dense LED distributions and persistent line-of-sight (LOS) between transmitter and receiver. What's more, sensors are imperfect, and their measurements are prone to errors. Through multi sensors fusion, we can compensate the deficiencies of stand-alone sensors and provide more reliable pose estimations. In this work, we propose a loosely-coupled multi-sensor fusion method based on VLP and Simultaneous Localization and Mapping (SLAM), using light detection and ranging (LiDAR), odometry, and rolling shutter camera. Our multi-sensor localizer can provide accurate and robust robot localization and navigation in LED shortage/outage situations. The experimental results show that our proposed scheme can provide an average accuracy of 2.5 cm with around 42 ms average positioning latency.

With a flourish of wireless technologies, indoor localization is becoming an emerging topic for crucial practical research including homeland security, police patrolling, machinery fault-detection, target tracking, etc. Generally speaking, indoor localization technology involves three primary types of methodologies, namely range-based localization , fingerprint-based localization and inertial localization . This paper is aimed to present an overview of these prominent indoor localization techniques and the state-of-the-art solutions. To manifest this important topic, the general localization mechanisms are analyzed. Finally, performance requirements such as accuracy, cost, robustness are presented for benchmarking localization system performances.

Objective: Over the last two decades, radar-based contactless monitoring of vital signs (heartbeat and respiration rate) has raised increasing interest as an emerging and added value to health care. However, until now, the flaws caused by indoor multipath propagation formed a fundamental hurdle for the adoption of such technology in practical healthcare applications where reliability and robustness are crucial. Multipath reflections, originated from one person, combine with the direct signals and multipaths of other people and stationary objects, thus jeopardizing individual vital signs extraction and localization. This work focuses on tackling indoor multipath propagation. Methods: We describe a methodology, based on accurate models of the indoor multipaths and of the radar signals, that enables separating the undesired multipaths from desired signals of multiple individuals, removing a key obstacle to real-world contactless vital signs monitoring and localization. Results: We also demonstrated it by accurately measure individual heart rates, respiration rates, and absolute distances (range information) of paired volunteers in a challenging real-world office setting. Conclusion: The approach, validated using a frequency-modulated continuous wave (FMCW) radar, was shown to function in an indoor environment where radar signals are severely affected by multipath reflections. Significance: Practical applications arise for health care, assisted living, geriatric and quarantine medicine, rescue and security purposes.

Indoor localization of human objects has many important applications nowadays. Here, we propose a new multiresolution device-free indoor localization scheme using wireless radio frequency (RF) fingerprints. In our proposed new device-free approach, all transceiver devices are fixed in an indoor environment, so human targets do not need to carry any transceiver device with them. First, the indoor geometry is divided into several zones, and then, the received signal strength indicators measured by the receiving antennas are input features to our designed innovative machine learning model to identify within which zone the target is. Our proposed machine learning model, i.e., a multiresolution random forest classifier, is composed of a cascade architecture, which integrates and distills learned results over various zoning resolutions. The proposed new multiresolution approach greatly outperforms the existing random forest classifier. The average Euclidean distance error resulting from our proposed new technique is 1.0376 m.

The localization system is one of the key components of autonomous vehicles. Widely-used GNSS (low-cost) alone can not meet the centimeter accuracy and is unstable in urban environment. Typically LiDAR-based localization system is very computationally demanding due to the usage of the dense point cloud map. In this paper, we propose a novel lightweight LiDAR-based localization system for autonomous vehicle in this paper. The proposed system only relies on lightweight poles and curbs landmark map, which is highly robust and efficient compared to other localization systems. Poles and curbs are selected as landmarks because of their commonality and stability. We novelly propose a Branch-and-Bound (BnB)-based global optimization method to tackle the data association problem of poles. Motion decoupling is adopted to decouple translation and rotation to improve the efficiency of the BnB-based algorithm. Besides, we propose a new local grid map-based representation for curbs to make better use of curb information. Cost functions for pole and curb are defined respectively and then fused for the subsequent non-linear optimization method to obtain the vehicle location. Experiments on KITTI dataset and our self-collected dataset demonstrate the efficiency and accuracy of our system.

