scholarly journals Performance Evaluation of Non-GPS Based Localization Techniques under Shadowing Effects

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2633 ◽  
Author(s):  
Ngoc Mai Nguyen ◽  
Le Chung Tran ◽  
Farzad Safaei ◽  
Son Lam Phung ◽  
Peter Vial ◽  
...  
Keyword(s):  
High Signal ◽  
Angle Of Arrival ◽  
Received Signal Strength Indicator ◽  
Combination Methods ◽  
Accuracy And Precision ◽  
Depth Analysis ◽  
Power Control Algorithm ◽  
Shadowing Effects ◽  
Gps Localization ◽  
Weighted Combination

Non-GPS localization has gained much interest from researchers and industries recently because GPS might fail to meet the accuracy requirements in shadowing environments. The two most common range-based non-GPS localization methods, namely Received Signal Strength Indicator (RSSI) and Angle-of-Arrival (AOA), have been intensively mentioned in the literature over the last decade. However, an in-depth analysis of the weighted combination methods of AOA and RSSI in shadowing environments is still missing in the state-of-the-art. This paper proposes several weighted combinations of the two RSSI and AOA components in the form of pAOA + qRSSI, devises the mathematical model for analyzing shadowing effects, and evaluates these weighted combination localization methods from both accuracy and precision perspectives. Our simulations show that increasing the number of anchors does not necessarily improve the precision and accuracy, that the AOA component is less susceptible to shadowing than the RSSI one, and that increasing the weight of the AOA component and reducing that of the RSSI component help improve the accuracy and precision at high Signal-to-Noise Ratios (SNRs). This observation suggests that some power control algorithm could be used to increase automatically the transmitted power when the channel experiences large shadowing to maintain a high SNR, thus guaranteeing both accuracy and precision of the weighted combination localization techniques.

scholarly journals Astrometric Observations of a Near-Earth Object Using the Image Fusion Technique

2021 ◽  
Vol 162 (6) ◽  
pp. 250
Author(s):  
Yigong Zhang ◽  
Jiancheng Wang ◽  
Jie Su ◽  
Xiangming Cheng ◽  
Zhenjun Zhang
Keyword(s):  
Image Fusion ◽  
Noise Analysis ◽  
Open Cluster ◽  
Observational Research ◽  
High Signal ◽  
Fusion Technique ◽  
Origin And Evolution ◽  
Accuracy And Precision ◽  
Fused Image ◽  
Image Fusion Technique

Abstract The precise astrometric observation of small near-Earth objects (NEOs) is an important observational research topic in the astrometric discipline, which greatly promotes multidisciplinary research, such as the origin and evolution of the solar system, the detection and early warning of small NEOs, and deep-space navigation. The characteristics of small NEOs, such as faintness and fast moving speed, restrict the accuracy and precision of their astrometric observations. In the paper, we present a method to improve the accurate and precise astrometric positions of NEOs based on image fusion technique. The noise analysis and astrometric test from the observed images of the open cluster M23 are given. Using the image fusion technique, we obtain the sets of superimposed images and original images containing reference stars and moving targets, respectively. The final fused image set includes background stars with high signal-to-noise ratios and ideal NEO images simultaneously and avoids the saturation of background stars. Using the fused images, we can reduce the influence of telescope tracking and NEO ephemeris errors on astrometric observations, and our results indicate that the accuracy and precision of NEO Eros astrometry are improved obviously after we choose suitable image fuse mode.

scholarly journals Three-Dimensional Empirical AoA Localization Technique for Indoor Applications

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5544 ◽  
Author(s):  
Abdallah Alma’aitah ◽  
Baha’ Alsaify ◽  
Raed Bani-Hani
Keyword(s):  
Antenna Arrays ◽  
Low Cost ◽  
Three Dimensional ◽  
Reference Points ◽  
Average Error ◽  
Navigation Systems ◽  
Angle Of Arrival ◽  
Received Signal Strength Indicator ◽  
Localization Technique ◽  
Localization Accuracy

