scholarly journals Passive Vector Sensing for Non-Cooperative Emitter Localization in Indoor Environments

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 442 ◽  
Author(s):  
Donald Hall ◽  
Ram Narayanan ◽  
Erik Lenzing ◽  
David Jenkins
Keyword(s):  
Three Dimensional ◽  
Gaussian Mixture ◽  
Wireless Security ◽  
Time Of Arrival ◽  
Indoor Environments ◽  
Blind Channel Equalization ◽  
Time Frequency ◽  
Channel Estimate ◽  
Vector Sensor ◽  
Emitter Localization

Indoor emitter localization is a topic of continued interest for improving wireless security as wireless technologies continue to become more advanced. Conventional methods have focused on the localization of devices relative to multi-sensor systems owing to ease of implementation with pre-existing infrastructures. This work, however, focuses on enhancing wireless security via non-cooperative emitter localization in scenarios where only a single receiver can be employed. A vector sensor is simulated and experimentally developed that extracts three-dimensional signal characteristics for room-based emitter localization and is compared to conventional methodologies such as Received Signal Strength (RSS), Time of Arrival (ToA), and Direction of Arrival (DoA). The proposed method generates time-frequency fingerprints and extracts features through dimensionality reduction. A second stage extracts spatial parameters consisting of Channel State Information (CSI) and DoAs that are analyzed using a Gaussian Mixture Model (GMM) to segregate fine-grained regions of interest within each room where the non-cooperative emitter resides. Blind channel equalization cascaded with a least squares channel estimate is used for acquiring the CSI, whereas the DoAs are obtained by unique trigonometric properties of the vector sensing antenna. The results demonstrate that a vector sensor can improve non-cooperative emitter localization and enhance wireless security in indoor environments.

scholarly journals Modeling and Performance Evaluation of Precise Positioning and Time-Frequency Transfer with Galileo Five-Frequency Observations

2021 ◽  
Vol 13 (15) ◽  
pp. 2972
Author(s):  
Wei Xu ◽  
Wen-Bin Shen ◽  
Cheng-Hui Cai ◽  
Li-Hong Li ◽  
Lei Wang ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Satellite Orbit ◽  
Average Frequency ◽  
Satellite System ◽  
Observation Data ◽  
Navigation Satellite ◽  
Dual Frequency ◽  
Time Frequency ◽  
Combination Model ◽  
Frequency Transfer

The present Global Navigation Satellite System (GNSS) can provide at least double-frequency observations, and especially the Galileo Navigation Satellite System (Galileo) can provide five-frequency observations for all constellation satellites. In this contribution, precision point positioning (PPP) models with Galileo E1, E5a, E5b, E5 and E6 frequency observations are established, including a dual-frequency (DF) ionospheric-free (IF) combination model, triple-frequency (TF) IF combination model, quad-frequency (QF) IF combination model, four five-frequency (FF) IF com-bination models and an FF uncombined (UC) model. The observation data of five stations for seven days are selected from the multi-GNSS experiment (MGEX) network, forming four time-frequency links ranging from 454.6 km to 5991.2 km. The positioning and time-frequency transfer performances of Galileo multi-frequency PPP are compared and evaluated using GBM (which denotes precise satellite orbit and clock bias products provided by Geo Forschung Zentrum (GFZ)), WUM (which denotes precise satellite orbit and clock bias products provided by Wuhan University (WHU)) and GRG (which denotes precise satellite orbit and clock bias products provided by the Centre National d’Etudes Spatiales (CNES)) precise products. The results show that the performances of the DF, TF, QF and FF PPP models are basically the same, the frequency stabilities of most links can reach sub10−16 level at 120,000 s, and the average three-dimensional (3D) root mean square (RMS) of position and average frequency stability (120,000 s) can reach 1.82 cm and 1.18 × 10−15, respectively. The differences of 3D RMS among all models are within 0.17 cm, and the differences in frequency stabilities (in 120,000 s) among all models are within 0.08 × 10−15. Using the GRG precise product, the solution performance is slightly better than that of the GBM or WUM precise product, the average 3D RMS values obtained using the WUM and GRG precise products are 1.85 cm and 1.77 cm, respectively, and the average frequency stabilities at 120,000 s can reach 1.13 × 10−15 and 1.06 × 10−15, respectively.

