Reduce Location Errors Research Articles

A novel fault location method based on traveling wave for multi-branch distribution network

In this paper, a novel fault location method is proposed for multi-branch distribution network based on traveling wave. The improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and Teager energy operator (TEO) is used to detect the arrival time of traveling waves. Firstly, according to the signal processing ability of the ICEEMDAN method, the ICEEMDAN method is used to decompose the sampled signals when the fault occurs, and extract the high-frequency component of the sampled signals. Then, the TEO is used to extract the abrupt characteristics of high-frequency modal components. And the first maximum mutation point of the signal is regarded as the initial fault traveling wave signal. In addition, considering the transmission characteristic of fault traveling wave signal in multi-branch lines of distribution network, the difference value characteristic of branches is defined. And the recognition principle and the fault location method of multi-branch lines are established to realize the accurate location of distribution network line faults. Simulation results show that the proposed ICEEMDAN-TEO method can effectively detect the fault traveling wave signals, and has a better detection performance than CEEMDAN-TEO and HHT methods. The proposed fault location method can effectively locate fault positions and reduce location errors.© 2017 Elsevier Inc. All rights reserved.

Inter-image Contrastive Consistency for Multi-Person Pose Estimation

Multi-person pose estimation (MPPE) has achieved impressive progress in recent years. However, due to the large variance of appearances among images or occlusions, the model can hardly learn consistent patterns enough, which leads to severe location jitter and missing issues. In this study, we propose a novel framework, termed Inter-image Contrastive consistency (ICON), to strengthen the keypoint consistency among images for MPPE. Concretely, we consider two-fold consistency constraints, which include single keypoint contrastive consistency (SKCC) and pair relation contrastive consistency (PRCC). The SKCC learns to strengthen the consistency of individual keypoints across images in the same category to improve the category-specific robustness. Only with SKCC, the model can effectively reduce location errors caused by large appearance variations, but remains challenging with extreme postures (e.g., occlusions) due to lack of relational guidance. Therefore, PRCC is proposed to strengthen the consistency of pair-wise joint relation between images to preserve the instructive relation. Cooperating with SKCC, PRCC further improves structure aware robustness in handling extreme postures. Extensive experiments on kinds of architectures across three datasets (i.e., MS-COCO, MPII, CrowdPose) show the proposed ICON achieves substantial improvements over baselines. Furthermore, ICON under the semi-supervised setup can obtain comparable results with the fully-supervised methods using only 30% labeled data.

Mobile Location in Wireless Sensor Networks Based on Multi Spot Measurements Model

The localization of sensors in wireless sensor networks has recently gained considerable attention. The existing location methods are based on a one-spot measurement model. It is difficult to further improve the positioning accuracy of existing location methods based on single-spot measurements. This paper proposes two location methods based on multi-spot measurements to reduce location errors. Because the multi-spot measurements model has more measurement equations than the single-spot measurements model, the proposed methods provide better performance than the traditional location methods using one-spot measurement in terms of the root mean square error (RMSE) and Cramer-Rao lower bound (CRLB). Both closed-form and iterative algorithms are proposed in this paper. The former performs suboptimally with less computational burden, whereas the latter has the highest positioning accuracy in attaining the CRLB. Moreover, a novel CRLB for the proposed multi-spot measurements model is also derived in this paper. A theoretical proof shows that the traditional CRLB in the case of single-spot measurements performs worse than the proposed CRLB in the case of multi-spot measurements. The simulation results show that the proposed methods have a lower RMSE than the traditional location methods.