Small and pervasive devices have been increasingly used to identify and track objects automatically. Consequently, several low-cost localization schemes have been proposed in the literature based on angle of arrival (AoA), time difference of arrival (TDoA), received signal strength indicator (RSSI) or their combinations. In this paper, we propose a three-dimensional empirical AoA localization (TDEAL) technique for battery-powered devices. The proposed technique processes the AoA measurements at fixed reader nodes to estimate the locations of the tags. The proposed technique provides localization accuracy that mitigates non-linear empirical errors in AoA measurements. We utilize two omni-directional antenna arrays at each fixed reader node to estimate the location vector. With multiple location estimations from different fixed reader nodes, each estimated location is assigned a weight that is inversely proportional to the AoA phase-difference error. Furthermore, the actual AoA parabolic formula of the location is approximated to a cone to simplify the location calculation process. The proposed localization technique has a low hardware cost, low computational requirements, and precise location estimates. Based on the performance evaluation, significant location accuracy is achieved by TDEAL; where, for instance, an average error margin of less than 13 cm is achieved using 10 readers in an area of   10   m ×   10   m . TDEAL can be utilized to provide reference points when integrated with a relative (e.g., inertial navigation systems) localization systems.

IMPLEMENTATION OF AN INEXPENSIVE AND IMPROVED PURPOSE-BUILT HIGH SNR BIOMEDICAL SIGNAL CAPTURE, RECORD, AND ANALYSIS SYSTEM

2020 ◽  
pp. 2150004
Author(s):  
Tung-Tai Kuo ◽  
Rong-Chin Lo ◽  
Yuan-Hao Chen ◽  
Chung-Ling Tseng
Keyword(s):  
Signal To Noise Ratio ◽  
Low Cost ◽  
High Signal ◽  
Record System ◽  
Brain Wave ◽  
Biomedical Signal ◽  
Accuracy And Precision ◽  
Analysis System ◽  
The Brain ◽  
Brain Study

It is very important for the brain study to design a multi-channel and high signal-to-noise ratio (SNR) bio-medical signal capture, record, and analysis system, which can effectively enhance accuracy and precision of the signal capture under dozens to hundreds of microvolts. Unfortunately, the system for data acquisition is very easily interfered by the environment, the power, and the bio-amplifier, so that the results will lead to a failed promotion of capturing high SNR signals, especially in the tiny brain wave signal. In this study, it has been designed an inexpensive, purpose-built, high SNR brainwave signals measurement system. The system is composed of an improved capture system, record system, and analysis system. To better consider the strength and characteristics of the tiny brainwave signals, the system was designed to include a suitable bio-amplifier to make each channel of the invasive microelectrode able to collect the brainwave signals correctly and it provides recording and analysis software, which can not only extract the characteristics of brainwave signals, but also quickly classify signals. The system can collect biological signals from 10[Formula: see text][Formula: see text]V to 420[Formula: see text][Formula: see text]V and has a high SNR[Formula: see text]30. The proposed system is easy to make and can be fabricated for the relatively low cost of only US$203. The brain wave signals from the three actions can also be easily classified, with a correct rate up to 46.70%. The system has six improvements: good SNR, the ability to capture small signals, modularity, a low price, easy fabrication, and simple operation.

scholarly journals Gaia Data Release 1

2017 ◽  
Vol 599 ◽  
pp. A50 ◽  
Author(s):  
F. Arenou ◽  
X. Luri ◽  
C. Babusiaux ◽  
C. Fabricius ◽  
A. Helmi ◽  
...  
Keyword(s):  
User Interfaces ◽  
Globular Clusters ◽  
Variable Stars ◽  
Published Data ◽  
The Public ◽  
Accuracy And Precision ◽  
Depth Analysis ◽  
Data Release ◽  
Coverage Accuracy ◽  
Validation Tests

Context. Before the publication of the Gaia Catalogue, the contents of the first data release have undergone multiple dedicated validation tests. Aims. These tests aim to provide in-depth analysis of the Catalogue content in order to detect anomalies and individual problems in specific objects or in overall statistical properties, and either to filter them before the public release or to describe the different caveats on the release for an optimal exploitation of the data. Methods. Dedicated methods using either Gaia internal data, external catalogues, or models have been developed for the validation processes. They test normal stars as well as various populations such as open or globular clusters, double stars, variable stars, and quasars. Properties of coverage, accuracy, and precision of the data are provided by the numerous tests presented here and are jointly analysed to assess the data release content. Results. This independent validation confirms the quality of the published data, Gaia DR1 being the most precise all-sky astrometric and photometric catalogue to date. However, several limitations in terms of completeness, and astrometric or photometric quality are identified and described. Figures describing the relevant properties of the release are shown, and the testing activities carried out validating the user interfaces are also described. A particular emphasis is made on the statistical use of the data in scientific exploitation.