scholarly journals Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigation

2017 ◽  
Vol 14 (5) ◽  
pp. 172988141773275 ◽  
Author(s):  
Francisco J Perez-Grau ◽  
Fernando Caballero ◽  
Antidio Viguria ◽  
Anibal Ollero
Keyword(s):  
Monte Carlo ◽  
Motion Tracking ◽  
Robot Navigation ◽  
Tracking System ◽  
Three Dimensional ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Localization Algorithm ◽  
Monte Carlo Localization ◽  
Motion Tracking System

This article presents an enhanced version of the Monte Carlo localization algorithm, commonly used for robot navigation in indoor environments, which is suitable for aerial robots moving in a three-dimentional environment and makes use of a combination of measurements from an Red,Green,Blue-Depth (RGB-D) sensor, distances to several radio-tags placed in the environment, and an inertial measurement unit. The approach is demonstrated with an unmanned aerial vehicle flying for 10 min indoors and validated with a very precise motion tracking system. The approach has been implemented using the robot operating system framework and works smoothly on a regular i7 computer, leaving plenty of computational capacity for other navigation tasks such as motion planning or control.

Moving Objects Detection Algorithm Based on Three-Dimensional Gaussian Mixture Codebook Model Using XYZ Color Model

2013 ◽  
Vol 347-350 ◽  
pp. 3505-3509 ◽  
Author(s):  
Jin Huang ◽  
Wei Dong Jin ◽  
Na Qin
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Moving Objects ◽  
Three Dimensional ◽  
Gaussian Mixture ◽  
Detection Algorithm ◽  
Color Model ◽  
Moving Objects Detection ◽  
Objects Detection ◽  
Model Algorithm

In order to reduce the difficulty of adjusting parameters for the codebook model and the computational complexity of probability distribution for the Gaussian mixture model in intelligent visual surveillance, a moving objects detection algorithm based on three-dimensional Gaussian mixture codebook model using XYZ color model is proposed. In this algorithm, a codebook model based on XYZ color model is built, and then the Gaussian model based on X, Y and Z components in codewords is established respectively. In this way, the characteristic of the three-dimensional Gaussian mixture model for the codebook model is obtained. The experimental results show that the proposed algorithm can attain higher real-time capability and its average frame rate is about 16.7 frames per second, while it is about 8.3 frames per second for the iGMM (improved Gaussian mixture model) algorithm, about 6.1 frames per second for the BM (Bayes model) algorithm, about 12.5 frames per second for the GCBM (Gaussian-based codebook model) algorithm, and about 8.5 frames per second for the CBM (codebook model) algorithm in the comparative experiments. Furthermore the proposed algorithm can obtain better detection quantity.

CFD Simulation of Particle Transport and Dispersion in Indoor Environment by Human Walking

2012 ◽  
Author(s):  
Iman Goldasteh ◽  
Goodarz Ahmadi ◽  
Andrea Ferro
Keyword(s):  
Particulate Matter ◽  
High Speed ◽  
Cfd Simulation ◽  
Three Dimensional ◽  
Study Data ◽  
Turbulent Dispersion ◽  
Experimental Chamber ◽  
Indoor Environments ◽  
Three Dimensional Model ◽  
Particle Resuspension

