Localization Accuracy Requirements Research Articles

Continuous TOA Measurement Based Single-Anchor Indoor Localization

On the premise of guaranteeing high-quality measurement data, active wireless localization (A-WL) locates the target by at least three anchor nodes (ANs) with known coordinates, while passive wireless localization (P-WL) requires more ANs. In addition, P-WL requires a cumbersome training process to maintain the robustness in application scenarios as much as possible. However, the optimal deployment of ANs is not an easy task, and the application scenario will not remain unchanged. Therefore, a lightweight localization system that satisfies the localization accuracy requirements and can be quickly deployed should be one of the preferred solutions for WL. In this paper, we propose a single-anchor localization system based on continuous time-of-arrival (TOA) measurement. Specifically, we fix the AN on the blade with uniform rotation, construct the time-length distribution map by using the cyclically varying TOA caused by blade rotation, and intercept it according to the rotation period. We extract the change angle and distance information from the intercepted data in real time to achieve single-anchor localization. Meanwhile, we also construct a support vector machine classifier to identify the line-of-sight state of each interception period to assist the angle estimation in harsh environments and further improve the adaptability of the system. Experimental results show that the line-of-sight recognition accuracy of higher than 92% under continuous movement, and the average localization error of less than 0.6m. The average localization error is less than 0.3m in the mixed motion state.

A Low-Cost and Robust Multi-Sensor Data Fusion Scheme for Heterogeneous Multi-Robot Cooperative Positioning in Indoor Environments

The latest development of multi-robot collaborative systems has put forward higher requirements for multi-sensor fusion localization. Current position methods mainly focus on the fusion of the carrier’s own sensor information, and how to fully utilize the information of multiple robots to achieve high-precision positioning is a major challenge. However, due to the comprehensive impact of factors such as poor performance, variety, complex calculations, and accumulation of environmental errors used by commercial robots, the difficulty of high-precision collaborative positioning is further exacerbated. To address this challenge, we propose a low-cost and robust multi-sensor data fusion scheme for heterogeneous multi-robot collaborative navigation in indoor environments, which integrates data from inertial measurement units (IMUs), laser rangefinders, cameras, and so on, into heterogeneous multi-robot navigation. Based on Discrete Kalman Filter (DKF) and Extended Kalman Filter (EKF) principles, a three-step joint filtering model is used to improve the state estimation and the visual data are processed using the YOLO deep learning target detection algorithm before updating the integrated filter. The proposed integration is tested at multiple levels in an open indoor environment following various formation paths. The results show that the three-dimensional root mean square error (RMSE) of indoor cooperative localization is 11.3 mm, the maximum error is less than 21.4 mm, and the motion error in occluded environments is suppressed. The proposed fusion scheme is able to satisfy the localization accuracy requirements for efficient and coordinated motion of autonomous mobile robots.

A Single-Site Vehicle Positioning Method in the Rectangular Tunnel Environment

Due to the satellite signals are blocked, it is difficult to obtain the vehicle position in the tunnels. We propose a single-site vehicle localization scheme for the rectangular tunnel environment, where most satellite-based positioning methods can not provide the required localization accuracy. In the non-line-of-sight (NLOS) scenarios, we make use of the reflection paths as assistants for vehicle positioning. Specifically, first, the virtual stations are established based on the actual geometrical structure of the tunnel. Second, we use the direction-of-arrival (DOA) and time-of-arrival (TOA) information of reflection paths from two tunnel walls to achieve vehicle positioning. Especially, the Cramer-Rao lower bound (CRLB) of the joint TOA and DOA localization for NLOS propagations in a two-dimensional (2D) space is derived. In addition, based on the localization algorithms with and without filters, we assess the localization performance. In the line-of-sight (LOS) scenarios, we use the LOS path and two reflection paths from the tunnel walls to estimate the vehicle location. First, virtual base stations are established. Second, based on the obtained TOA information, different positioning algorithms are used to estimate the vehicle location. Simulation results illustrate that the proposed positioning approach can provide a small root mean square error. The localization algorithms using filters improve the localization accuracy, compared with the positioning algorithm without using filters, namely, the two-stage weighted least squares (TSWLS) algorithm. Moreover, the Unscented Particle Filter (UPF) algorithm achieves better positioning accuracy than other methods (i.e., Unscented Kalman Filter (UKF), Extended Kalman Filter (EKF), TSWLS algorithms).

