scholarly journals Bayesian Multilateration

2021 ◽  
Author(s):  
Alisson Alencar ◽  
César Mattos ◽  
João Gomes ◽  
Diego Mesquita
Keyword(s):  
Bayesian Modeling ◽  
Likelihood Function ◽  
Position Estimation ◽  
Mobile Systems ◽  
Surveillance Systems ◽  
Real World Data ◽  
Point Estimates ◽  
Points Of Interest ◽  
Highly Sensitive ◽  
Multiple Measurements

Multilateration (MLAT) is the de facto standard to localize points of interest (POIs) in navigation and surveillance systems. Despite sensors being inherently noisy, most existing techniques i) are oblivious to noise patterns in sensor measurements; and ii) only provide point estimates of the POI’s location. This often results in unreliable estimates with high variance, i.e., that are highly sensitive to measurement noise. To overcome this caveat, we advocate the use of Bayesian modeling. Using Bayesian statistics, we provide a comprehensive guide to handle uncertainties in MLAT. We provide principled choices for the likelihood function and the prior distributions. Inference within the resulting model follows standard MCMC techniques. Besides coping with unreliable measurements, our framework can also deal with sensors whose location is not completely known, which is an asset in mobile systems. The proposed solution also naturally incorporates multiple measurements per reference point, a common practical situation that is usually not handled directly by other approaches. Comprehensive experiments with both synthetic and real-world data indicate that our Bayesian approach to the MLAT task provides better position estimation and uncertainty quantification when compared to the available alternatives.

scholarly journals Bayesian Multilateration

2021 ◽  
Author(s):  
Alisson Alencar ◽  
César Mattos ◽  
João Gomes ◽  
Diego Mesquita
Keyword(s):  
Bayesian Modeling ◽  
Likelihood Function ◽  
Position Estimation ◽  
Mobile Systems ◽  
Surveillance Systems ◽  
Real World Data ◽  
Point Estimates ◽  
Points Of Interest ◽  
Highly Sensitive ◽  
Multiple Measurements

Multilateration (MLAT) is the de facto standard to localize points of interest (POIs) in navigation and surveillance systems. Despite sensors being inherently noisy, most existing techniques i) are oblivious to noise patterns in sensor measurements; and ii) only provide point estimates of the POI’s location. This often results in unreliable estimates with high variance, i.e., that are highly sensitive to measurement noise. To overcome this caveat, we advocate the use of Bayesian modeling. Using Bayesian statistics, we provide a comprehensive guide to handle uncertainties in MLAT. We provide principled choices for the likelihood function and the prior distributions. Inference within the resulting model follows standard MCMC techniques. Besides coping with unreliable measurements, our framework can also deal with sensors whose location is not completely known, which is an asset in mobile systems. The proposed solution also naturally incorporates multiple measurements per reference point, a common practical situation that is usually not handled directly by other approaches. Comprehensive experiments with both synthetic and real-world data indicate that our Bayesian approach to the MLAT task provides better position estimation and uncertainty quantification when compared to the available alternatives.

scholarly journals Direct and Indirect TDOA Estimation based Multilateration System Position Estimation Accuracy Comparison

2021 ◽  
Vol 20 (1) ◽  
pp. 54-64
Author(s):  
Abdulmalik Shehu Yaro ◽  
Ahmad Zuri Sha'ameri ◽  
Sa’id Musa Yarima
Keyword(s):  
Electromagnetic Emission ◽  
Position Estimation ◽  
Civil Aviation ◽  
Estimation Accuracy ◽  
Surveillance Systems ◽  
Actual System ◽  
Time Difference Of Arrival ◽  
3 Dimensional ◽  
Accuracy Comparison ◽  
Better Than