A guide to pre‐processing high‐throughput animal tracking data

Modern, high‐throughput animal tracking increasingly yields ‘big data’ at very fine temporal scales. At these scales, location error can exceed the animal's step size, leading to mis‐estimation of behaviours inferred from movement. ‘Cleaning’ the data to reduce location errors is one of the main ways to deal with position uncertainty. Although data cleaning is widely recommended, inclusive, uniform guidance on this crucial step, and on how to organise the cleaning of massive datasets, is relatively scarce.A pipeline for cleaning massive high‐throughput datasets must balance ease of use and computationally efficiency, in which location errors are rejected while preserving valid animal movements. Another useful feature of a pre‐processing pipeline is efficiently segmenting and clustering location data for statistical methods while also being scalable to large datasets and robust to imperfect sampling. Manual methods being prohibitively time‐consuming, and to boost reproducibility, pre‐processing pipelines must be automated.We provide guidance on building pipelines for pre‐processing high‐throughput animal tracking data to prepare it for subsequent analyses. We apply our proposed pipeline to simulated movement data with location errors, and also show how large volumes of cleaned data can be transformed into biologically meaningful ‘residence patches’, for exploratory inference on animal space use. We use tracking data from the Wadden Sea ATLAS system (WATLAS) to show how pre‐processing improves its quality, and to verify the usefulness of the residence patch method. Finally, with tracks from Egyptian fruit bats Rousettus aegyptiacus, we demonstrate the pre‐processing pipeline and residence patch method in a fully worked out example.To help with fast implementation of standardised methods, we developed the R package atlastools, which we also introduce here. Our pre‐processing pipeline and atlastools can be used with any high‐throughput animal movement data in which the high data‐volume combined with knowledge of the tracked individuals' movement capacity can be used to reduce location errors. atlastools is easy to use for beginners while providing a template for further development. The common use of simple yet robust pre‐processing steps promotes standardised methods in the field of movement ecology and leads to better inferences from data.

Estimation of micro-earthquake source locations based on full adjoint P and S wavefield imaging

SUMMARY Locating micro-earthquakes with high resolution and accuracy is a challenge for traveltime inversion, which has uncertainty on the order of a Fresnel zone (many wavelengths). We develop a wave-equation imaging method to increase resolution and reduce location errors to less than a wavelength, but requires very densely deployed receiver arrays with wide aperture and considerable computational cost. Instead of using acoustic data or direct P wave arrivals only, we use elastic multicomponent data and present a new method that uses the full P and S adjoint wavefields to image the microseismic source locations. We separate the P and S waves from the data, and extrapolate the P and S wavefields of each receiver subarray by solving the P and S adjoint wave equations in parallel. We formulate three source imaging conditions by multiplying over subarrays the adjoint P wavefield (IP), S wavefield (IS) and cross-correlated P and S wavefields (IPS). We perform numerical experiments on the highly realistic SEG SEAM4D reservoir model using surface acquisition array geometries. Results for 2-D and 3-D microseismic source estimations show clean images without noisy artefacts at shallow depths. In particular, IPS provides the highest resolution source location image, while IP is limited by the P wavelength and IS is influenced by small coda artefacts. The major-axis alignment and resolution of the source location image are determined by the hypocentral location with respect to the receiver array and illumination-angle coverage, respectively. We discuss the impacts of S-wave attenuation and frequency bandwidth on the source location images. Noise tests indicate that the imaging results are relatively insensitive to ambient noise, as is observed for the surface monitoring data. Using smoothed velocity models, the imaging results are similar to the results using the true realistically heterogeneous velocity model. The 90 per cent confidence ellipse of the source location due to Gaussian-distributed velocity errors shows a larger depth error as the source becomes deeper, while the horizontal error does not change as much.