Pyramid Indoor Localization System Using Dual-Band Wi-Fi Sensors

Fingerprinting map technology brings tremendous advancements in indoor localization, especially for unconstrained position perception, as it provides a convenient and low-cost way in applicability of mobile phone and smart device. However, since traditional way only includes one fingerprinting database from sensors, the localization task is difficult to balance positioning accuracy and efficiency. Especially when users have prior knowledge, the localization accuracy requirements are generally consistent, and excessive pursuit of accuracy leads to low efficiency. In order to solve this problem, Fingerprinting Pyramid Map (FPM) structure is proposed in this paper, where users can freely choose between localization range and time. Not only that, the signal strength difference measurement between 2.4GHz and 5GHz from two access-point sensors is used as fingerprint data to resolve the issue of device heterogeneity in FPM. Extensive experimental analysis in a university library environment, further confirm that the proposed method accommodates localization efficiency and device heterogeneity, and outperforms other existing methods.

Multiple Random Forests Based Intelligent Location of Single-phase Grounding Fault in Power Lines of DFIG-based Wind Farm

To address the problems of wind power abandonment and the stoppage of electricity transmission caused by a short circuit in a power line of a doubly-fed induction generator (DFIG) based wind farm, this paper proposes an intelligent location method for a single-phase grounding fault based on a multiple random forests (multi-RF) algorithm. First, the simulation model is built, and the fundamental amplitudes of the zero-sequence currents are extracted by a fast Fourier transform (FFT) to construct the feature set. Then, the random forest classification algorithm is applied to establish the fault section locator. The model is resampled on the basis of the bootstrap method to generate multiple sample subsets, which are used to establish multiple classification and regression tree (CART) classifiers. The CART classifiers use the mean decrease in the node impurity as the feature importance, which is used to mine the relationship between features and fault sections. Subsequently, a fault section is identified by voting on the test results for each classifier. Finally, a multi-RF regression fault locator is built to output the predicted fault distance. Experimental results with PSCAD/EMTDC software show that the proposed method can overcome the shortcomings of a single RF and has the advantage of locating a short hybrid overhead/cable line with multiple branches. Compared with support vector machines (SVMs) and previously reported methods, the proposed method can meet the location accuracy and efficiency requirements of a DFIG-based wind farm better.

Three-dimensional Positioning in 3GPP Wireless Networks with Small Cells with Barometric Pressure Sensor

The US federal communications commission announced new location accuracy requirements for emergency calls. To examine these requirements, we first establish a 3GPP system-level simulator and calibrate its performance for radio access technology-dependent techniques. After considering the 3D channel model, we test and conclude that existing technologies fail to satisfy the indoor vertical accuracy requirement. Therefore, we propose a two-step least-square estimator that adopts a barometric pressure sensor (BPS) to measure the altitude above sea level. The use of BPS improves accuracy for both vertical and horizontal aspects. The positioning performance of both outdoor and indoor user equipment (UE) is simulated, and the results demonstrate that 67% of UE can be localized within 18-m horizontal and 3-m vertical accuracy with 10 small cells. The improved vertical accuracy obtained by the proposed method can be beneficial to UAV communication.

Optimal Search Parameters for A Random Pulsed-Point Source with the Required Accuracy

The questions of creating high-speed algorithms for detecting and localizing point sources having a random distribution and manifesting themselves by generating instantaneous delta pulses at random times are described. The search is carried out by a system including one or more receiving devices, and is performed taking into account the requirements for localization accuracy. It is assumed that all receivers have freely tunable viewing windows. The optimal procedure is one that minimizes (in a statistical sense) the average localization time. It is established that even with relatively low requirements for localization accuracy, the optimal procedure consists of several stages (each such stage ends at the moment of the next pulse registration). In this case, it is possible receiving system to miss some pulses generated by the source during the optimal search. In the work, the optimal search parameters are calculated depending on the number of receiving devices and the required localization accuracy. The possibility of using the results in a multidimensional case is shown.