Multilateration (MLAT) system estimate aircraft position from its electromagnetic emission using time difference of arrival (TDOA) estimated at ground receiving station (GRS)s with a lateration algorithm. The position estimation (PE) accuracy of the MLAT system depends on several factors one of which is the TDOA estimation approach. In this paper, the PE performance of a minimum configuration 3-dimensional (3-D) MLAT system based on the direct and indirect approaches to TDOA estimation is presented. The analysis is carried out using Monte Carlo simulation with the transmitter and receiver parameters based on an actual system used in the civil aviation. Simulation results show that within 150 km radius, the direct TDOA based MLAT system performs better than the indirect TDOA based MLAT system. Beyond 150 km radius, the indirect TDOA based MLAT system has the least PE error compared the direct TDOA based MLAT system. Further comparison of the MLAT system based on the two TDOA estimation approaches with other surveillance systems shows that the direct TDOA based MLAT system has the least PE error within 150 km radius while long-range aircraft PE beyond 150 km, automatic surveillance dependent broadcast (ADS-B) outperformed the MLAT system as it has the least PE error

Likelihood

2015 ◽  
Author(s):  
N. Thompson Hobbs ◽  
Mevin B. Hooten
Keyword(s):  
Maximum Likelihood ◽  
Probability Distribution ◽  
Bayesian Analysis ◽  
Bayesian Modeling ◽  
Likelihood Function ◽  
Bayesian Framework ◽  
Significant Aspect ◽  
Similarities And Differences ◽  
The Relationship ◽  
Bare Bones

This chapter is an overview of likelihood and maximum likelihood. Likelihood forms the fundamental link between models and data in the Bayesian framework. In addition, maximum likelihood is a widely used alternative to Bayesian methods for estimating parameters in ecological models. Though is possible to learn Bayesian modeling with a bare-bones treatment of likelihood, the chapter emphasizes the importance of this concept in Bayesian analysis. A significant aspect of likelihood within the Bayesian framework can be found in the similarities and differences between Bayesian analysis and analysis based on maximum likelihood. In addition, the chapter also considers the relationship between a probability distribution and a likelihood function.

scholarly journals Online Event Recognition from Moving Vehicles: Application Paper

2019 ◽  
Vol 19 (5-6) ◽  
pp. 841-856
Author(s):  
EFTHIMIS TSILIONIS ◽  
NIKOLAOS KOUTROUMANIS ◽  
PANAGIOTIS NIKITOPOULOS ◽  
CHRISTOS DOULKERIDIS ◽  
ALEXANDER ARTIKIS
Keyword(s):  
Event Recognition ◽  
Weather Data ◽  
External Information ◽  
Commercial Vehicles ◽  
Real World Data ◽  
Mobility Data ◽  
Composite Event ◽  
Points Of Interest ◽  
Moving Vehicles ◽  
Application Paper

AbstractWe present a system for online composite event recognition over streaming positions of commercial vehicles. Our system employs a data enrichment module, augmenting the mobility data with external information, such as weather data and proximity to points of interest. In addition, the composite event recognition module, based on a highly optimised logic programming implementation of the Event Calculus, consumes the enriched data and identifies activities that are beneficial in fleet management applications. We evaluate our system on large, real-world data from commercial vehicles, and illustrate its efficiency.

scholarly journals GPS: User Position Calculation Including Advanced Troposphere Delay Modeling

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1781-1787
Keyword(s):  
Real Time ◽  
Ray Tracing ◽  
Emergency Services ◽  
Signal Propagation ◽  
Position Estimation ◽  
Calculation Error ◽  
Surveillance Systems ◽  
Time Data ◽  
Position Errors ◽  
Mitigation Techniques