Errors in microseismic events locations introduced by neglecting anisotropy during velocity model calibration in downhole monitoring

Microseismic monitoring is the only tool to provide locations of microseismic events induced e.g. during hydraulic fracturing of unconventional reservoirs. Obtained locations become a baseline for the evaluation of stimulation effectiveness. However, due to several factors, they can be different from the true ones and lead to misinterpretation. Most important is the inaccuracy of the velocity model used in the location procedure, which is often amplified by seismic anisotropy. Even though the anisotropy is a common feature of organic-rich mudrocks, it is often neglected in the current industry practice. The usual approach is to produce a set of isotropic models, valid for the limited subsurface area only, which compensate for anisotropy effects. Here we explore how the accuracy of microseismic events' locations is affected when neglecting the anisotropy during velocity model building using traveltimes of calibration shots. We analyse events' mislocations using the synthetic modelling that simulates an ongoing multi-stage treatment in a vertically-transverse isotropic (VTI) media, monitored with a single vertical borehole. Finally, we provide a quantitative interpretation of location errors expected to be present when VTI anisotropy is being neglected. The most important result is the demonstration of how spatially limited are single-stage isotropic models in terms of correct event locations, despite performing an accurate calibration. Also, we show the limited gain in terms of location accuracy when multi-stage isotropic velocity models are constructed. As the ultimate solution to reduce location errors, we propose to account for anisotropy by inverting VTI anisotropy model parameters even when only part of calibration data is available.

DeepNavi

Indoor navigation plays a crucial role in indoor location-based services. Single signal-based navigation systems, however, are prone to sensor noises, signal ambiguities and are specific to trial sites. To address these, existing work fuses different signals with user trajectories. Despite their accuracy, many of them are specific to input signals and navigation modes (e.g., spot-based or sequence-based) and are computationally expensive in large sites. Additionally, they do not give predictive uncertainty estimations, leading to a lack of trust in navigation instructions. In this paper, we propose a unified framework for accurate indoor navigation in various modes with different inputs, termed DeepNavi. We exploit either convolutional or recurrent neural networks for initial feature extraction. Afterwards, we insert fully connected layers to generalize extracted signal-dependent features to a shared domain before fusion. Then, we leverage state-of-the-art ensemble learning to learn multiple predictive models. By combining them together, we further reduce the impact of signal noises and achieve high accuracy. Finally, we insert mixture density networks to model more generalized data distributions and provide uncertainty estimations. We have implemented DeepNavi and conducted extensive experiments in two different trial sites with different signal combinations. Experimental results show that DeepNavi reduces location errors by more than 20% with comparable orientation accuracy.

Hierarchical model-based object localization for auto-contouring in head and neck radiation therapy planning.

Segmentation of organs at risk (OARs) is a key step during the radiation therapy (RT) treatment planning process. Automatic anatomy recognition (AAR) is a recently developed body-wide multiple object segmentation approach, where segmentation is designed as two dichotomous steps: object recognition (or localization) and object delineation. Recognition is the high-level process of determining the whereabouts of an object, and delineation is the meticulous low-level process of precisely indicating the space occupied by an object. This study focuses on recognition. The purpose of this paper is to introduce new features of the AAR-recognition approach (abbreviated as AAR-R from now on) of combining texture and intensity information into the recognition procedure, using the optimal spanning tree to achieve the optimal hierarchy for recognition to minimize recognition errors, and to illustrate recognition performance by using large-scale testing computed tomography (CT) data sets. The data sets pertain to 216 non-serial (planning) and 82 serial (re-planning) studies of head and neck (H&N) cancer patients undergoing radiation therapy, involving a total of ~2600 object samples. Texture property "maximum probability of occurrence" derived from the co-occurrence matrix was determined to be the best property and is utilized in conjunction with intensity properties in AAR-R. An optimal spanning tree is found in the complete graph whose nodes are individual objects, and then the tree is used as the hierarchy in recognition. Texture information combined with intensity can significantly reduce location error for gland-related objects (parotid and submandibular glands). We also report recognition results by considering image quality, which is a novel concept. AAR-R with new features achieves a location error of less than 4 mm (~1.5 voxels in our studies) for good quality images for both serial and non-serial studies.

