Accurate Location Information Research Articles

A Bayesian Nonparametric Mixture Measurement Error Model With Application to Spatial Density Estimation Using Mobile Positioning Data With Multi-Accuracy and Multi-Coverage

The development of mobile network technologies has made it possible to collect location data of mobile devices through various positioning technologies. The location data can be used to estimate the spatial density of mobile devices, which in turn can be used by mobile service providers to plan for network capacity improvements. The two most prevalent positioning technologies are the assisted global positioning system (AGPS) and cell tower triangulation (CTT) methods. AGPS data provide more accurate location information than CTT data but can cover only a fraction of mobile devices, while CTT data can cover all mobile devices. Motivated by this problem, we propose a Bayesian nonparametric mixture measurement error model to estimate the spatial density function by integrating both noise-free data (i.e., AGPS data) and data contaminated with measurement errors (i.e., CTT data). The proposed model estimates the true latent locations from contaminated data, and the estimated latent locations, combined with noise-free data, are used to infer the model parameters. We model the true density function using a Dirichlet process (DP) mixture model with a bivariate beta distribution for the mixture kernel and a DP prior for the mixing distribution. The use of bivariate beta distributions for the mixture kernel allows the density function to have various shapes with a bounded support. Moreover, the use of a DP prior for the mixing distribution allows the number of mixture components to be determined automatically without being specified in advance. Therefore, the proposed model is very flexible. We demonstrate the effective performance of the proposed model via simulated and real-data examples.

High-Resolution Encoder-Decoder Networks for Low-Contrast Medical Image Segmentation.

Automatic image segmentation is an essential step for many medical image analysis applications, include computer-aided radiation therapy, disease diagnosis, and treatment effect evaluation. One of the major challenges for this task is the blurry nature of medical images (e.g., CT, MR and, microscopic images), which can often result in low-contrast and vanishing boundaries. With the recent advances in convolutional neural networks, vast improvements have been made for image segmentation, mainly based on the skip-connection-linked encoder-decoder deep architectures. However, in many applications (with adjacent targets in blurry images), these models often fail to accurately locate complex boundaries and properly segment tiny isolated parts. In this paper, we aim to provide a method for blurry medical image segmentation and argue that skip connections are not enough to help accurately locate indistinct boundaries. Accordingly, we propose a novel high-resolution multi-scale encoder-decoder network (HMEDN), in which multi-scale dense connections are introduced for the encoder-decoder structure to finely exploit comprehensive semantic information. Besides skip connections, extra deeply-supervised high-resolution pathways (comprised of densely connected dilated convolutions) are integrated to collect high-resolution semantic information for accurate boundary localization. These pathways are paired with a difficulty-guided cross-entropy loss function and a contour regression task to enhance the quality of boundary detection. Extensive experiments on a pelvic CT image dataset, a multi-modal brain tumor dataset, and a cell segmentation dataset show the effectiveness of our method for 2D/3D semantic segmentation and 2D instance segmentation, respectively. Our experimental results also show that besides increasing the network complexity, raising the resolution of semantic feature maps can largely affect the overall model performance. For different tasks, finding a balance between these two factors can further improve the performance of the corresponding network.

A Robust, Non-Cooperative Localization Algorithm in the Presence of Outlier Measurements in Ocean Sensor Networks.

As an important means of multidimensional observation on the sea, ocean sensor networks (OSNs) could meet the needs of comprehensive information observations in large-scale and multifactor marine environments. In what concerns OSNs, accurate location information is the basis of the data sets. However, because of the multipath effect—signal shadowing by waves and unintentional or malicious attacks—outlier measurements occur frequently and inevitably, which directly degrades the localization accuracy. Therefore, increasing localization accuracy in the presence of outlier measurements is a critical issue that needs to be urgently tackled in OSNs. In this case, this paper proposed a robust, non-cooperative localization algorithm (RNLA) using received signal strength indication (RSSI) in the presence of outlier measurements in OSNs. We firstly formulated the localization problem using a log-normal shadowing model integrated with a first order Taylor series. Nevertheless, the problem was infeasible to solve, especially in the presence of outlier measurements. Hence, we then converted the localization problem into the optimization problem using squared range and weighted least square (WLS), albeit in a nonconvex form. For the sake of an accurate solution, the problem was then transformed into a generalized trust region subproblem (GTRS) combined with robust functions. Although GTRS was still a nonconvex framework, the solution could be acquired by a bisection approach. To ensure global convergence, a block prox-linear (BPL) method was incorporated with the bisection approach. In addition, we conducted the Cramer–Rao low bound (CRLB) to evaluate RNLA. Simulations were carried out over variable parameters. Numerical results showed that RNLA outperformed the other algorithms under outlier measurements, notwithstanding that the time for RNLA computation was a little bit more than others in some conditions.