Objective of this project is to analyze and mitigate troposphere delays induced in GPS signals, which can result in very large position errors while estimating user position. The standard models currently present do not take into account all the various set of parameters or elements of the troposphere that can cause a significant delay. This project also includes study of troposphere propagation delays that improve the understanding of GPS signal propagation through the troposphere during irregular conditions. This characteristic is very important as it can play crucial role in real time surveying, navigation, precision farming and positioning for emergency services. Due to the tropical nature of the Indian climate the troposphere delay can be observed significantly in India sub-continent. In order to accurately estimate delay troposphere in real time conditions is taken into account, which are provided by the Indian meteorological department, by their automatic weather surveillance systems. GPS data for stations in India is obtained from CORS data for Bangalore, from where we obtain the observation and navigation files used in the calculations. Obtained data is processed and run through various algorithms like least squares satellite position calculation, error mitigation and ray tracing algorithms to mitigate troposphere and better estimate user position. Apart from these algorithms this project also includes a study on various concepts/formulas that help in using the forecasted real time data to be used in snell’s law to estimate delay as part of ray tracing techniques. All the code development in this project is done using MATLAB by math works and GUI is developed for an easier interface. For analysis purposes the data is analyzed with and without the advanced mitigation techniques to show the improvement in position estimation using advanced troposphere mitigation techniques.

scholarly journals Statistical methods for estimating historical fire frequency from multiple fire-scar data

2004 ◽  
Vol 34 (11) ◽  
pp. 2306-2313 ◽  
Author(s):  
William J Reed ◽  
Edward A Johnson
Keyword(s):  
Likelihood Function ◽  
Fire Frequency ◽  
Standard Errors ◽  
Binomial Model ◽  
Artificial Data ◽  
Expected Time ◽  
Fire Scar ◽  
Point Estimates ◽  
Approximate Confidence Intervals ◽  
Data Estimation

This paper considers the statistical analysis of fire-interval charts based on fire-scar data. Estimation of the fire interval (expected time between scar-registering fires at any location) by maximum likelihood is presented. Because fires spread, causing a lack of independence in scar registration at distinct sites, an overdispersed binomial model is used, leading to a two-variable quasi-likelihood function. From this, point estimates, standard errors, and approximate confidence intervals for fire interval and related quantities can be derived. Methods of testing for the significance of spatial and temporal differences are also discussed. A simple example using artificial data is given to illustrate the computational steps involved, and an analysis of real fire-scar data is presented.

scholarly journals Amplitude Modeling of Specular Multipath Components for Robust Indoor Localization

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 462
Author(s):  
Hong Anh Nguyen ◽  
Van Khang Nguyen ◽  
Klaus Witrisal
Keyword(s):  
Indoor Localization ◽  
Channel Model ◽  
Likelihood Function ◽  
Geometric Model ◽  
Gaussian Process Regression ◽  
Position Estimation ◽  
Channel Measurements ◽  
Channel Knowledge ◽  
Data Driven Approach ◽  
Potential Applicability

Ultra-Wide Bandwidth (UWB) and mm-wave radio systems can resolve specular multipath components (SMCs) from estimated channel impulse response measurements. A geometric model can describe the delays, angles-of-arrival, and angles-of-departure of these SMCs, allowing for a prediction of these channel features. For the modeling of the amplitudes of the SMCs, a data-driven approach has been proposed recently, using Gaussian Process Regression (GPR) to map and predict the SMC amplitudes. In this paper, the applicability of the proposed multipath-resolved, GPR-based channel model is analyzed by studying features of the propagation channel from a set of channel measurements. The features analyzed include the energy capture of the modeled SMCs, the number of resolvable SMCs, and the ranging information that could be extracted from the SMCs. The second contribution of the paper concerns the potential applicability of the channel model for a multipath-resolved, single-anchor positioning system. The predicted channel knowledge is used to evaluate the measurement likelihood function at candidate positions throughout the environment. It is shown that the environmental awareness created by the multipath-resolved, GPR-based channel model yields higher robustness against position estimation outliers.

scholarly journals WTP vs. WTA: Christmas Presents and the Endowment Effect

2012 ◽  
Vol 232 (1) ◽  
Author(s):  
Thomas K. Bauer ◽  
Christoph M. Schmidt
Keyword(s):  
Market Price ◽  
Experimental Studies ◽  
Endowment Effect ◽  
Point Estimates ◽  
Highly Sensitive ◽  
Subject Pool ◽  
The Subject ◽  
Males And Females ◽  
The Difference ◽  
Using Data

