Proposed Localization Scheme Research Articles

TDoA positioning with data-driven LoS inference in mmWave MIMO communications

Location awareness is an essential feature to support various mobile services, and cooperative positioning with channel state information (CSI) in millimeter wave multiple-input multiple-output (MIMO) networks is promising. Meanwhile, strong non-line of sight (NLoS) effects in outdoor scenarios severely reduce the model-based localization accuracy, and existing fingerprint-based methods have a critical requirement for labeled data and lack scalability. To overcome these shortcomings, we propose a hybrid data-and-model driven cooperative localization scheme using uplink wideband MIMO CSI at multiple base stations (BS) as measurements. First, we design a data-driven line of sight (LoS) inference module, realized by a transformer with an attention mechanism, to estimate the statistics of LoS arrival times. Each BS is equipped with a module, and the counterpart LoS inferences are simultaneously and distributedly performed. Second, we locate the mobile user by a model-driven approximate maximum likelihood time difference of arrival algorithm with the estimated statistics. Experiment results in urban scenarios show that the proposed localization scheme is extensible and also robust to varying number of channel paths and combination of BS measurements. Considering a small training dataset, the proposed scheme significantly outperforms the data- and model-driven baselines in NLoS scenarios, in terms of positioning accuracy.

Computer Vision-Aided mmWave UAV Communication Systems

Unmanned aerial vehicle (UAV) communication systems usually operate in harsh scenarios, which require accurate information about the topology and wireless channel to achieve the desired transmission performance. Therefore, when millimeter wave (mmWave) communication with its intrinsic line-of-sight (LoS) condition is adopted, accurate target localization is essential to determine the spatial relationship between the UAV and the grounded receivers (Rxs). In this paper, a computer-vision (CV)-aided jointly optimization scheme of flight trajectory and power allocation is designed for mmWave UAV communication systems by utilizing the visual information captured via cameras equipped at the UAV. Compared with traditional schemes, the implementation cost and overhead can be greatly saved as no radio frequency transmissions are required in the proposed localization scheme. In addition, the transmit power at the UAV is jointly optimized with its flight trajectory in two different cases. Finally, simulation results are presented to demonstrate the efficiency of the proposed schemes.

Novel Robust Indoor Device-Free Moving-Object Localization and Tracking Using Machine Learning With Kalman Filter and Smoother

A novel robust device-free moving-object localization and tracking approach incorporating a machine-learning classifier with both Kalman filter and Kalman smoother is proposed in this work. The received signal-strength indicator (RSSI) maps for an arbitrary indoor geometry are simulated by the prevalent Feko channel-propagation emulator. Such instantaneous RSSIs of WiFi signals collected by different receivers indoors are acquired as a feature vector. The indoor geometry is first partitioned into a number of equisized zones, and then the underlying localization problem can be converted to the multiclassification problem. The RSSI feature vectors are collected as the training data for the gradient boosting decision-tree classifier. We propose a novel online tracking and offline localization approach using the advanced machine-learning technique regularized by Kalman filter and Kalman smoother, respectively. Simulation results demonstrate that our proposed new localization and tracking approach leads to the highest localization-accuracy of 73.1% over four test trajectories as the room is partitioned into 16 equisized zones. Moreover, our proposed new method also reaches the smallest average Euclidean-distance error of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1.33{\kern0.99594pt}\text{m}$</tex-math></inline-formula> . Our proposed novel indoor localization and tracking scheme would be very promising and convenient as it can well serve for devices free indoor surveillance in the future.

Moving Aerial Anchors Assisted Network Localization

To provide effective wireless connectivity, the use of aerial vehicles has been proposed as a promising solution in a wide variety of applications. In some of these applications, however, to achieve desirable performance, knowing the location of users may be a necessity. One promising approach for solving the problem of obtaining user locations is through the locations of some nodes (called anchors) and measuring the distance between connected nodes in the network. In this paper, we propose prominent techniques to improve the performance of the localization using multiple moving aerial anchors (MAA). In using MAAs as anchor nodes, the anchor nodes in our scenario have movement capability. This provides us with more flexibility to enhance the accuracy of the localization with taking into consideration the power consumption of MAAs. In fact, we propose an MAA placement method by which each user can be connected to at least one MAA while MAAs communicate with as small as possible power consumption. Then, we propose a path planning for MAAs by which distances between users and MAAs can be measured in order to estimate the locations of users. Our simulation results show that our proposed localization scheme can provide highly accurate location estimations.