Ubiquitous connection applications have become unprecedentedly attractive recently, and increasing demand for real-time multiple targets localization is proposed. Traditional methods face challenges on efficient localization simultaneously due to signal conflict. In this paper, a real-time multiple-object localization mechanism NL-Locate is proposed. By utilizing the nonlinearity of the microphone modules, we design a modulated ultrasound signal for each target device, which enables the receiver to identify each device simultaneously. Such a method does not require any channel negotiation or preallocation, and we implement the prototype of the localization system and evaluate the system performance in both simulations and real systems. The experiment results demonstrate that NL-Locate can achieve a decimeter-level localization accuracy for multiple targets efficiently.

Many Wi-Fi based device free localization (DFL) methods have been proposed for indoor location based services. Unfortunately, the received signal is superimposed with Line-of-Sight signal and reflections so that multi target DFL is only possible by estimating the time of flight (ToF) of each signal. To estimate multiple ToFs, we utilize the sampling timing offset (STO) that inherently occurs by asynchronous sampling timing between TX-RX. By utilizing STO, we can generate signals mimicking oversampled signals. We put the signal to our correlation based ToF estimation algorithm. We achieved 0.75-4.65 ns median error when 2–5 signals are superimposed.

Indoor location-based services (LBS) have exhibited large commercial and social values in smart cities, and urgent demands of which have spurred many localization techniques. Existing indoor localization approaches mostly rely on fingerprint techniques, leveraging either spatially discrete fingerprints or temporally consecutive ones for localization. However, these approaches often suffer from large errors or high time overhead in practice due to signal ambiguities or long input sequences. To overcome these drawbacks, this paper proposes a framework utilizing multiple adaptive representations of signal sequences for localization, where each representation indicates a corresponding signal structure with underlying location clues. As an example, the proposed approach takes geomagnetic signal sequences as input and infers location features from two intuitive representations, e.g., spatial and temporal ones. With adaptive signal representations, the proposed approach takes specifically optimized neural networks to extract corresponding location clues respectively and fuses them to generate more distinguishing features for more accurate localization. Furthermore, the ensemble learning mechanism is adopted in the approach and a weighted k-NN based location estimation algorithm is devised to enhance the robustness. Extensive experiments in three different trial sites demonstrate that the proposed approach outperforms state-of-the-art competing schemes by a wide margin, reducing mean localization error by more than 46%.

Target recognition in fluidic environment is an important research topic which can be implemented in various applications. Turbulent diffusion is a common phenomenon in this scenario, while the turbulent diffusion channels are not well-understood. In this letter, a BP neural network based on Bayesian regularization (BR-BPNN) is proposed to recognize a target in a bounded turbulent diffusion environment. Besides, we first propose the concentration total variation comparison (CTVC) algorithm to select the dominant sensing points in the channel. The results show that the radius and location of the target are recognized precisely, and the recognition accuracy is guaranteed (e.g., using 20 dominant sensing points, the error rate of radius is 1.73%, and the error rate of location is 0.97%).

Due to its high-dimensional data characteristics, the channel state information (CSI) of Wi-Fi signals has become a strong candidate for use in indoor localization. In addition, machine learning techniques can improve the accuracy of indoor localization systems using multiview CSI data received at multiple access points (APs). However, in complex environments, most CSI views collected at APs in non-line-of-sight (NLoS) configurations relative to a transmitter may lose so much useful data information as to become nonsalient. In this paper, we propose a practical machine learning approach named unsupervised view-selective deep learning (UVSDL), in which only the most salient CSI view is selected in an unsupervised manner to be applied in regression for localization. In an experiment in a complex building, our variational deep learning (VDL)-based regression method with the most salient CSI view achieves a localization accuracy of 1.36 m, significantly outperforming the best-known system BiLoc by 25 %.