High-precision sound source localization method based on successive approximation in an inhomogeneous sound velocity field

In an inhomogeneous sound velocity field, the conventional time of flight (ToF) method cannot derive an exact distance directly. Therefore, a high-precision sound source localization method is required to address this difficult problem. In this study, we propose a novel high-accuracy successive approximation sound source localization method (SALM) in the inhomogeneous sound velocity field. The ToFs from a sound source to multiple receivers are used to constitute a real time vector. And the ToF from the assumed sound source to each receiver is considered a virtual time vector. It can be calculated according to the distribution of the sound velocity field, regardless of where the sound source is in the field. Then, modified vector and iterative formula can be constructed given the minimum difference between the roles of real and virtual time vectors. By using successive approximation method, the assumed sound source position approaches the actual position systematically until satisfies the localization accuracy requirements. Numerical results indicate that the SALM can considerably improve the localization accuracy. For example, in the 100 m × 100 m horizontal region, the ranging error of the SALM can be lower than 1 mm with more than 20 iterations when the velocity of sound is distributed unevenly within ±5 m/s. Moreover, increasing the number of receivers ensures a significant improvement in the approximate speed. And the localization accuracy can also be improved infinitely by increasing the number of iterations. Finally, the SALM is also applicable to sound source localization in a 3D space.

Оптимальные по быстродействию алгоритмы поиска неизвестного точечного источника со случайной дисциплиной генерации мгновенных импульсов

A speed-optimal strategy has been constructed for the spatial localization of a random point source having a uniform distribution density over the search interval and revealing itself by generating instantaneous pulses (delta functions) at random times. Localization of the source is carried out with the help of a receiver with a free tunable (in time) window. The parameters of the timeoptimal search algorithms calculated in accordance with the proposed scheme (i.e., the optimal number of scanning steps and the size of the receiver's view window on each of them, depending on the required localization accuracy) are summarized in a general table. The obtained results are analyzed, and the optimal parameters of the asymptotic search are established for cases when the required accuracy of localization of an unknown source tends to zero. A further promising area of research is the construction of optimal localization algorithms when the distribution density of a random source differs from the uniform one. Calculation of the parameters of optimal search procedures for the case of simultaneous localization of several pulsed sources is also interesting, as well as the construction of optimal-speed algorithms when localization is carried out by systems that include several receiving devices.

An RSS-based regression model for user equipment location in cellular networks using machine learning

Dissemination of wireless networks and mobile devices, such as smartphones, has motivated the appearance of several types of location-based services. Consequently, the interest in low-complexity cost-effective mobile positioning techniques against the traditional method based on global positioning system has emerged. An interesting alternative is to utilize radio frequency (RF) signals to estimate the location of mobile terminals, as in multilateration and RF fingerprinting techniques. In this context, the objective of this work is to propose a new radio strength signal-based user equipment location method using a machine learning regression-based model to find directly the geographical coordinates of the mobile user in cellular networks. Numerical results show that, in most cases, the proposed method can meet the location accuracy requirements established by the Federal Communications Commission for network-based localization methods.

A Distance Boundary with Virtual Nodes for the Weighted Centroid Localization Algorithm.

In wireless sensor networks, accurate location information is important for precise tracking of targets. In order to satisfy hardware installation cost and localization accuracy requirements, a weighted centroid localization (WCL) algorithm, which is considered a promising localization algorithm, was introduced. In our previous research, we proposed a test node-based WCL algorithm using a distance boundary to improve the localization accuracy in the corner and side areas. The proposed algorithm estimates the target location by averaging the test node locations that exactly match with the number of anchor nodes in the distribution map. However, since the received signal strength has large variability in real channel conditions, the number of anchor nodes is not exactly matched and the localization accuracy may deteriorate. Thus, we propose an intersection threshold to compensate for the localization accuracy in this paper. The simulation results show that the proposed test node-based WCL algorithm provides higher-precision location information than the conventional WCL algorithm in entire areas, with a reduced number of physical anchor nodes. Moreover, we show that the localization accuracy is improved by using the intersection threshold when considering small-scale fading channel conditions.