A fixed point multi-scale finite volume method: Application to two-phase incompressible fluid flow through highly heterogeneous porous media

An enhanced Multi-scale Finite Volume (MsFV) method is proposed to efficiently simulate two phase flow through highly heterogeneous porous media. Here, the accuracy of the MsFV method is significantly improved by enhancing its so-called basis functions, while its computational cost remains in the same order of the basic MsFV method. First, a fixed point is defined along each edge of local problems producing the basis functions and a proper basis function value at this point is estimated based on the local absolute permeability data. Then, the variable boundary condition is independently calculated for both sides of each fixed point. The proposed numerical method is validated by using the analytic solution of a heterogeneous problem. In addition, the convergence of the MsFV method is discussed. Considering highly heterogeneous permeability domains derived from 35 top layers of the tenth SPE comparative study problem, the accuracy of different localization schemes is compared for a set of imbibition problems with different global boundary conditions and mobility ratios. Numerical results have indicated that the overall accuracy of multi-scale velocity solutions is increased noticeably (up to 45%) by using the proposed localization scheme in the MsFV method.

A Semi-Supervised 3D Indoor Localization Using Multi-Kernel Learning for WiFi Networks.

Indoor localization is an important issue for indoor location-based services. As opposed to the other indoor localization approaches, the radio frequency (RF) based approaches are low-energy solutions with simple implementation. The kernel learning has been used for the RF-based indoor localization in 2D environment. However, the kernel learning has not been used in 3D environment. Hence, this paper proposes a multi-kernel learning scheme for 3D indoor localization. Based on the signals collected in the area of interest, the WiFi signals with better quality and closer to the user are selected so as to reduce the multipath effect and the external interference. Through the construction of multi-kernel, the localization accuracy can be improved as opposed to the localization based on the single kernel. We build multiple kernels to get the user’s location by collecting wireless received signal strengths (RSS) and signal-to-noise ratios (SNR). The kernel learning maps data to high dimension space and uses the optimization process to find the surface where the data are mapped. By multi-kernel training, the surface is fine-tuned and eventually converges to form the location database during the mapping process. The proposed localization scheme is verified by the real RSS and SNR collected from multiple wireless access points (AP) in a building. The experimental results verify that the proposed multi-kernel learning scheme performs better than the multi-DNN scheme and the existing kernel-based localization schemes in terms of localization accuracy and error in 3D indoor environment.

Design and implementation of a novel real‐time unmanned aerial vehicle localization scheme based on received signal strength

AbstractTo accurately localize unmanned aerial vehicles (UAVs) is one of the key issues to deal with the security threats caused by UAVs. Thus, this article proposes a UAV localization scheme that utilizes the area grid quantization and transmit power statistical calibration techniques, in which the location and transmit power of the UAV are unknown. Firstly, the adaptive discrete particle swarm optimization (ADPSO) algorithm is used to find the global optimal solution to the localization problem. A grid quantization method is proposed to discretize the value space of the traditional PSO algorithm to reduce the estimated localization time. An adaptive factor is also introduced to adjust the value of power for each iteration in the discrete particle swarm optimization (DPSO) algorithm in order to find the global optimal solution efficiently. Secondly, the fixed transmit power range is adopted to calibrate the estimated value of transmit power and the corresponding location. The prototype system comprises four anchor nodes based on a universal software radio peripheral radio frequency (USRP RF). Finally, field experiments are carried out to demonstrate the effectiveness of the proposed localization scheme.