Accurate positioning is an essential requirement of autonomous vehicular navigation system (AVNS) for safe driving. Although the vehicle position can be obtained in global position system friendly environments, in GPS denied environments (such as suburb, tunnel, forest, or underground scenarios) the positioning accuracy of AVNS is easily reduced by the trajectory error of the vehicle. In order to solve this problem, the plane, sphere, cylinder and cone are often selected as the ground control targets to eliminate the trajectory error for AVNS. However, these targets usually suffer from the limitations of incidence angle, measuring range, scanning resolution, and point cloud density, etc. To bridge this research gap, an adaptive continuum shape constraint analysis (ACSCA) method is presented in this article to design a new target with optimized identifiable specific shape to eliminate the trajectory error for AVNS. First of all, according to the proposed ACSCA method, we conduct extensive numerical simulations to explore the optimal ranges of the vertexes and the faces for target shape design, and based on these trials, the optimal target shape is found as icosahedron, which composes of ten vertexes, 20 faces and combines the properties of plane and volume target. Moreover, the algorithm of automatic detection and coordinate calculation is developed to recognize the icosahedron target and calculate its coordinates information for AVNS. Finally, a series of experimental investigation were performed to evaluate the effectiveness of the designed icosahedron target in GPS denied environments. The experimental results demonstrate that compared with the plane, sphere, cylinder and cone targets, the developed icosahedron target can produce better performances than the above targets in terms of the clustered minimum registration error, ambiguity and range of field-of-view; also can significantly improve the positioning accuracy of AVNS in GPS denied environments.

The lighthouse localization system has recently been developed and used for localization in virtual reality (VR). Not only for VR but also for a general indoor positioning systems (IPSs) it has several advantages over existing methods, including low cost, wide detection area, and easy setup. Here, we adopt the stereo configuration of a lighthouse for improved sensing performance and propose a novel calibration method for stereo configuration. For the stereo calibration, the exact positions of sufficient corresponding points in two sensor coordinates need to be determined. A printed checkerboard is widely used for stereo camera systems because it is easy to construct and its accuracy is guaranteed owing to its printing accuracy. However, in the case of the lighthouse system, it is very difficult or impossible to construct a highly accurate calibration board similar to the checkerboard mainly because of manufacturing errors. In this study, we use a receiver sensor and a two-axis linear stage equipped with micrometers. By moving predetermined distances along the x and y directions on the linear stage, we can obtain multiple-point information with high accuracy, which can then be used for the stereo calibration of two lighthouses. In this paper, the calibration and pose estimation procedures are described in detail, and the pose estimation result of the perspective-n-points method is compared with that of the triangulation method. Finally, the pose estimation accuracy of the proposed system is compared with that of a commercial system that is widely used for highly accurate medical applications.

In this letter, fingerprinting-based cooperative localization for millimeter-wave (mmWave) cell-free massive multiple-input multiple-output (MIMO) systems is investigated. For the purpose of robust localization in line-of-sight (LOS) or non-LOS (NLOS) environments, a dynamic two-dimensional (2D) fingerprint training scheme employing maximum likelihood (ML) estimation and information entropy theory is proposed. This scheme regularly extracts link state matrix and confidence factor-aided AOA vectors from uplink channels. Then, a dynamic AP select scheme along with an anchor node (AN) filtering scheme is used to rapidly lock the range of the user location. Based on this, a weighted mean square error (WMSE)-based AOA fingerprinting scheme is developed to estimate the user location. Numerical results are provided to validate the localization accuracy of the proposed scheme.

This article proposes a method to detect vocal signals and the corresponding locations of multiple humans using a multiple-input and multiple-output (MIMO) frequency-modulated continuous-wave (FMCW) radar system. The signals of multiple humans are extracted with digital beamforming by the virtual array generated by MIMO radar processing. To remove body motion artifacts that distort the speech information detected by the radar, a body motion effect cancellation method is proposed. The technique allows recovering vocal signals from detected distorted signals by estimating phase variations due to body motions. A combination of the proposed vocal signal detection algorithm with a human localization one enables to detect speaking humans by means of the MIMO radar. Experiments verify that the proposed method can accurately locate and detect speaking humans based on their vocal vibration signals.