Small aperture acoustic methods to locate transients

Acoustic microphone arrays have been used in battlefield environments to detect and locate continuous wave targets like helicopters and ground vehicles, and for transient events like gunshots, mortars, rockets and explosions. Depending on the application, source bandwidth, propagation distance, and required localization accuracy, these arrays can vary their sensor separation greatly. Unattended ground sensors, manned- and unmanned-platforms, and Soldier-worn systems can all benefit from very small apertures if performance can be maintained. A commercially available acoustic particle velocity sensor, approximately one-half an inch in diameter, showed good transient localization at a field experiment. Data will also be presented from a small cluster-array of cardioid microphones.

Branch localization method based on the skeleton feature extraction and stereo matching for apple harvesting robot

Aiming at the problem of apple branch obstacle localization in fruit picking process of harvesting robot manipulator, in order to obtain three-dimensional information of the apple branch obstacle, the binocular stereo vision localization method of apple branch obstacle is proposed. Firstly, branch skeleton is extracted by morphological thinning method and then the feature skeleton is obtained after removing the false branch and recovering the occluded branch. After that, the endpoints and bifurcation points regarded as match feature points are extracted from skeleton, and the stereo matching algorithm based on features is adopted. Then, the depth information of branch obstacle is obtained on the basis of triangulation theory. Finally, the experiment results for apple tree branches localization show that the error lies in ±6.2 mm. Moreover, the error is merely ±1.5 mm when the distance between the object and the binocular camera is 1000 mm, which meets with localization accuracy requirements of apple harvesting robot visual system.

Assistive Intelligent Transportation Systems: The Need for User Localization and Anonymous Disability Identification

The main goal of Assistive Technology (AT) is to ensure the functional independence of disabled individuals. This paper proposes the definition of a new concept of AT within the context of the ITS, Assistive Intelligent Transportation System (AITS), analyzing its intrinsic requirements and providing a set of examples. We demonstrate that AITS must localize users with disabilities and identify their specific type of impairment in order to provide an efficient response, and we propose a specific procedure to guarantee anonymity while identifying the type of disability. Moreover, this new type of AT is illustrated by means of a new assistive intelligent pedestrian crossing application that is capable of localizing pedestrians with disabilities, identifying the specific type of impairment and providing an adaptive response to enhance functional capabilities of impaired pedestrians while crossing. By combining stereo-based object detection with radio-frequency identification technology (RFID and Bluetooth Low Energy), a specific solution to the problem of user localization and anonymous disability identification is proposed. Our approach has been validated in a real crosswalk scenario and it may be extended to other types of AITS, depending on the localization accuracy requirements and the range of operation of the specific application.

The Algorithm of the Snail: An Example to Grasp the Window of Opportunity to Boost Big Data

This work explores a new application which can effectively meet different localization accuracy requirements of most data location services studying the interactions between customers and suppliers. It helps to have the status or position of what is sought with respect to an address that summarizes thus a reference point which is the point of research. This proposal explains what snail algorithm is and how we can benefit from using it for the localization of information for business applications especially in the field of analytics. A business application using our algorithm has been developed by the Autour.com company (located in the department of Herault, Montpellier city) to illustrate its feasibility and availability. The results show that our algorithm can improve the localization accuracy.

RSSI-Based Distributed Self-Localization for Wireless Sensor Networks Used in Precision Agriculture

In this paper, we propose a received signal strength indication-based distributed Bayesian localization algorithm based on message passing to solve the approximate inference problem. The algorithm is designed for precision agriculture applications, such as pest management and pH sensing in large farms, where greater power efficiency besides communication and computational scalability is needed but location accuracy requirements are less demanding. Communication overhead, which is a key limitation of popular non-Bayesian and Bayesian distributed techniques, is avoided by a message passing schedule, in which outgoing message by each node does not depend on the destination node, and therefore is a fixed size. Fast convergence is achieved by: 1) eliminating the setup phase linked with spanning tree construction, which is frequent in belief propagation schemes and 2) the parallel nature of the updates, since no message needs to be exchanged among nodes during each update, which is called the coupled variables phenomenon in non-Bayesian techniques and accounts for a significant amount of communication overhead. These features make the proposed algorithm highly compatible with realistic wireless sensor network (WSN) deployments, e.g., ZigBee, that are based upon the ad hoc on-demand distance vector, where route request and route reply packets are flooded in the network during route discovery phase.