A Localization Scheme for Underwater Acoustic Wireless Sensor Networks using AoA

Background: Underwater Acoustic Sensor Networks (UWASNs) have been proposed for the hard oceanographic application where human efforts are not possible. In UWASNs localization is a challenging task due to unavailability of Global Positioning System (GPS), high propagation delay and the dynamic mobility of the sensor nodes due to ocean dynamics. Objective: To address the issues related to the localization of the sensors for the network deployed under water. This paper presents a localization scheme specially designed for UWASNs. Methods: In this paper, we propose a localization scheme using Angle-of-Arrival (AoA) technique for UWASNs. The proposed localization scheme is divided into Angle estimation phase, Projection Phase and Localization phase. The angle estimation phase estimations the angle of the signal arriving at the sensors. The projection phase converts the 3-Dimensional localization problem in equivalent 2-Dimensional by projecting the sensor nodes to a virtual projection plane. In the localization phase, the position of the sensor nodes is estimated based on the Angle-of-Arrival and distance from neighboring nodes information. Results: The simulation result shows that the proposed scheme provides a high localization ratio and Localization coverage with less energy consumption. Conclusion: A distributed range-based localization scheme for UWASNs using AoA technique is presented. The localization scheme projects the sensor nodes to a virtual plane and calculates the angle of signals initiated by the reference nodes. The scheme provides a great success in node localization and network coverage.

Time-Synchronization Free Localization Scheme with Mobility Prediction for UAWSNs

Background: Underwater Acoustic Wireless Sensor Network supports a lot of civil and military applications. It has come out as an effective tool to explore the ocean area of the earth. Sensor deployed underwater can help to relate the events occurring underwater with the rest of the world. To achieve the goal, the information gained from the sensors needs to be tagged with their real-time locations. Objective: To study the effect of the mobility of the sensor nodes on the accuracy of the location estimation during the localization period and develop a localization scheme which can give an accurate result by predicting the mobility behavior of the sensors. Methods: In this paper, a time-synchronization free localization scheme for underwater networks has been presented. The scheme employs a mobile beacon in the network to move vertically and broadcast the beacon messages. Results: The performance evaluation shows that the scheme reduces the error in location estimation caused by the mobility of the sensors by predicting their further location according to the mobility pattern of the sensor node. In an existing localization scheme, nodes are localized without time-synchronization but the scheme does not consider the mobility of the senor between the reception of two messages. The result of shows that the proposed localization scheme has achieved success in reducing the localization error by introducing the mobility behavior of the sensors in the existing localization scheme. Conclusion: localization scheme without a need of time-synchronization is presented. The main reason of the inaccuracy of a localization scheme is the mobility of the sensor node during the range estimation. The accuracy of a localization scheme can be improved by prediction the mobility pattern of the sensor during a localization period.

Robust Lidar-Based Localization Scheme for Unmanned Ground Vehicle via Multisensor Fusion.

This article proposes a robust and precise localization scheme for unmanned ground vehicle (UGV) in global positioning system (GPS)-denied and GPS-challenged environments via multisensor fusion approach. The localization scheme is proposed to be under an available point-cloud map. First, initialization in localization module is designed to calculate the initial position of UGV in map using the Gaussian projection approach and obtain the frame transformation between the 3-D lidar and the inertial measurement unit (IMU). Second, the best alignment between each scan frame and the available submap is obtained, and the pose of vehicle relative to the origin of map is calculated. Third, the precise pose of UGV is well predicted by integrating the data from 3-D lidar and IMU. Fourth, in order to visually verify the proposed localization scheme, the motion of vehicle is visualized by designing the visualization module. Note that the preprocessing module aims to process the raw scan data. The availability of our proposed localization scheme is verified by conducting experiments in our campus.

Reverse localization on composite laminates using attenuated strain wave

Abstract Carbon Fiber Reinforced Polymer (CFRP) composite materials have been widely used in high-tech fields because of their outstanding physical properties. However, barely visible impact damage can be introduced by low velocity impact, which might bring tremendous risk. Monitoring impact forces is essential for the integrity and reliability of CFRP structures. In this paper, a reverse localization scheme on CFRP composite materials is developed. The scheme is based on a predictive mode of attenuated strain wave. The signals induced by low velocity impact are obtained by strain gauges. The proposed localization scheme has been verified by experimental tests of the CFRP plates with symmetrical stacking [0/45/90/−45]2s The errors occurring in the test can be accepted for engineering application. The calculated impact locations are generally in consistent with the testing impact locations.