A Precise and Robust Segmentation-Based Lidar Localization System for Automated Urban Driving

Real-time and high-precision localization information is vital for many modules of unmanned vehicles. At present, a high-cost RTK (Real Time Kinematic) and IMU (Integrated Measurement Unit) integrated navigation system is often used, but its accuracy cannot meet the requirements and even fails in many scenes. In order to reduce the costs and improve the localization accuracy and stability, we propose a precise and robust segmentation-based Lidar (Light Detection and Ranging) localization system aided with MEMS (Micro-Electro-Mechanical System) IMU and designed for high level autonomous driving. Firstly, we extracted features from the online frame using a series of proposed efficient low-level semantic segmentation-based multiple types feature extraction algorithms, including ground, road-curb, edge, and surface. Next, we matched the adjacent frames in Lidar odometry module and matched the current frame with the dynamically loaded pre-build feature point cloud map in Lidar localization module based on the extracted features to precisely estimate the 6DoF (Degree of Freedom) pose, through the proposed priori information considered category matching algorithm and multi-group-step L-M (Levenberg-Marquardt) optimization algorithm. Finally, the lidar localization results were fused with MEMS IMU data through a state-error Kalman filter to produce smoother and more accurate localization information at a high frequency of 200Hz. The proposed localization system can achieve 3~5 cm in position and 0.05~0.1° in orientation RMS (Root Mean Square) accuracy and outperform previous state-of-the-art systems. The robustness and adaptability have been verified with localization testing data more than 1000 Km in various challenging scenes, including congested urban roads, narrow tunnels, textureless highways, and rain-like harsh weather.

A Node Localization Algorithm in Wireless Sensor Network

In order to solve the problems such as complex topology computation and low positioning accuracy induced by the random error factors in the localization process of Wireless Sensor Network(WSN), a new node localization algorithm for WSN is proposed. Firstly, the preliminary position estimation of positioned network node is finished based on the linear parameter evaluation mechanism and evaluates the received anchor node information to calculate the linear parameter value for searching the best anchor triangle, then the precise positioning of the node is done by using the weighted evaluation method and triangle positioning parameters of different anchor nodes to multiple weighted average for obtaining the accurate location information of nodes. Simulation results show that this algorithm has higher localization accuracy and lower error compared with DV-hop algorithm and DV-distance algorithm.

Indoor Positioning Based on Visible Light Communication

The emergent context-aware applications in ubiquitous computing demands for obtaining accurate location information of humans or objects in real-time. Indoor location-based services can be delivered through implementing different types of technology, among which is a recent approach that utilizes LED lighting as a medium for Visible Light Communication (VLC). The ongoing development of solid-state lighting (SSL) is resulting in the wide increase of using LED lights and thereby building the ground for a ubiquitous wireless communication network from lighting systems. Considering the recent advances in implementing Visible Light Positioning (VLP) systems, this article presents a review of VLP systems and focuses on the performance evaluation of experimental achievements on location sensing through LED lights. We have outlined the performance evaluation of different prototypes by introducing new performance metrics, their underlying principles, and their notable findings. Furthermore, the study synthesizes the fundamental characteristics of VLC-based positioning systems that need to be considered, presents several technology gaps based on the current state-of-the-art for future research endeavors, and summarizes our lessons learned towards the standardization of the performance evaluation.