scholarly journals A Novel Positioning System Based on Coverage Area Pruning in Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4469 ◽  
Author(s):  
Shih-Chang Huang ◽  
Fu-Gong Li
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Positioning System ◽  
Angle Of Arrival ◽  
Coverage Area ◽  
Received Signal Strength Indicator ◽  
Centroid Point ◽  
Positioning Algorithm

Wireless sensor networks are commonly applied in environmental monitoring applications. The crucial factor in such applications is to accurately retrieve the location of a monitoring event. Although many technologies have been proposed for target positioning, the devices used in such methods require better computational abilities or special hardware that is unsuitable for sensor networks with limited ability. Therefore, a range-free positioning algorithm, named coverage area pruning positioning system (CAPPS), is proposed in this study. First, the proposed CAPPS approach determines the area that includes the target approximately by using sensor nodes that can detect the target. Next, CAPPS uses sensor nodes that cannot detect the target to prune the area to improve positioning accuracy. The radio coverage variation is evaluated in a practical scenario, and a heuristic mechanism is proposed to reduce false positioning probability. Simulation results show that the size of the positioning area computed by CAPPS is smaller than that computed using distance vector hop, angle of arrival, and received signal strength indicator by approximately 98%, 97%, and 93%, respectively. In the radio variation scenario, the probability of determining an area excluding the target can be reduced from 50%–95% to 10%–30% by applying the proposed centroid point mechanism.

scholarly journals A Combined Batteryless Radio and WiFi Indoor Positioning for Hospital Nursing

2016 ◽  
Vol 12 (1) ◽  
pp. 34 ◽  
Author(s):  
Riad Kanan ◽  
Obaidallah Elhassan
Keyword(s):  
Mobile Phone ◽  
Indoor Localization ◽  
Time Of Arrival ◽  
Online Database ◽  
Angle Of Arrival ◽  
Received Signal Strength Indicator ◽  
Hospital Nursing ◽  
Efficient Hospital ◽  
User Friendly ◽  
Localization Systems

This paper proposes a design of an efficient hospital nurse calling system which combines two types of indoor localization systems. The purpose of the first system is to locate patients while the second is to locate nurses equipped with their smart phones. The main goal of developing such system is to decrease the time taking for nurses to provide healthcare for patients. Patients' positioning system is RF based. Indeed, each patient is equipped with a wireless and battery-free call button. When the switch is pressed, a wireless telegram is sent to reference nodes that act like Wireless Sensor Networks (WSN). The positioning of patient is performed using trilateration method with the help of Received Signal Strength Indicator (RSSI) values. Hence, beacons will forward the received signal from patient’s call button to a central receiver module connected to a computer. A dedicated program has been developed to calculate the position of the call button and post it on an online database. On the other hand, the nurses’ localization system is WiFi-based. Nurses' positioning is done by determining the Time of Arrival (ToA) and the Angle of Arrival (AoA) between the mobile phone and the WiFi router. The mobile phone locations are posted to the online database as well. Our program performs a comparison between the nurses' and the patient's coordinates. The nearest nurse gets an alarm. As consequence, a patient gets care from the nearest available nurse in an efficient way and with less time. The proposed system is user-friendly and Internet of Things (IoT) based architecture integrating two heterogeneous localization systems seamlessly.

scholarly journals GuideLoc: UAV-Assisted Multitarget Localization System for Disaster Rescue

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Anwen Wang ◽  
Xiang Ji ◽  
Dajun Wu ◽  
Xuedong Bai ◽  
Nana Ding ◽  
...  
Keyword(s):  
Target Location ◽  
Wireless Devices ◽  
Angle Of Arrival ◽  
Wireless Localization ◽  
Received Signal Strength Indicator ◽  
Localization System ◽  
Region Division ◽  
Aerial Vehicle ◽  
Target Experiment ◽  
Short Time