In this letter, we study the localization of a mobile ground-user via received-signal-strength, where $N$ unmanned-aerial-vehicles (UAVs) are deployed as anchors. Due to the movements of anchors and user, the path-loss exponent is time-varying and has to be estimated properly following the cancellation of the shadowing effect. A problem is formulated to estimate the time-varying path-loss exponent and the distance between each anchor and user, however it is non-convex. In order to obtain accurate estimations, we propose to break the problem into two sub-tasks. A filter is first designed by least-mean-square method. Then, a Newton-gradient algorithm is proposed to iteratively find the estimations of the path-loss exponent and the distance. The effectiveness of the proposed method is verified by numerical simulations.

This article introduces the latest advancements in the field of Internet of Underwater Things (IoUT) with importance toward the technological aspects for communication and location awareness. The IoUT is composed of underwater devices such as sensor nodes with defined communication and networking protocols. The IoUT framework integrates various marine communication technologies based on acoustic waves, radio waves, optical waves, and magnetic induction. This new framework enables multiple applications, including ocean exploration, assisted navigation, location awareness, and environmental pollution control. Hence, this article provides state-of-the-art communication technologies and consumer electronics aspect for implementing the IoUT network. Furthermore, this article also states the recent advancements in IoUT and its useful applications in the underwater environment. Moreover, we also classify various research requirements and challenges concerning the efficient design and proper implementation of IoUT networks.

In this paper, a novel model to localize a human accurately in indoor industrial environments by integrating the inertial navigation system (INS) technique with ultra wide band (UWB) technology is present. The distances between the target human and the reference node measured from INS and UWB are used by the fusion filter. To improve accuracy, the robust extended finite impulse response (EFIR) filtering algorithm is developed to operate on multi horizons (MHs), thereby providing blind suboptimal estimates. Experimental testing has shown that the real-time blind MH EFIR algorithm is almost as accurate as the EFIR filter, which requires a preliminarily determined optimal horizon.

We consider a distributed cooperative localization (CL) algorithm for a group of mobile robots in GPS-denied environments. Each robot is equipped with IMU and optical cameras. Using the CL method, mobile robots in a group achieve stable self-localization by communicating and processing interrobot relative measurements with sparse absolute positioning information such as a few identifiable landmarks. This paper provides a fully-distributed algorithm for bearing-only cooperative localization in a form of stepwise update, in which each agent plays as both the observer and the observed to update its states in two steps. It’s an iterative algorithm that requires less memory, and does not need all-to-all communication. In the proposed method, the covariance intersection (CI) is generalized to a bearing measurement model and it can be applied in switching topology. The simulation result shows the feasibility of the present method.

Onboard GNSS signal loss is a challenging issue for automated vehicles during driving in urban areas due to the effects of tall and dense buildings, flyovers, tunnels, and vegetation. To improve the vehicle localization accuracy when GNSS signals temporarily lost, this paper proposes a Geometry-based cooperative localization method (GCL) for the Internet of Vehicles. The vehicle position is estimated using mathematical geometry information, including vehicle dynamics and road shapes, which could reduce the environment-aware position constraint of cooperative localization. The relocation approach is enabled by communicating with neighboring vehicles to attenuate GNSS signal loss on localization-based services. The efficiency and scalability of GCL are evaluated in different driving conditions. The results showed that GCL achieved better localization performance than the state-of-the-art techniques in shorter and longer GNSS signal loss situations. The experimental results demonstrated the capabilities and effectiveness of the proposed algorithm to handle typical GNSS signal loss driving scenarios.

Unmanned aerial vehicle (UAV) swarms have shown great potential for Internet of Things (IoT). Meantime, its malicious use may cause huge threat to the national security. UAV swarms show the characteristic of high density which poses formidable challenges to radar resolution in the defense of critical areas. In this article, we consider a radar equipped with the coprime array, and then, use the coherent long-time integration (LTI) technique and gridless sparse technique to detect and localize UAVs in a swarm. This strategy takes full account of advantages of the coprime array, coherent LTI technique, and gridless sparse technique, i.e.: 1) the coprime array can provide a larger array aperture than the uniform linear array with the same number of array elements to relieve the stress of the gridless sparse technique and 2) the combination of coherent LTI technique and gridless sparse technique can maximize their advantages and make up for their shortcomings. By mathematical analyses and extensive numerical examples, we show the superiority of the proposed strategy in terms of accurate detection and localization of UAV swarms.