SummaryUsing data on the valuation of Christmas gifts received by students enrolled in different fields at a German university, we investigate whether the endowment effect, the difference between asking and bidding prices, differs between males and females, students of economics and other fields and whether it varies with the market price of the object under consideration. Our estimation results suggest that female students display a significantly higher endowment effect, indicating that experimental studies on the endowment effect are highly sensitive towards the choice of the subject pool. The results further indicate that the endowment effect depends on the market price of the object. Finally, even though the point estimates hint towards both, a lower WTP and WTA of economic students, these differences are not statistically significant at conventional levels.

scholarly journals Time Series Measurement Based on Multiscale High Order Entropy and Roughness Grain Exponents

2021 ◽  
Author(s):  
Meng Xu ◽  
Pengjian Shang ◽  
Sheng Zhang
Keyword(s):  
Time Series ◽  
High Order ◽  
Multiscale Entropy ◽  
Real World Data ◽  
Broad Perspective ◽  
Highly Sensitive ◽  
The Difference ◽  
Synthetic Time Series ◽  
Series Measurement ◽  
Time Series Measurement

Abstract In multiscale time series analysis, multiscale entropy provides a good framework to quantify the information of time series. Multiscale fractional high-order entropy based on roughness grain exponents (MFHER) is able to identify dynamical, scale dependent and oscillation information. In detail, MFHERcan be seen as a powerful tool to assess the complex characteristics of time series. A set of synthetic time series and an application of real world data financial series are researched. The results show that high order entropy performs well in distinguishing different time series. It has also been found fractional high order entropy is highly sensitive to parameter variation and thus provides a broad perspective to research the complexity of dynamic systems. This study gains an insight into the measurement of MFHER to demonstrate the wide applicability of entropy measures.Aiming at the complexity of network and the uncertainty of internal and external environment, this paper proposes MFHER to quantify the time series information on multiscale time scales. It is of great interests in identifying dynamical properties of nancial series. The results show that the volatility of the sequence is gradually stable when the scale is greater than four. High order entropy can identify the difference among the time series.

scholarly journals Multi-page Labeling of Small-screen Maps with a Graph-coloring Approach

2019 ◽  
Vol 2 ◽  
pp. 1-8
Author(s):  
Sven Gedicke ◽  
Benjamin Niedermann ◽  
Jan-Henrik Haunert
Keyword(s):  
Graph Coloring ◽  
Web Applications ◽  
Maximum Size ◽  
Greedy Heuristic ◽  
Optimal Solutions ◽  
Real World Data ◽  
Simple Strategy ◽  
Points Of Interest ◽  
Labeling Approach ◽  
Small Screen

Abstract. Annotating small-screen maps with additional content such as labels for points of interest is a highly challenging problem that requires new algorithmic solutions. A common labeling approach is to select a maximum-size subset of all labels such that no two labels constitute a graphical conflict and to display only the selected labels in the map. A disadvantage of this approach is that a user often has to zoom in and out repeatedly to access all points of interest in a certain region. Since this can be very cumbersome, we suggest an alternative approach that allows the scale of the map to be kept fixed. Our approach is to distribute all labels on multiple pages through which the user can navigate, for example, by swiping the pages from right to left. We in particular optimize the assignment of the labels to pages such that no page contains two conflicting labels, more important labels appear on the first pages, and sparsely labeled pages are avoided. Algorithmically, we reduce this problem to a weighted and constrained graph coloring problem based on a graph representing conflicts between labels such that an optimal coloring of the graph corresponds to a multi-page labeling. We propose a simple greedy heuristic that is fast enough to be deployed in web-applications. We evaluate the quality of the obtained labelings by comparing them with optimal solutions, which we obtain by means of integer linear programming formulations. In our evaluation on real-world data we particularly show that the proposed heuristic achieves near-optimal solutions with respect to the chosen objective function and that it substantially improves the legibility of the labels in comparison to the simple strategy of assigning the labels to pages solely based on the labels’ weights.

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