Locating trapped targets using the signals of wireless devices such as mobile phones carried by targets is increasingly becoming the preferred scheme for disaster rescue. However, the accuracy of radio-localization technology currently adopted in the rescue is not high enough. To solve such problems, researchers proposed the approaches of Warwalking or Wardriving. However, both approaches are all limited by application scenarios. This paper proposes GuideLoc, a highly efficient aerial wireless localization system, which uses the target guiding technology based on region division. GuideLoc allows an unmanned aerial vehicle (UAV) to fly over a target device and provide position coordinates of the UAV as the target location information. During the process of positioning targets, based on the result of regional division, GuideLoc uses a number of fixed antennas to get the received signal strength indicator (RSSI) and angle of arrival (AOA) information of the target and estimates the target location using the information and correspondingly controls the UAV to fly towards the target. Averaging method is also applied for determining coordinates of the target. Experiment results show that GuideLoc can in short time achieve 2.7-meter positioning accuracy in average. Compared with other wireless localization methods using UAVs proposed in the literature, it shortened the flight distance by more than 50%. In addition, energy efficiency is also improved significantly.

scholarly journals A Systematic Comparison of Data Selection Criteria for SMT Domain Adaptation

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Longyue Wang ◽  
Derek F. Wong ◽  
Lidia S. Chao ◽  
Yi Lu ◽  
Junwen Xing
Keyword(s):  
Domain Adaptation ◽  
Statistical Machine Translation ◽  
Real Life ◽  
Training Data ◽  
Data Selection ◽  
Domain Specific ◽  
Combination Methods ◽  
Depth Analysis ◽  
Similarity Measuring ◽  
The Individual

Data selection has shown significant improvements in effective use of training data by extracting sentences from large general-domain corpora to adapt statistical machine translation (SMT) systems to in-domain data. This paper performs an in-depth analysis of three different sentence selection techniques. The first one is cosine tf-idf, which comes from the realm of information retrieval (IR). The second is perplexity-based approach, which can be found in the field of language modeling. These two data selection techniques applied to SMT have been already presented in the literature. However, edit distance for this task is proposed in this paper for the first time. After investigating the individual model, a combination of all three techniques is proposed at both corpus level and model level. Comparative experiments are conducted on Hong Kong law Chinese-English corpus and the results indicate the following: (i) the constraint degree of similarity measuring is not monotonically related to domain-specific translation quality; (ii) the individual selection models fail to perform effectively and robustly; but (iii) bilingual resources and combination methods are helpful to balance out-of-vocabulary (OOV) and irrelevant data; (iv) finally, our method achieves the goal to consistently boost the overall translation performance that can ensure optimal quality of a real-life SMT system.

scholarly journals Spatial and Temporal Consistency of Brain Networks for different Multi-Echo fMRI Combination Methods

2021 ◽  
Author(s):  
J. Pilmeyer ◽  
G. Hadjigeorgiou ◽  
R. Lamerichs ◽  
M. Breeuwer ◽  
A.P. Aldenkamp ◽  
...  
Keyword(s):  
Time Series ◽  
Resting State ◽  
Spatial Domain ◽  
Optimal Combination ◽  
Combination Method ◽  
Resting State Networks ◽  
Combination Methods ◽  
Temporal And Spatial ◽  
Noise Ratio ◽  
Weighted Combination

AbstractThe application of multi-echo functional magnetic resonance imaging (fMRI) studies has considerably increased in the last decade due to its superior BOLD sensitivity compared to single-echo fMRI. Various methods have been developed that combine the fMRI time-series derived at different echo times to improve the data quality. Here we evaluated three multi-echo combination schemes, i.e. ‘optimal combination’ (T2*-weighted), temporal Signal-to-Noise Ratio (tSNR) weighted, and temporal Contrast-to-Noise Ratio (tCNR) weighted combination. For the first time, the effect of these multi-echo combinations on functional resting-state networks was assessed in the temporal and spatial domain, and compared to networks derived from the second echo (35 ms) functional images. Sixteen healthy volunteers were scanned during a 5 minutes resting-state fMRI session. After obtaining the networks, several temporal and spatial metrics were calculated for their time-series and spatial maps. Our results showed that, compared to the second echo network time-series, the Pearson correlation and root mean square error were the most consistent for the optimal combination time-series and the least with those derived from tSNR-weighted combination. The frequency analysis further suggested that the time-series from the tSNR-weighted combination method reduced hardware- and physiological-related artifacts as reflected by the reduced power for the associated frequencies in almost all networks. Moreover, the spatial stability and extent of the networks significantly increased after multi-echo combination, primarily for the optimal combination, followed by the tSNR-weighted combination. The performance of the tCNR-weighted combination lacked robustness and instead varied remarkedly between resting-state networks in both the temporal and spatial domain. The results highlight the benefits of multi-echo sequences on resting-state networks as well as the importance of adjusting the choice of multi-echo combination method to the research question and domain of interest.

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