Nowadays, the autonomous vehicle has undergone an impressive development, in which one of the most challenging tasks is to pursue high-precision and continuous navigation in complex scenarios. However, GNSS signals are very likely to be blocked and the inertial system suffers from error drift over time. To achieve centimeter-level positioning accuracy, a tightly-coupled model is built via fusing visual, GNSS, and INS measurements, based on a priori visual feature map. The visual map is first generated by a visual mobile surveying system, and the quality of the map is improved by outlier elimination and road lane tracking. This visual feature map consists of two layers that involve 3D coordinates of robust visual features and road lane markings, then two GNSS/INS/Vision fusion models are developed. The first one fuses visual 2D-3D feature matching, GNSS, and INS measurement. The proposed system is effective even when satellites are all out of lock. The second one fuses lane line constraints with GNSS, INS, and distance measurement indicator (DMI). In the experiment part, we show how a visual feature map is generated and refined, then, the performance of the positioning algorithm is evaluated. For the first fusion model, we achieve 10 cm RMS accuracy when only one feature point was matched and all GNSS signals are blocked for 20 minutes. For the other fusion model, the system maintained 10 cm RMS accuracy or even better when a few GNSS satellites are out of lock for 300 seconds.

This letter proposes the use of neural networks to realize the passive localization by signal time difference of arrival (TDOA). In the face of multiple complex targets with radiation sources in a specific area, real-time localization is an urgent problem. In this letter, positions of the known targets from the prior data are obtained and their time difference is calculated, which will be connected as data pairs and input to the neural network trained to obtain a corresponding model. Subsequently, unknown targets can be localized by this network. It is verified that the localization accuracy of the algorithm is reliable and its robustness is higher than that of traditional algorithms. The proposed method also shows that great reduction of operation time depending on the previous network training can complete real-time goals.

Thanks to their high carrying capacity and strong maneuverability, carlike robots which move with non-holonomic constraints, are frequently utilized in numerous coverage operation fields. In such fields, the robots need to complete the coverage task via autonomous path planning and tracking, which is referred to as a “Coverage Navigation for Carlike Robots” (CNCR) problem. When it comes to CNCR, two folds of challenges are put forward as follows. On one hand, the local path planner should not only consider the non-holonomic constraints of carlike robots but also should meet the high coverage rate required by the coverage task. On the other hand, during CNCR, there must be many U-turns in narrow areas. As a result, Reeds-Shepp-type paths will be planned inevitably. Tracking this sort of multi-segment paths stably is also an urgent problem to be solved in CNCR. To this end, this letter introduces a reference line constraint and a heuristic initialization strategy along with a cubic parametric path smoother to the time elastic band planner and designs a multi-segment-supported path tracker. Experimental results show that the proposed path planner largely reduces the average deviation off the reference line and obtains a high success rate even in complex environments. Besides, the designed path tracker achieves stable and high-precision tracking for multi-segment paths. Real-world experiments reveal that the proposed path planner and tracker are fully competent for CNCR, rendering our sweeping robot to attain an actual average coverage rate of 92.21%. To make our results fully reproducible, relevant codes and data are made available online at https://github.com/csLinZhang/cncr.git .

We consider the problem of abnormality localization for clinical applications. While deep learning has driven much recent progress in medical imaging, many clinical challenges are not fully addressed, limiting its broader usage. While recent methods report high diagnostic accuracies, physicians have concerns trusting these algorithm results for diagnostic decision-making purposes because of a general lack of algorithm decision reasoning and interpretability. One potential way to address this problem is to further train these models to localize abnormalities in addition to just classifying them. However, doing this accurately will require a large amount of disease localization annotations by clinical experts, a task that is prohibitively expensive to accomplish for most applications. In this work, we take a step towards addressing these issues by means of a new attention-driven weakly supervised algorithm comprising a hierarchical attention mining framework that unifies activation- and gradient-based visual attention in a holistic manner. Our key algorithmic innovations include the design of explicit ordinal attention constraints, enabling principled model training in a weakly-supervised fashion, while also facilitating the generation of visual-attention-driven model explanations by means of localization cues. On two large-scale chest X-ray datasets (NIH ChestX-ray14 and CheXpert), we demonstrate significant localization performance improvements over the current state of the art while also achieving competitive classification performance. Our code is available on https://github.com/oyxhust/HAM.