Hybrid TOA Trilateration Algorithm Based on Line Intersection and Comparison Approach of Intersection Distances

The ever-growing mobile station (MS) localization technologies provide an increasingly important role in all aspects of the wireless cellular systems and Internet of Things (IoT). The accurate MS location information is the basis in connection of different devices in IoT. The MS localization techniques based on time of arrival (TOA) trilateration algorithm, which determines the location of MS using an intersection point of three circles based on distances between MS and base stations (BS) and coordinates of BSs, have been actively studied. In general, the distance between the MS and BS is calculated by counting the number of delay samples or measuring the power of the received signal. Since the estimated distance (radius of a circle) between MS and BS is commonly increased, three circles may not meet at a single point, resulting in the estimation error of MS localization. In order to improve this problem, in this paper, we propose the hybrid TOA trilateration algorithm based on the line intersection algorithm for the general case for intersection of three circles and the comparison approach of intersection distances for the specific case where a small circle is located inside the area of two large circles. The line intersection algorithm has an excellent location estimation performance in the general case, but it does not work in the specific case. The comparison approach of intersection distances has good performance only for the specific case. In addition, we propose the mode selection algorithm to efficiently select a proper mode between the general and specific cases. The representative computer simulation examples are provided to verify the localization performance of the proposed algorithm.

A Multilevel Model Approach for Assessing the Effects of House and Neighborhood Characteristics on Housing Vacancy: A Case of Daegu, South Korea

Previous studies on housing vacancy mostly focused on variables representing regional characteristics while overlooking the characteristics of individual houses. This is due to the limitations of available data. Using the house-level Housing Vacancy Database, this study aims to identify the spatial clustering pattern of vacant houses by examining single-family houses in Daegu, South Korea, and analyze the factors affecting housing vacancy. The Housing Vacancy Database built in this study provides accurate location information of vacant houses, making it possible to analyze the clustering pattern of vacant houses in a more detailed spatial unit. Furthermore, the Housing Vacancy Database considered various physical and neighborhood factors at the house level. The result of hot spot analysis showed that vacant houses were spatially concentrated in the city center. As a result of analyzing the factors affecting housing vacancy at the house level and neighborhood level using a multilevel model, it was found that the physical environment characteristics of individual houses were key factors affecting housing vacancy. Additionally, the probability of housing vacancy tended to increase when the land prices were higher, the houses were located in redevelopment zones, and there were more neighboring vacant houses nearby. Meanwhile, population decline and the ratio of old houses were the only significant variables at the neighborhood level. Thus, this study addresses that policies are needed to improve housing and physical environment characteristics that contribute to housing vacancy.

ANLoC: An Anomaly-Aware Node Localization Algorithm for WSNs in Complex Environments.

Accurate and sufficient node location information is crucial for Wireless Sensor Networks (WSNs) applications. However, the existing range-based localization methods often suffer from incomplete and detorted range measurements. To address this issue, some methods based on low-rank matrix recovery have been proposed, which usually assume noises follow single Gaussian distribution or/and single Laplacian distribution, and thus cannot handle the case with wider noise distributions beyond Gaussian and Laplacian ones. In this paper, a novel Anomaly-aware Node Localization (ANLoC) method is proposed to simultaneously impute missing range measurements and detect node anomaly in complex environments. Specifically, by utilizing inherent low-rank property of Euclidean Distance Matrix (EDM), we formulate range measurements imputation problem as a Robust -norm Regularized Matrix Decomposition (RRMD) model, where complex noise is fitted by Mixture of Gaussian (MoG) distribution, and node anomaly is sifted by -norm regularization. Meanwhile, an efficient optimization algorithm is designed to solve proposed RRMD model based on Expectation Maximization (EM) method. Furthermore, with the imputed EDM, all unknown nodes can be easily positioned by using Multi-Dimensional Scaling (MDS) method. Finally, some experiments are designed to evaluate performance of the proposed method, and experimental results demonstrate that our method outperforms three state-of-the-art node localization methods.