Beamforming Design for Joint Localization and Data Transmission in Distributed Antenna System

A distributed antenna system whose goal is to provide data communication and positioning functionalities to mobile stations (MSs) is studied. Each MS receives data from a number of base stations (BSs) and uses the received signal not only to extract the information but to determine its location as well. This is done based on time-of-arrival or time-difference-of-arrival measurements, depending on the assumed synchronization conditions. The problem of minimizing the overall power expenditure of the BSs under data throughput and localization accuracy requirements is formulated with respect to the beamforming vectors used at the BSs. The analysis covers both frequency-flat and frequency-selective channels and accounts for robustness constraints in the presence of parameter uncertainty as well. The proposed algorithmic solutions are based on rank-relaxation and difference-of-convex programming.

Asymmetric Event-Driven Localization Algorithm in Constrained Space

Existing technologies are inapplicable to localization in constrained space, especially when considering environmental factors. These methods with low localization accuracy cannot meet the location requirements in constrained space, for they usually call for lots of computation time and process resources. Moreover, they are easily interfered by environmental factors and attacks from other users. Consequently, in order to improve location accuracy in constrained space, an asymmetric event-driven localization algorithm (AELA) is proposed in this paper, which is based on the combination of event distribution and anchor node achieving a distributed location estimation strategy, so that it can satisfy the localization requirement of constrained space and achieve the high-accuracy localization with a small amount of events and anchor nodes. Meanwhile, to improve the accuracy of the algorithm, a set of candidate events are adopted to prune the event which does not meet location accuracy requirements. We finally perform experiments in indoor corridors, and the results show that the proposed algorithm has higher performances not only on localization accuracy and energy consumption but also on anti-interference ability than RSSI and MSP.

Phase defect mitigation strategy: unveiling fiducial mark requirements on extreme ultraviolet lithography masks

For extreme ultraviolet lithography (EUVL), the fabrication of defect free multi-layered (ML) mask blanks is a challenge. ML defects are generated by substrate defects and adders during ML coating and are called phase defects (PDs). If we can accept ML blanks with a certain number of PDs, the blank yield will be drastically increased. We can use fewer PD blanks and reduce PD influence by covering them with an absorber layer. To do this, PDs should be located during ML blank defect inspection before absorber patterning. To locate PDs on blanks accurately and precisely, the fiducial marks (FMs) on ML blanks can be used for mask alignment. The defect location accuracy requirement is below 10 nm. We present the results of a feasibility study on the requirements of FMs on EUVL masking by simulations and experiments to establish a PD mitigation method with the EUV actinic blank inspection tool. Based on the results, the optimum ranges for FM lines etched into the ML are 3 to 5 μm in width and at least 100 nm in depth.

Timing-Based Mobile Sensor Localization in Wireless Sensor and Actor Networks

In this paper, localization problem in wireless sensor and actor networks (WSAN) is addressed. In WSAN, the performance of event detection and tracking highly depends on the exact location information of the events that must be reported along with the event features. Having precise location information of the sensor nodes, actors are able to execute actions more effectively in the region of detected events. In this context, the accurate localization of sensor nodes is essential with respect to the actors. Particularly, the problem becomes much more complicated when the sensor nodes as well as the anchor nodes (actors) are mobile. In order to localize the mobile sensor nodes relative to the actors, a novel Timing-based Mobile Sensor Localization (TMSL) algorithm is introduced. In TMSL, sensor nodes determine their distance from actors by using propagation time and speed of RF signal. In order to determine distance from the actors, actors actively broadcast reference beacons in a pattern of intervals adaptively defined according to the mobility of sensor nodes and the required level of localization accuracy. These reference beacons carry the interval numbers in which they were transmitted. The interval numbers are then used by the sensor nodes to calculate the start time of the beacons locally which is then used to determine the propagation time. TMSL does neither require nor assume any time synchronization among the sensor nodes or with the actors. Performance evaluations clearly show that TMSL is adaptive to velocity of mobile sensor and actor nodes and can be configured according to the required localization accuracy in order to avoid overhead raised due to high velocity.