Radio technologies are appealing for unobtrusive and remote monitoring of human activities. Radar-based human activity recognition proves to be a success, for example, Project Soli developed by Google. However, it is expensive to scale up for multiuser environments. In this article, we propose a solution—the HoloTag system—which circumvents the multichannel-radar scaling problem through the use of a quasivirtual ultralow-cost UHF RFID array over which a holographic projection of its environment is measured and used to both localize and monitor the health of several targets. The method is first described in detail, before the image reconstruction process, employing known beamforming algorithms—Delay & Sum, and Capon—is shown and its scaling properties simulated. Then, the idiosyncrasies of the implementation of HoloTag using low-cost off-the-shelf hardware are explained, before its ability to simultaneously measure the breathing rates and positions of multiple real and synthetic targets with accuracies of better than 0.8 bpm and 20 cm is demonstrated.

The sensor-source geometry has a significant effect on accuracy of source localization problems. In a sensor placement problem, one attempts to optimally place the sensors in the surveillance area so as to optimize a performance criterion. Sensor placement methods mainly solve the associated problems without taking any specific constraint on permissible location of sensors into account. In practical applications, however, possible location for deployment of the sensors are subject to such limitations as environmental, industrial, and communication constraints, which affects the optimal sensor-source geometry. In this article, we consider the problem of optimal sensor placement, based on time difference of arrival measurement, under some communication constraints. It is assumed that the sensors can communicate with the reference sensor only when the associated sensor-reference interdistance is lower than a given constant value (due to a limited communication budget). The analysis conducted in this article is based on the D-optimality criterion, i.e., maximizing the determinant of associated Fisher information matrix. We theoretically analyze the optimal sensor-source geometry with an arbitrary number of sensors in two cases of the source being inside and outside of the permissible sensor placement area. Numerical simulations are included to verify the theoretical results.

This article addresses the multistatic localization of an object by exploiting the direct-blast and object-reflected signals, where the transmitter location is not known and only the sensor positions are available. We study the cases that either the object or the transmitter is moving, in a noncooperative environment at which unknown offsets are present in the time delay and Doppler shift measurements. We analyze thoroughly by the Cramer–Rao lower bound (CRLB) if the direct-blast signals are useful in increasing the object localization performance, albeit doing so introduces extra parameters of the transmitter for estimation. Performance comparison with the fully dynamic moving object and moving transmitter modeling is established. Algebraic solutions are derived for the localization problem and analysis is made to validate that the proposed estimators are able to reach the CRLB performance under Gaussian noise over the small error region.

This paper studies the multi-robot cooperative localization (CL) problem, a challenging scenario in which robots may receive spurious sensor data, potentially causing inconsistent state estimates. To address this problem, this paper presents a fully decentralized CL algorithm based on covariance union (CU), referred to as DCL-CU. The proposed approach is fault-tolerant and supports generic measurement models. Extensive Monte Carlo simulations and a group of real-world experiments were conducted to verify the performance of the proposed DCL-CU approach. The results show that the DCL-CU approach can efficiently deal with spurious sensor data.