Improved range-free localization for three-dimensional wireless sensor networks using genetic algorithm

This paper presents a distributed range-free node localization algorithm for three-dimensional (3D) wireless sensor networks (WSNs), named as 3D-GAIDV Hop (3D genetic algorithm based Improved Distance Vector Hop). In the proposed algorithm, the average hop size of anchor nodes is modified by updating a correction factor and the modified hop size is further optimized by line search algorithm. The concept of coplanarity is introduced to reduce location errors caused by the anchor nodes which are coplanar. The localization accuracy is further improved by applying genetic algorithm (GA). To improve the positioning coverage of the network, those target nodes which have been localized successfully in the first round of the localization process, are upgraded to assistant anchor node. All the processes and calculations are carried out at target node level, which makes the proposed algorithm energy efficient. Simulation results show that our proposed algorithm outperforms the similar kind of existing algorithms.

Modified Energy-Efficient Range-Free Localization Using Teaching–Learning-Based Optimization for Wireless Sensor Networks

ABSTRACTThis paper presents an energy-efficient Modified Distance Vector Hop algorithm using Teaching–Learning-Based Optimization, viz. MDV-TLBO, which is range-free, distributed localization algorithm for wireless sensor network (WSN). In the proposed algorithm, the hop size of anchor node is modified by adding correction factor. The concept of collinearity is introduced in this paper to reduce location errors caused by anchor nodes which are collinear. TLBO is used to enhance the localization accuracy, which is parameter-free, efficient optimization technique. Target nodes estimate their final coordinates after location upgradation procedure. In MDV-TLBO, anchor nodes communicate only one time with target nodes to broadcast their location. Hop size modification, optimal selection of anchor nodes, location optimization, and location upgradation are done at target node level, resulting considerable reduction in communication between nodes, due to which energy consumption of the nodes has been significantly reduced. To show the applicability of proposed algorithm in real scenarios, radio irregularity model is considered. Simulation results show that our proposed algorithm achieves better localization accuracy, high positioning coverage with less energy consumption in comparison of the existing improved DV-Hop algorithms.

Improved DV-Hop localization algorithm using teaching learning based optimization for wireless sensor networks

Node localization is one of the most critical issues for wireless sensor networks, as many applications depend on the precise location of the sensor nodes. To attain precise location of nodes, an improved distance vector hop (IDV-Hop) algorithm using teaching learning based optimization (TLBO) has been proposed in this paper. In the proposed algorithm, hop sizes of the anchor nodes are modified by adding correction factor. The concept of collinearity is introduced to reduce location errors caused by anchor nodes which are collinear. For better positioning coverage, up-gradation of target nodes to assistant anchor nodes has been used in such a way that those target nodes are upgraded to assistant anchor nodes which have been localized in the first round of localization. For further improvement in localization accuracy, location of target nodes has been formulated as optimization problem and an efficient parameter free optimization technique viz. TLBO has been used. Simulation results show that the proposed algorithm is overall 47, 30 and 22% more accurate than DV-Hop, DV-Hop based on genetic algorithm (GADV-Hop) and IDV-Hop using particle swarm optimization algorithms respectively and achieves high positioning coverage with fast convergence.

CSI-based Fingerprinting for Indoor Localization: A Deep Learning Approach

With the fast growing demand of location-based services in indoor environments, indoor positioning based on fingerprinting has attracted a lot of interest due to its high accuracy. In this paper, we present a novel deep learning based indoor fingerprinting system using Channel State Information (CSI), which is termed DeepFi. Based on three hypotheses on CSI, the DeepFi system architecture includes an off-line training phase and an on-line localization phase. In the off-line training phase, deep learning is utilized to train all the weights of a deep network as fingerprints. Moreover, a greedy learning algorithm is used to train the weights layer-by-layer to reduce complexity. In the on-line localization phase, we use a probabilistic method based on the radial basis function to obtain the estimated location. Experimental results are presented to confirm that DeepFi can effectively reduce location error compared with three existing methods in two representative indoor environments.