Particle resuspension is an important source of particulate matter in indoor environments that significantly affects the indoor air quality and could potentially have adverse effect on human health. Earlier efforts to investigate indoor particle resuspension hypothesized that high speed airflow generated at the floor level during the gate cycle is the main cause of particle resuspension. The resuspended particles are then assumed to be dispersed by the airflow in the room, which is impacted by both the ventilation and the occupant movement, leading to increased PM concentration. In this study, a three dimensional model of a room was developed using FLUENT™ CFD package. A RANS approach with the RNG k-ε turbulence model was used for simulating the airflow field in the room for different ventilation conditions. The trajectories of resuspended particulate matter were computed with a Lagrangian method by solving the equations of particle motion. The effect of turbulent dispersion was included with the use of the eddy lifetime model. The resuspension of particles due to gait cycle was estimated and included in the computational model. The dispersion and transport of particles resuspended from flooring as well as particle re-deposition on flooring and walls were simulated. Particle concentrations in the room generated by the resuspension process were evaluated and the results were compared with experimental chamber study data as well as simplified model predictions, and good agreement was found.

Decomposition of Event-Related Brain Potentials into Multiple Functional Components Using Wavelet Transform

2001 ◽  
Vol 32 (3) ◽  
pp. 122-138 ◽  
Author(s):  
Tamer Demiralp ◽  
Ahmet Ademoglu
Keyword(s):  
Wavelet Transform ◽  
Three Dimensional ◽  
Experimental Session ◽  
Brain Potentials ◽  
Time Frequency ◽  
Similar Characterization ◽  
Component Identification ◽  
Frequency Components ◽  
Neurophysiological Mechanisms ◽  
Functional Components

Event related brain potential (ERP) waveforms consist of several components extending in time, frequency and topographical space. Therefore, an efficient processing of data which involves the time, frequency and space features of the signal, may facilitate understanding the plausible connections among the functions, the anatomical structures and neurophysiological mechanisms of the brain. Wavelet transform (WT) is a powerful signal processing tool for extracting the ERP components occurring at different time and frequency spots. A technical explanation of WT in ERP processing and its four distinct applications are presented here. The first two applications aim to identify and localize the functional oddball ERP components in terms of certain wavelet coefficients in delta, theta and alpha bands in a topographical recording. The third application performs a similar characterization that involves a three stimulus paradigm. The fourth application is a single sweep ERP processing to detect the P300 in single trials. The last case is an extension of ERP component identification by combining the WT with a source localization technique. The aim is to localize the time-frequency components in three dimensional brain structure instead of the scalp surface. The time-frequency analysis using WT helps isolate and describe sequential and/or overlapping functional processes during ERP generation, and provides a possibility for studying these cognitive processes and following their dynamics in single trials during an experimental session.

Distribution of arrival times in cosmic ray showers

1978 ◽  
Vol 10 (4) ◽  
pp. 730-735
Author(s):  
H. S. Green
Keyword(s):  
Cosmic Radiation ◽  
Three Dimensional ◽  
Stochastic Theory ◽  
Cosmic Ray ◽  
Time Of Arrival ◽  
Air Showers ◽  
Actual Distribution ◽  
Arrival Times ◽  
Primary Particles ◽  
Theoretical Analyses

The theoretical analyses of the extensive air showers developing from the cosmic radiation has its origins in the work of Carlson and Oppenheimer (1937) and Bhabha and Heitler (1937), at a time when it was thought that such showers were initiated by electrons. The realization that protons and other nuclei were the primary particles led to a reformulation of the theory by Heitler and Janossy (1949), Messel and Green (1952) and others, in which the production of energetic pions and the three-dimensional development of air showers were accounted for. But as the soft (electromagnetic) component of the cosmic radiation is the most prominent feature of air showers at sea level, there has been a sustained interest in the theory of this component. Most of the more recent work, such as that by Butcher and Messel (1960) and Thielheim and Zöllner (1972) has relied on computer simulation; but this method has disadvantages in terms of accuracy and presentation of results, especially where a simultaneous analysis of the development of air showers in terms of several physical variables is required. This is so for instance when the time of arrival is one of the variables. Moyal (1956) played an important part in the analytical formulation of a stochastic theory of cosmic ray showers, with time as an explicit variable, and it is essentially this approach which will be adopted in the following. The actual distribution of arrival times is cosmic ray showers, for which results are obtained, is of current experimental interest (McDonald, Clay and Prescott (1977)).