The awareness of physical network topology in a large-scale Internet of Things (IoT) system is critical to enable location-based service provisioning and performance optimization. However, due to the dynamics and complexity of IoT networks, it is usually very difficult to discover and update the physical topology of the large-scale IoT systems in real-time. Considering the stringent latency requirements in IoT systems, while the initial processing time for topology discovery can be tolerated, latency due to real-time topology update constitutes an even higher level of challenge. In this paper, a novel fast hierarchical topology update scheme is proposed for the large-scale IoT systems enabled by using the edge-cloud collaborative architecture. Specifically, an event-driven neighbor update algorithm, termed as TriggerOn, is firstly developed to update the local neighbor table of the end devices when device association or disassociation occurs. Based on the updated neighbor tables, the physical topology update of the subnet is conducted at the coordinated edge device, where a hybrid multidimensional scaling (MDS) based 3D localization algorithm is developed to locate the newly associated devices. Simulation results have indicated that as compared to the benchmark methods, the neighbor discovery latency has been reduced dramatically, and the 3D localization accuracy has been improved. Furthermore, the overall latency incurred by the proposed hierarchical physical topology update scheme is significantly lower than the distributed consensus-based update scheme, especially for the large-scale IoT subnets.

This letter presents a decentralized algorithm that allows a swarm of identically programmed agents to cooperatively estimate their global poses using local range and bearing measurements. The design of our algorithm explicitly considers the phase asynchrony of each agent's local clock, moreover, the execution of our algorithm does not require each agent to actively keep the same neighbors over time. A theoretical analysis about the effect of each agent's sensing noise and communication loss is given, in addition, we validate the presented algorithm via experiments running on a swarm of up to 256 simulated robots and a swarm of 100 physical robots. The results from the experiments show that the presented algorithm allows each agent to estimate its global pose quickly and reliably. Video of 100 robots executing the presented algorithm as well as supplementary material can be found in [1] and [2] .

The core problem of visual multi-robot simultaneous localization and mapping (MR-SLAM) is how to efficiently and accurately perform multi-robot global localization (MR-GL). The difficulties are two-fold. The first is the difficulty of global localization for significant viewpoint difference. Appearance-based localization methods tend to fail under large viewpoint changes. Recently, semantic graphs have been utilized to overcome the viewpoint variation problem. However, the methods are highly time-consuming, especially in large-scale environments. This leads to the second difficulty, which is how to perform real-time global localization. In this letter, we propose a semantic histogram based graph matching method that is robust to viewpoint variation and can achieve real-time global localization. Based on that, we develop a system that can accurately and efficiently perform MR-GL for both homogeneous and heterogeneous robots. The experimental results show that our approach is about 30 times faster than Random Walk based semantic descriptors. Moreover, it achieves an accuracy of 95% for global localization, while the accuracy of the state-of-the-art method is 85%.

A new extreme learning machine (ELM) localization technique that uses received signal strength indicator fingerprints only is proposed for multifloor environments. This structured scheme forms multiple individual ELMs for the floors as well as for the geographically formed data clusters of each floor. Multifloor environments often have huge amount of training and online measurement data. To maximize efficiency, we develop a data preprocessing algorithm, aiming to: 1) efficiently extract out only the essential information from the vast amount of data sets and reduce the data dimension and 2) transform the floor-level data sets and positioning data sets of each floor into a proper structure that is suitable for the proposed ensemble ELM technique. The proposed solution is unique in that its offline phase exploits multiple individual ELMs for all floors to generate a set of floor-level classification functions with the preprocessed training data sets, and for each floor, it exploits multiple ELMs for the data clusters to generate a set of position regression functions. The online phase executes a coarse localization step to estimate the floor by using the floor-level classification functions and a refined step to estimate the position on the floor by using the position regression functions. The proposed algorithm and several existing algorithms are implemented to perform localization using the same measured datasets in a multistory building. For both floor estimation and localization on the floor, it outperforms existing schemes. For most cases, the performance gap is substantial.

Localization is necessary for applications such as warning systems, where the sensed data along with the location of occurrence is essential for decision making in Wireless Sensor Networks (WSNs). Equipping each sensor node with a global positioning system (GPS) unit is infeasible, as it increases both the power consumption and cost of the deployed network. A more practical approach is to employ a single or a few GPS-equipped mobile anchor nodes (MANs). These MANs localize the unknown sensor nodes by broadcasting their current locations periodically while moving in the area of interest. In a realistic environment, obstacles may be present in the deployment area that obstruct the radio connectivity between unknown sensors and the MANs. In this paper, we propose a mechanism to handle irregular obstacles in the path of various existing static trajectories. Also, we have considered an accurate and reliable channel model to provide a realistic evaluation of the proposed method. The results are compared on the basis of four different localization techniques, namely, accuracy priority trilateration (APT), time priority trilateration (TPT), weighted centroid localization (WCL) and weight-compensated weighted centroid localization (WCWCL). Experimental and simulation results show that the proposed method effectively handles regular and irregular obstacles in the deployed environment.