Hybrid area exploration–based mobility‐assisted localization with sectored antenna in wireless sensor networks

SummaryIn common practice, sensor nodes are randomly deployed in wireless sensor network (WSN); hence, location information of sensor node is crucial in WSN applications. Localization of sensor nodes performed using a fast area exploration mechanism facilitates precise location‐based sensing and communication. In the proposed localization scheme, the mobile anchor (MA) nodes integrated with localization and directional antenna modules are employed to assist in localizing the static nodes. The use of directional antennas evades trilateration or multilateration techniques for localizing static nodes thereby resulting in lower communication and computational overhead. To facilitate faster area coverage, in this paper, we propose a hybrid of max‐gain and cost‐utility–based frontier (HMF) area exploration method for MA node's mobility. The simulations for the proposed HMF area exploration–based localization scheme are carried out in the Cooja simulator. The paper also proposes additional enhancements to the Cooja simulator to provide directional and sectored antenna support. This additional support allows the user with the flexibility to feed radiation pattern of any antenna obtained either from simulated data of the antenna design simulator, ie, high frequency structure simulator (HFSS) or measured data of the vector network analyzer (VNA). The simulation results show that the proposed localization scheme exhibits minimal delay, energy consumption, and communication overhead compared with other area exploration–based localization schemes. The proof of concept for the proposed localization scheme is implemented using Berkeley motes and customized MA nodes mounted with indigenously designed radio frequency (RF) switch feed network and sectored antenna.

Damage Localization of Stacker’s Track Based on EEMD-EMD and DBSCAN Cluster Algorithms

The vibration and friction triggered by the long-term operation of stacker inevitably lead to the damage problem of stacker’s track. In order to ensure the stable operation of the stacker, a novel scheme is proposed to address an accurate damage localization problem of the stacker’s track in industrial environment based on ensemble empirical mode decomposition-empirical mode decomposition (EEMD-EMD) and density-based spatial clustering of applications with noise (DBSCAN). First, the original electric current signal with unbalanced distribution is processed by fixed-interval smoothing and cubic Hermite interpolation algorithms. Second, the EEMD-EMD method, which solves the mode-mixing problem, is adopted to decompose the preprocessed signal into well-defined intrinsic mode functions (IMFs). Third, based on Hilbert–Huang transform (HHT) of the IMFs, the preprocessed signal and the difference of average instantaneous amplitudes in two adjacent time points are considered as the 2-D feature vector input of the DBSCAN algorithm. As such, damage locations of the stacker’s track are detected by means of the outliers that are obtained by the DBSCAN algorithm. The efficiency of the proposed localization scheme and its superiority over the existing cooperative damage localization methods, namely, the box-plot method, the pulse coupling neural network (PCNN) and wavelet transform (WT) theory method, and the box-plot and WT method, are verified through experiments using the data provided by State Grid Measuring Center of China.

Correlation-Based Localization Effective in Ensemble-Based History Matching

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 191305, “Correlation-Based Adaptive Localization for Ensemble-Based History Matching Applied to the Norne Field Case Study,” by Xiaodong Luo, SPE, Rolf Lorentzen, SPE, Randi Valestrand, SPE, and Geir Evensen, SPE, International Research Institute of Stavanger, prepared for the 2018 SPE Norway One Day Seminar, Bergen, Norway, 18 April. The paper has been peer reviewed and published in the October 2018 SPE Journal. Ensemble-based methods are considered to be state-of-the-art history-matching algorithms. However, in practice, they often suffer from ensemble collapse, a phenomenon that deteriorates history-matching performance. An ensemble history-matching algorithm is equipped customarily with a localization scheme to prevent ensemble collapse. To enhance the applicability of localization to various history-matching problems, the authors adopt an adaptive localization scheme that exploits the correlations between model variables and observations. Introduction In the current work, the authors focus on adopting an efficient adaptive localization scheme, previously established in the literature, for the full Norne Field case study. The adaptive localization scheme exploits the information of sample correlation coefficients between an ensemble of model variables and the corresponding realizations of simulated observations. The adaptive localization scheme uses a data-selection procedure; however, instead of physical distances between the locations of model variables and observations being used for data selection, the magnitudes of the sample correlation coefficients are used for data selection through a hard-thresholding strategy (i.e., keep or kill). To conduct data selection in the adaptive localization scheme, one specifies a positive correlation-threshold value. For a given observation, if the magnitude of the sample correlation coefficient between a model variable and the simulated observation is greater than the threshold value, then the observation will be used to update that model variable. Otherwise, one discards the observation in the update of that model variable. This described data-selection procedure essentially means that a given model variable is updated using only the observations that have significant correlations with the model variable. The rationale behind this hard-thresholding strategy is the interpretation of the magnitude of the correlation coefficient as a measure to detect the causal relation between a model variable and an observation, and the effect is the suppression of spurious correlations caused by a finite sample size. Correlation-based localization can overcome or alleviate the issues arising in distance-based localization, such as the use of nonlocal and time-lapse observations, the need of physical locations for model variables and measurements, and the different degrees of correlations or sensitivities of model variables to observations. Because data selection depends on the magnitudes of sample correlation coefficients between model variables and the corresponding simulated observations, the measurements need not have associated physical locations. Model variables are thus selected by using those observations that exhibit strong-enough correlations, regardless of the physical distances between observations and model variables. As a result, correlation-based localization can be used to localize nonlocal observations. The changes of correlations caused by the effect of time-lapse observations or different types of model variables will be taken into account automatically in correlation-based localization, and this makes the proposed localization scheme more-adaptive and more-flexible in various situations.