IMPLEMENTASI LOCATION BASED SERVICE PADA APLIKASI HALO TAMBAL BAN BERBASIS ANDROID DI KOTA MALANG

Leaks on vehicle tires experienced by motorists, and become an obstacle to vehicles when traveling, require motorists to push their vehicles to avoid excessive damage to the tires. In finding a tire service provider to fix tire leakage problems, sometimes the driver must push the vehicle without knowing where and how far to find a tire repair service provider. The HALO Tambal Ban application is an application that provides information about tire patch service providers. Application built to facilitate finding information about tire patch service providers, information about location, availability of services, operating hours, to tariffs imposed by tire patch service providers. To obtain information about the location of the nearest tire repair service provider from the application user, this research proposes to implement the Location Based Service feature on the HALO Tambal Ban application, to obtain more accurate location information. Using HTML5 and AngularJS programming languages, Ionic as a Framework, and firebase as database processing. In order to improve the accuracy of location data obtained by the application by implementing the Location Based Service feature on the HALO Tambal Ban application. Keywords: Tambal Ban, Android, Ionic, Location Based Service, Malang

Cooperative Localization in Hybrid Infrared/Visible Light Networks: Theoretical Limits and Distributed Algorithms

Light emitting diode (LED) based visible light positioning networks can provide accurate location information in environments where the global positioning system (GPS) suffers from severe signal degradation and/or cannot achieve high precision, such as indoor scenarios. In this paper, we propose to employ cooperative localization for hybrid infrared/visible light networks that involve multiple LED transmitters having known locations (e.g., on the ceiling) and visible light communication (VLC) units equipped with both LEDs and photodetectors (PDs) for the purpose of cooperation. In the considered scenario, downlink transmissions from LEDs on the ceiling to VLC units occur via visible light signals, while the infrared spectrum is utilized for device-to-device communications among VLC units. First, we derive the Cramér-Rao lower bound and the maximum likelihood estimator (MLE) for the localization of VLC units in the proposed cooperative scenario. To tackle the nonconvex structure of the MLE, we adopt a set-theoretic approach by formulating the problem of cooperative localization as a quasiconvex feasibility problem, where the aim is to find a point inside the intersection of convex constraint sets constructed as the sublevel sets of quasiconvex functions resulting from the Lambertian formula. Next, we devise two feasibility-seeking algorithms based on iterative gradient projections to solve the feasibility problem. Both algorithms are amenable to distributed implementation, thereby avoiding high-complexity centralized approaches. Capitalizing on the concept of quasi-Fejér convergent sequences, we carry out a formal convergence analysis to prove that the proposed algorithms converge to a solution of the feasibility problem in the consistent case. Numerical examples illustrate the improvements in localization performance achieved via cooperation among VLC units and evidence the convergence of the proposed algorithms to true VLC unit locations in both the consistent and inconsistent cases.

High-quality reconstruction of four-dimensional cone beam CT from motion registration prior image

Four-dimensional cone beam CT (4D-CBCT) imaging can provide accurate location information of real-time breathing for imaging-guided radiotherapy. How to improve the accuracy of 4D-CBCT reconstruction image is a hot topic in current studies. PICCS algorithm performs remarkably in all 4D-CBCT reconstruction algorithms based on CS theory. The improved PICCS algorithm proposed in this paper improves the prior image on the basis of the traditional PICCS algorithm. According to the location information of each phase, the corresponding prior image is constructed, which completely eliminates the motion blur of the reconstructed image caused by the mismatch of the projection data. Meanwhile, the data fidelity model of the proposed method is consistent with the traditional PICCS algorithm. The experimental results showed that the reconstructed image using the proposed method had a clearer organization boundary compared with that of images reconstructed using the traditional PICCS algorithm. This proposed method significantly reduced the motion artifact and improved the image resolution.

Navigation Algorithm Applied to the Bit of Underwater Horizontal Directional Drilling in a Vibration Environment

Abstract In its working state, the bit used in underwater horizontal directional drilling (UHDD) produces a high-frequency vibration that can affect accuracy of navigation. We designed a low-pass filter with linear phase on the basis of spectral characteristics of sensor data. To improve further the accuracy of navigation, we deduce the state error model on the basis of the random walk model of acceleration and angular velocity. We use an indirect Kalman filter algorithm to correct the attitude and position of the bit used with UHDD on the basis of observations coming from our working state analysis. Last, we derive a complete navigation algorithm function, including the acquisition method of steady-state component of acceleration and angular velocity. Experimental results show that the navigation algorithm proposed in this paper can obtain accurate attitude and location information of the bit in a vibration environment.