Wireless sensor networks have been widely adopted in various areas. However, large-scale sensor networks face four critical problems. Information transmissions through the network would cause the energy hole problem; deploying sensors in a distributed manner might cause the isolated problem; simultaneous transmissions lead to the communication collision problem; and the lack of intelligent computing incurs the limited computation problem. Due to drones' high mobility and flexibility, drone-based aerial computing emerges as a new paradigm to address these problems. In this article, we propose an aerial-computing-assisted architecture for large-scale sensor networks; the core layer contains the following modules: sensor localization, trajectory planning of drones, communication control between drones and sensors, and enabling intelligent computation on drones. The architecture significantly advances large-scale sensor networks with higher efficiency, smaller delay, larger lifetime, and more powerful computing capability.

Magnetic localization technique has the advantages of millimeter-level localization accuracy, fast localization speed, and no limit-of-sight constraints. However, most studies are limited to the tracking of single or two magnets because of the low localization success rate for multiple magnets, thereby restricting its application scenarios. In this study, a novel multi-magnetic target localization method, named individual memory-based whale optimization and Levenberg-Marquardt (IMWO-LM) algorithm, is proposed to overcome the problem of low localization success rate. The IMWO algorithm adds the individual historical optimal location memory to each whale, which improves the global search ability of the population. This IMWO algorithm result is employed as the initial guess of the LM method. Afterward, the LM result is adopted as the initial guess of the LM method in the subsequent localization process. In 20 groups of 500 localizations, the average localization success rates of the particle swarm optimization-LM algorithm, whale optimization-LM algorithm, and the proposed IMWO-LM algorithm were 85.1%, 83.82%, and 98.28%, respectively. In the experiment of locating three magnetic targets simultaneously, the average position error and the average orientation error were 3.46 mm and 6.77°, respectively. Results show that this method can significantly improve the localization success rate while maintaining high localization accuracy.

Finding a small object (e.g., earbuds, keys or a wallet) in an indoor environment (e.g., in a house or an office) can be frustrating. In this paper, we propose an innovative system, called HyperEar , to localize such an object using only a single smartphone, based on enhanced time-difference-of-arrival (TDoA) measurements over acoustic signals issued from the object. One major challenge is the hardware limitations of a Commercial-Off-The-Shelf (COTS) phone with a short separation between the two microphones and the low sampling rate of such microphones. HyperEar enhances the accuracy of TDoA measurements by virtually increasing distances between microphones through sliding the phone in the air. HyperEar requires no communication for synchronization between the phone and the object and is a low-cost and easy-to-use system. We evaluate the performance of HyperEar via extensive experiments in various indoor conditions and the results demonstrate that, for an object of 7 m away, HyperEar can achieve a mean localization accuracy of about 15 cm when the object in normal indoor environments.

This paper is concerned with the security problem of Time of Arrival (ToA) based localization schemes in a wireless sensor network (WSN) with multiple attackers and this paper focuses on defending against external attacks, especially under cooperative external attackers. The prior scheme for defending against the attacks in the localization scheme often introduce high communication overhead and their security relies on the capability of the attackers. In this paper, we propose a lightweight secure ToA-based localization scheme in a WSN by exploiting the noise feature caused by external distance attacks. In comparison with the prior scheme, our scheme provides lower communication overhead and a higher level of security. We theoretically analyze the performance of the proposed scheme over fading channels and derive the closed-form expressions. We implemented our scheme and conducted extensive performance comparisons through simulations. Our experimental results show that the closed-form expressions for the detection performance perfectly match with their simulation results as we expected. The communication overhead of the proposed scheme is saved by 72.8% than that of the prior scheme for different numbers of anchors and is independent of the times of measurements.