GPS-free vehicular localization system using roadside units with directional antennas

The success of dedicated short-range communications (DSRC) applications depends on an accurate knowledge of the positions of vehicles within the network. At present, vehicle localization is generally performed using some form of vehicle-mounted global positioning system (GPS). However, GPS signals may not be always available. Various GPS-free vehicle localization techniques using the ranging information, the prior knowledge of the vehicle's position, or the special hardware have been proposed for enhancing the performance of traditional GPS-based methods. Different from the previous approaches, we propose a GPS-free vehicle localization system using roadside units (RSUs) with directional antennas without specific hardware support on the vehicle and the assumption of the prior knowledge of the vehicle. In the proposed approach, each vehicle determines its position using the information contained within beacon messages transmitted by neighboring RSUs deployed along the road only. The performance of the proposed localization scheme is evaluated by ns-2 simulations and is compared with those of recent GPS-free and GPS-assisted localization systems. The simulation results show that the proposed localization scheme achieves a higher positioning accuracy than the existing GPS-free and GPS-assisted schemes. The feasibility of the proposed system for practical applications is further investigated experimentally. The experimental results for the positioning accuracy are consistent with those obtained from the ns-2 simulations.

Correlation-Based Adaptive Localization for Ensemble-Based History Matching: Applied To the Norne Field Case Study

Summary Ensemble-based methods are among the state-of-the-art history-matching algorithms. However, in practice, they often suffer from ensemble collapse, a phenomenon that deteriorates history-matching performance. It is customary to equip an ensemble history-matching algorithm with a localization scheme to prevent ensemble collapse. Conventional localization methods use distances between the physical locations of model variables and observations to modify the degree of the observations’ influence on model updates. Distance-based localization methods work well in many problems, but they also suffer from dependence on the physical locations of both model variables and observations, the challenges in dealing with nonlocal and time-lapse measurements, and the nonadaptivity to handling different types of model variables. To enhance the applicability of localization to various history-matching problems, we adopt an adaptive localization scheme that exploits the correlations between model variables and simulated observations. We elaborate how correlation-based adaptive localization can overcome or mitigate issues arising in conventional distance-based localization. To demonstrate the efficacy of correlation-based adaptive localization, we adopt an iterative ensemble smoother (iES) with the proposed localization scheme to history match the real production data of the Norne Field model, and we compare the history-matching results with those obtained by using the iES with distance-based localization. Our study indicates that when compared with distance-based localization, correlation-based localization not only achieves close or better performance in terms of data mismatch, but also is more convenient to use in practical history-matching problems. As a result, the proposed correlation-based localization scheme might serve as a viable alternative to conventional distance-based localization.