Robust Localization for Cognitive IoT via the Mobile Anchor Node Based on the Diameter-Varying Spiral Line

Research on IoT that merely aims at connecting and communicating is about to past. Thereafter, general objects should have the capability to learn, think, and understand both physical and social areas by themselves. Cognitive Internet of Things (CIoT) attempts to empower the current IoT with a “brain” for high-level intelligence, requiring networks to have the ability to bridge the physical and social worlds. This attempt means matching equipment and resources with people and their behavior. Therefore, accurate location information is crucial for equipment connecting to CIoT. This endeavor sets a higher requirement for the localization technology of wireless sensor networks in terms of accuracy, energy, and efficiency compared with that in the past. In this paper, we propose an efficient and accurate mobile anchor node assisted localization algorithm for WSNs based on diameter-varying spiral line (LDVSL), which broadcasts coordinates of the anchor node to assist localizing unknown sensor nodes. The proposed algorithm has two main innovations. First, we obtain the mobile anchor node position through a time and angle mechanism instead of GPS, given the unique characteristics of the diameter-varying spiral line. Second, the linear fitting method is adapted to select the key virtual node, which has the real maximum received signal strength indicator. Simulations indicate that the proposed LDVSL algorithm outperforms other similar algorithms in terms of average localization error and positionable node ratio. The simulations also show that the LDVSL is not affected by obstacles seriously and has good robustness. The LDVSL has a wide prospect of application in CIoT.

On the Performance of AoA–Based Localization in 5G Ultra–Dense Networks

Cellular systems are undergoing a transformation toward the fifth generation (5G). Envisioned applications in 5G include intelligent transport system (ITS), autonomous vehicles, and robots as a part of future roads, factories, and society. These applications rely to a great extent on accurate and timely location information of connected devices. This paper proposes a practical scheme for acquiring precise and timely position information by means of a user–centric ultra–dense network (UDN) architecture based on an edge cloud. The considered solution consists of estimating and tracking the azimuth angle–of–arrival (AoA) of the line–of–sight (LoS)–path between a device and multiple transmission–reception points (TRPs), each having a uniform linear antenna array (ULA). AoA estimates from multiple TRPs are fused into position estimates at the edge cloud to obtain timely position information. The extensive measurements have been carried out using a proof–of–concept software–defined–radio (SDR) testbed in order to experimentally assess the achievable positioning accuracy of the proposed architecture. A realistic UDN deployment scenario has been considered in which TRPs consist of antenna arrays mounted on lamp posts. Our results show that practical UDNs can provide sub–meter positioning accuracy of mobile users by employing ULAs with at least four antennas per TRP and by taking into account the non–idealities of the ULAs' phase and magnitude response.

Indoor Region Localization With Asynchronous Sensing Data: A Bayesian Probabilistic Model

Obtaining accurate location information of tracked objects is the cornerstone of providing high-quality location-based services (LBSs). Recently, passive localization has become an increasingly important research theme, attracting the attention from both academic and industrial communities. However, existing Wi-Fi fingerprinting-based passive localization methods mainly focus on the indoor localization problem and require Wi-Fi detection equipment to scan wireless communications simultaneously. In this paper, we propose the concept of indoor region localization, which aims at estimating the region where an object stayed for a given time interval. Moreover, a Bayesian probabilistic model is presented to cope with this issue. The key novelty of our method lies in that it allows Wi-Fi detection equipment to behave in an asynchronous fashion, which eliminates synchronization concerns when implementing the positioning system. Specifically, it first introduces a time window for each timestamp, and then aggregates all the sensing records in the given time window to obtain an RSSI measurement vector. Afterward, a kernel function is utilized to measure the conditional probability with respect to every fingerprint, and all these probabilities are combined to obtain the posterior probabilities corresponding to different regions. Extensive experiments are conducted on both public and proprietary data sets and our method significantly outperforms all the comparing algorithms in terms of each evaluation metric.