A Proposed Stealthy Target Detection in Ground Plane Effect Based on Balloon-borne Netted System Using Hybrid (PO/TDOA) Method

AbstractA proposed detection technique for stealthy target model F-117A based on balloon-borne netted radar system is applied. One of the potential problems of the proposed scheme is the ground plane effect caused by the high altitude platform. In this paper, we present a proposed study of the ground plane effect on the stealthy detection based on a new hybrid of the physical optics (PO) and time difference of arrival (TDOA) methods. Through simulation, the effect results of all parameters characterizing the clutter on balloon fall point distribution are obtained. To achieve high location accuracy of stealthy model, we proposed a scheme of detecting stealthy model in ground plane effect by using accurate radar cross section (RCS) measurement to compute the stealthy target position based on PO formulation. The netted radar sensitivity of proposed scheme has been improved due to increasing PO-scattered field of stealthy model with higher aspect angles of balloon-borne comparing to the conventional ground-based netted system, which has limited observability and poor detection due to low signal-to-noise ratio (SNR). Simulations demonstrate that proposed scheme gives much higher location accuracy and reduces location errors comparing to the conventional system.

Location error eliminating approach for network delay measurement based on network driver interface specification

In the network performance measurement,location error is one of the main factors which influence the accurate network delay measurement.To reduce location error,an improved method for delay measurement was proposed based on Windows Network Driver Interface Specification(NDIS).By using this method,the timestamp position was removed from application to NDIS Intermediate Driver(ID) which was embedded between Miniport Driver(MD) and Protocol Driver(PD),and then the measurement program could calculate the network delay according to those timestamps.Compared with the traditional method,the experimental results show that the proposed method can nearly eliminate location error,and make sure the measurement standard deviation to be lowered than 10 μs under different packet-lengths and host-loads.The improved measurement method does not need additional software and hardware,so it has lower measurement cost and can be applied widely.

A WSN-Based Localization Scheme Considering the Reliability of RSSI Measurements

In this paper, we propose a localization scheme considering the reliability of RSSI (Received Signal Strength Indication) measurements in the WSN (Wireless Sensor Network) environment. This scheme attempts to reduce location errors due to indoor obstacles or environmental factors, when location calculations are based on RSSI. The standard deviation is used to evaluate the reliability of RSSI measurements from the reference node. Also, the directional path loss exponent is calculated through learning with respect to the reference node. The experimental results show that the proposed localization scheme improves the performance significantly in terms of location accuracy, compared to the existing RSSI-based approaches.

Evaluation of Seismic-Acoustic Analysis Methods for a Real-time UXO Monitoring System

The Department of Defense (DoD) uses over two million rounds of high-explosive (HE) munitions per year ( Defense Science Board Task Force, 2003 ). A small percentage does not explode, thus generating unexploded ordnance (UXO) in current range areas at a substantial rate. As these ranges are closed, the DoD becomes responsible for the environmental restoration of the affected properties. Current methods of UXO remediation are costly because of high false alarm rates. Our current research is to develop a complementary technology that will alleviate false alarm rate by detecting, classifying, and locating UXO in near real time (less than 1 minute) as a munition impacts the range. This technology will utilize an array of buried seismic sensors in a calibrated range area, along with a set of algorithms based on theoretical and applied seismology and statistical analysis.Initial field tests at three sites focused on developing concepts of the seismic and acoustic location of ordnance impacts. Our research program developed from these initial field tests has four primary objectives: 1) fully implement a wired seismic-acoustic ordnance impact location system for live fire ranges; 2) develop a system capability to discriminate high-order (HE), low-order (partially exploded), and zero-order (UXO) events; 3) reduce location error to a stringent program metric of 1–2 m; and 4) investigate the feasibility of developing a wireless implementation of the technology.This paper describes the procedures and results from follow-on tests that were conducted in two locations at the U.S. Army Aberdeen Proving Ground (APG), Maryland. These tests were used to evaluate potential seismic-acoustic methods and system configurations for a Seismic-Acoustic Impact Monitoring Assessment (SAIMA) system for mitigating UXO hazards. Significant results from this work include: 1) seismic impulses from low-order impacts were detected at distances up to 1,000 meters; 2) classification features based on measurements of the amplitude of acoustic and seismic phases produce clear discrimination between HE and UXO impacts; 3) calculated location solutions for HE and UXO impacts yield an average location error of 10–20 meters; and 4) empirical observation and waveform modeling demonstrated that surface waves dominate the signal at all distances and therefore should be the primary phase used for all components of analysis. Furthermore, these tests demonstrated the current system design, allowing further enhancements, is capable of meeting the initial research objectives (1) and (2). Future research will focus on improving system performance with refinement of the sensor-layout geometry and the detection and location algorithms through system error analyses and follow-on field testing.