scholarly journals Indoor path visualization method based on the spatial characteristics of indoor environment

2019 ◽  
Vol 1 ◽  
pp. 1-2
Author(s):  
Jiafeng Shi ◽  
Jie Shen ◽  
Zdeněk Stachoň ◽  
Yawei Chen
Keyword(s):  
Indoor Environment ◽  
Design Method ◽  
Three Dimensional ◽  
Indoor Environments ◽  
Route Guidance ◽  
Visualization Method ◽  
Continuous Space ◽  
Indoor Location ◽  
Indoor Space ◽  
Map Design

<p><strong>Abstract.</strong> With the increasing number of large buildings and more frequent indoor activities, indoor location-based service has expanded. Due to the complicated internal passages of large public buildings and the three-dimensional interlacing, it is difficult for users to quickly reach the destination, the demand of indoor paths visualization increases. Isikdag (2013), Zhang Shaoping (2017), Huang Kejia (2018) provided navigation services for users based on path planning algorithm. In terms of indoor path visualization, Nossum (2011) proposed a “Tubes” map design method, which superimposed the channel information of different floors on the same plane by simplifying the indoor corridor and the room. Lorenz et al (2013) focused on map perspective (2D/3D) and landmarks, developed and investigated cartographic methods for effective route guidance in indoor environments. Holscher et al (2007) emphasized using the landmark objects at the important decision points of the route in indoor map design. The existing studies mainly focused on two-dimensional plane to visualize the indoor path, lacking the analysis of three-dimensional connectivity in indoor space, which makes the intuitiveness and interactivity of path visualization greatly compromised. Therefore, it is difficult to satisfy the wayfinding requirements of the indoor multi-layer continuous space. In order to solve this problem, this paper aims to study the characteristics of the indoor environment and propose a path visualization method. The following questions are addressed in this study: 1) What are the key characteristics of the indoor environment compared to the outdoor space? 2) How to visualize the indoor paths to satisfy the users’ wayfinding needs?</p>

scholarly journals RECONSTRUCTION OF INDOOR MODELS USING POINT CLOUDS GENERATED FROM SINGLE-LENS REFLEX CAMERAS AND DEPTH IMAGES

2015 ◽  
Vol XL-4/W5 ◽  
pp. 99-102
Author(s):  
F. Tsai ◽  
T.-S. Wu ◽  
I.-C. Lee ◽  
H. Chang ◽  
A. Y. S. Su
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Three Dimensional ◽  
Point Clouds ◽  
Data Acquisition System ◽  
Acquisition System ◽  
Indoor Environments ◽  
Single Lens ◽  
Processing Procedure ◽  
Data Processing Procedure

This paper presents a data acquisition system consisting of multiple RGB-D sensors and digital single-lens reflex (DSLR) cameras. A systematic data processing procedure for integrating these two kinds of devices to generate three-dimensional point clouds of indoor environments is also developed and described. In the developed system, DSLR cameras are used to bridge the Kinects and provide a more accurate ray intersection condition, which takes advantage of the higher resolution and image quality of the DSLR cameras. Structure from Motion (SFM) reconstruction is used to link and merge multiple Kinect point clouds and dense point clouds (from DSLR color images) to generate initial integrated point clouds. Then, bundle adjustment is used to resolve the exterior orientation (EO) of all images. Those exterior orientations are used as the initial values to combine these point clouds at each frame into the same coordinate system using Helmert (seven-parameter) transformation. Experimental results demonstrate that the design of the data acquisition system and the data processing procedure can generate dense and fully colored point clouds of indoor environments successfully even in featureless areas. The accuracy of the generated point clouds were evaluated by comparing the widths and heights of identified objects as well as coordinates of pre-set independent check points against in situ measurements. Based on the generated point clouds, complete and accurate three-dimensional models of indoor environments can be constructed effectively.

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