Edge-empowered accurate urban vehicle localization with cellular-aware trajectories

Acquiring accurate vehicle location information in urban settings is very challenging due to the complexity of urban environments. In this paper, we propose a novel scheme, called UPS, to tackle urban vehicle localization problem. After extensive empirical study, we find that GSM power spectrogram collected over a distance has ideal temporal–spatial characteristics for fingerprinting. Encouraged by this observation, UPS tries to utilize the geographical trajectory and the associated GSM power spectrogram information of a moving vehicle to identify its location with reference to a map. To this end, two appealing techniques, i.e., online vehicle localization and GSM map construction, are elegantly integrated. With the former, a vehicle can accurately fix its location under complex urban environments. With the latter, a reliable metropolitan-scale GSM power map can be cost-efficiently built at edges, leveraging the strong power of crowdsourcing. By design, UPS is light-weight, requiring only a minimum hardware deployment. We implement a prototype system to validate the feasibility of the UPS design. Furthermore, we conduct extensive trace-driven simulations and results show that UPS can work stably in various urban settings and achieve an accuracy of 5.3 m with a 90% precision, overwhelming the performance of GPS by five times.

3D Printing Endobronchial Models for Surgical Training and Simulation

Lung cancer is the leading cause of cancer-related deaths. Many methods and devices help acquire more accurate clinical and localization information during lung interventions and may impact the death rate for lung cancer. However, there is a learning curve for operating these tools due to the complex structure of the airway. In this study, we first discuss the creation of a lung phantom model from medical images, which is followed by a comparison of 3D printing in terms of quality and consistency. Two tests were conducted to test the performance of the developed phantom, which was designed for training simulations of the target and ablation processes in endochonchial interventions. The target test was conducted through an electromagnetic tracking catheter with navigation software. An ablation catheter with a recently developed thermochromic ablation gel conducted the ablation test. The results of two tests show that the phantom was very useful for target and ablation simulation. In addition, the thermochromic gel allowed doctors to visualize the ablation zone. Many lung interventions may benefit from custom training or accuracy with the proposed low-cost and patient-specific phantom.

On-Line RSS Calibration Method Based on Partial Errors-in-Variables Model

The received signal strength-based localization with the propagation model is a simple and inexpensive method to estimate the location in an indoor environment. However, this approach gives inaccurate results due to environmental and situation-dependent noisy signal. Therefore, it is important to calibrate parameters of the propagation model in an on-line manner. In wireless sensor networks (WSNs), there exist not only uncertainties such as inherent measurement noise and inaccurate location of unknown nodes, but also certainties such as accurate location information of anchor nodes. By observing that both such certainty and uncertainty of the WSN can be appropriately formulated under the partial errors-in-variables (EIV) model, the proposed calibration method is derived under the partial EIV model to estimate the model parameters with a high accuracy. The solution can be obtained in a computationally efficient way by applying the weighted total least square method. Various simulations and real experiment are conducted to illustrate the performance of the proposed partial EIV-based calibration method, and we conclude that its performance is close to the results of the true parameters under various conditions.

Automatic pavement defect detection using 3D laser profiling technology

Abstract Asphalt pavement defects e.g. cracks, potholes, rutting, often cause significant safety and economic problems, thus, to automatic detect these defects is vital for pavement maintaining and management. The fact that 3D defect detection methods is superior to traditional 2D methods and manual survey methods in terms of accuracy and comprehensiveness has been widely recognized. Based on 3D laser scanning pavement data, an automatic defect detection method is proposed to detect pavement cracks and pavement deformation defects information simultaneously in this paper. Specifically, a sparse processing algorithm for 3D pavement profiles is first designed to extract crack candidate points and deformations support points, these processing is based on the assumption that the cracks are microscopic local defects while deformations are macroscopic defects in profiles. Then, the crack information can be detected by combining the extracted candidate points and an improved minimum cost spanning tree algorithm. On the other hand, the deformation depth information is acquired based on the profile standard contours which are constructed by profile envelopes and deformation support points, the accurate location and classification information of deformation defects can be obtained by the regional depth property. Experimental tests were conducted using real measured 3D pavement data containing two categories of defects. The experimental results showed that, based on the 3D laser scanning data, the proposed method can effectively detect typical cracks under different road conditions and environments, with the detection accuracy above 98%. Furthermore, different types of deformation defects including potholes, rutting, shoving, subsidence, can also be accurately detected with location error less than 8.7%.