Mobile station positioning using time difference of arrival and received signal strength

In this paper, a new Mobile Station Positioning (MSP) technique is proposed and its performance is evaluated. The proposed technique combines the Received Signal Strength (RSS) and the Time Difference of Arrival (TDOA) techniques. Each RSS measurement yields a circle on which the Mobile Station (MS) may lie. Meanwhile, each TDOA measurement defines a hyperbola on which the MS may reside. The proposed hybrid TDOA/RSS technique uses Taylor series expansion to solve the hyperbolas and circles specified by the distance measurements obtained by the Base Stations (BSs). The MS position is determined based on Linear Least Squares (LLS) method in an iterative fashion. Without hardware modification, the proposed technique reduces location errors compared with either technique separately. Simulation results demonstrate superior performance over both TDOA and RSS techniques.

Performance Analysis of W Phase Detector in Galileo Signal Receiver

Galileo system is a new satellite navigation system. In this paper, a Galileo signal receiver based on W phase detector is presented. It can capture and track the Galileo signals. Moreover, it can also reduce location errors caused by the multipath signal when the delay is just small chip length away from the useful signal. At first, the principle of the traditional E-L phase detector and its anti-multipath performance is analyzed in this article. Finally, the forming method of the W phase detector waveform is introduced.

A Novel Iterative Multilateral Localization Algorithm for Wireless Sensor Networks

In many applications of wireless sensor networks, location is very important information. It can be used to identify the location at which sensor readings originate, in routing and data storage protocols based on geographical areas and so on. Location information can come from manual setting or GPS device. However, manual setting requires huge cost of human time, and GPS setting requires expensive device cost. Both approaches are not applicable to localization task of large scale wireless sensor networks. In this paper, we propose an accurate and efficient localization algorithm, called iterative multilateral localization algorithm based on time rounds. This Algorithm uses time round mechanism and anchor nodes triangle placement scheme to reduce error accumulation caused by iteratively localization. And it also reduces location errors and prevents abnormal phenomena caused by trilateral localization through limiting the minimum number of neighboring beacon nodes used in different localizing time rounds. Experimental results reveal that this algorithm has high localization accuracy, even if in large range errors, it can achieve good result.

Errors due to the tropospheric and ionospheric effects on the geographic location of data collection platforms

The use of the Doppler shift measurements to locate atransmitter has been studied and tested within the Brazilian Environmental Data Collection System. To improve the location of Data Collection Platforms (DCPs) with their transmitters, some effects such as corrections due to the ionosphere and the troposphere have been considered in aprevious work (Celestino et aI., 2007) using simulated data and some initial error evaluation for one existing DCP. This work presents an evaluation of the impacts on the geographical location due to the ionospheric and the tropospheric effects, however using two meteorological DCPs and covering a longer test period (one year). The seasonal and day-night effects are also analyzed during this test period. The results of the analysis indicated that correction of the ionospheric and tropospheric effects can, on the average, reduce the location errors to the scale between ten and hundred meters for the test conditions independent of the DCP location that was used. Due to the complexity of modeling sources of errors in the location of a platform, still a considerable work need to be done to reduce location errors in the system. Initial analysis indicates that the knowledge of the satellite position can be another significant source of platform position error.