Correlation-Based Adaptive Localization With Applications to Ensemble-Based 4D-Seismic History Matching

Summary Ensemble-based history-matching methods have received much attention in reservoir engineering. In real applications, small ensembles are often used in reservoir simulations to reduce the computational costs. A small ensemble size may lead to ensemble collapse, a phenomenon in which the spread of the ensemble of history-matched reservoir models becomes artificially small. Ensemble collapse is not desired for an ensemble-based history-matching method because it not only deteriorates the capacity in uncertainty quantification, but also forces the ensemble-based method to later stop updating reservoir models. In practice, distance-based localization is thus introduced to tackle ensemble collapse. Distance-based localization works well in many problems. However, one prerequisite in using distance-based localization is that the observations have associated physical locations. In certain circumstances with complex observations, this may not be true, and it thus becomes challenging to apply distance-based localization. In this work, we propose a correlation-based adaptive localization scheme that does not rely on the physical locations of the observations. Instead, we use the spatial distributions of the correlations between model variables and the corresponding simulated observations. In the course of history matching, we update model variables by only using the observations that have relatively high correlations with them, while excluding those that have relatively low correlations. This is equivalent to introducing a data-selection procedure to the history-matching algorithm. As a result, the threshold values for data selection play an essential role in the proposed adaptive localization scheme, and we develop both ideal and practical approaches to the choices of the threshold values. We demonstrate the efficacy of the proposed localization scheme using seismic history-matching problems—one 2D and one 3D—in which ensemble collapse is severe in the presence of large amounts of observational data, but distance-based localization may not be applicable because of the lack of physical locations of the seismic data in use. In contrast, correlation-based localization works well to prevent ensemble collapse and also renders good history-matching results. We also note some practical conveniences of the proposed localization scheme, including the applicability to nonlocal observations, the relative simplicity in implementation, the transferability of the same codes among different (either 2D or 3D) case studies, and the adaptivity to different types of observations and petrophysical parameters.

Localization of impact on composite plates based on integrated wavelet transform and hybrid minimization algorithm

Carbon fiber reinforced polymer (CFRP) composite material has been widely used in engineering structures due to its outstanding physical properties. However, the susceptibility to internal damage caused by the low-velocity impact event is a critical problem and monitoring of impact is essential for the integrity and reliability of CFRP structures. An integrated impact localization scheme is proposed and validated in this paper. The scheme is based on the arrival time obtained by wavelet transform (WT) analysis of signals received by dynamic strain gauges, triangulation localization technique together with a proposed hybrid particle swarm optimization (PSO) and genetic algorithm (GA) algorithm. The proposed localization scheme is validated by experimental testing of two CFRP plates with cross-ply [0/90]4s and symmetrical stacking [0/45/90/−45/0/90/−45/0]s respectively. The calculated impact locations are generally in agreement with the testing impact locations with acceptable accuracy for engineering application.

Multi-objective optimization for a reliable localization scheme in wireless sensor networks

In many wireless sensor network (WSN) applications, the information transmitted by an individual entity or node is of limited use without the knowledge of its location. Research in node localization is mostly geared towards multi-hop range-free localization algorithms to achieve accuracy by minimizing localization errors between the node's actual and estimated position. The existing localization algorithms are focused on improving localization accuracy without considering efficiency in terms of energy costs and algorithm convergence time. In this work, we show that our proposed localization scheme, called DV-maxHop, can achieve good accuracy and efficiency. We formulate the multi-objective optimization functions to minimize localization errors as well as the number of transmission during localization phase. We evaluate the performance of our scheme using extensive simulation on several anisotropic and isotropic topologies. Our scheme can achieve dual objective of accuracy and efficiency for various scenarios. Furthermore, the recently proposed algorithms require random uniform distribution of anchors. We also utilized our proposed scheme to compare and study some practical anchor distribution schemes.

A finite point process approach to multi-target localization using transient measurements

A finite point process approach to multi-target localization from a transient signal is presented. After modeling the measurements as a Poisson point process, we propose a twofold scheme that includes an expectation maximization algorithm to estimate the target locations for a given number of targets and an information theoretic algorithm to select the number of targets. The proposed localization scheme does not require explicitly solving the data association problem and can account for clutter noise as well as missed detections. Although point process theory has been widely utilized for sequential tracking of multiple moving targets, the application of point process theory for multi-target localization from transient measurements has received very little attention. The optimal subpattern assignment metric is used to assess the performance and accuracy of the proposed localization algorithm. Implementation of the proposed algorithm on synthetic data yields desirable results. The proposed algorithm is then applied to the multi-shooter localization problem using acoustic gunfire detection systems.