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Abdelhak M. Zoubir - One of the best experts on this subject based on the ideXlab platform.
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Bayesian Cooperative Localization Using Received Signal Strength With Unknown Path Loss Exponent: Message Passing Approaches
IEEE Transactions on Signal Processing, 2020Co-Authors: Carsten Fritsche, Fredrik Gustafsson, Abdelhak M. ZoubirAbstract:We propose a Bayesian framework for the Received-Signal-Strength-based cooperative localization problem with unknown path loss exponent. Our purpose is to infer the marginal posterior of each unknown parameter: the position or the path loss exponent. This probabilistic inference problem is solved using message passing algorithms that update messages and beliefs iteratively. For numerical tractability, we combine the variable discretization and Monte-Carlo-based numerical approximation schemes. To further improve computational efficiency, we develop an auxiliary importance sampler that updates the beliefs with the help of an auxiliary variable. An important ingredient of the proposed auxiliary importance sampler is the ability to sample from a normalized likelihood function. To this end, we develop a stochastic sampling strategy that mathematically interprets and corrects an existing heuristic strategy. The proposed message passing algorithms are analyzed systematically in terms of computational complexity, demonstrating the computational efficiency of the proposed auxiliary importance sampler. Various simulations are conducted to validate the overall good performance of the proposed algorithms.
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bayesian cooperative localization using Received Signal Strength with unknown path loss exponent message passing approaches
arXiv: Signal Processing, 2019Co-Authors: Carsten Fritsche, Fredrik Gustafsson, Abdelhak M. ZoubirAbstract:We propose a Bayesian framework for the Received-Signal-Strength-based cooperative localization problem with unknown path loss exponent. Our purpose is to infer the marginal posterior of each unknown parameter: the position or the path loss exponent. This probabilistic inference problem is solved using message passing algorithms that update messages and beliefs iteratively. To enable the numerical tractability, we combine the variable discretization and Monte-Carlo-based numerical approximation schemes. To further improve computational efficiency, we develop an auxiliary importance sampler that updates the beliefs with the help of an auxiliary variable. To sample from a normalized likelihood function, which is an important ingredient of the proposed auxiliary importance sampler, we develop a stochastic sampling strategy that mathematically interprets and corrects an existing heuristic strategy. The proposed message passing algorithms are analyzed systematically in terms of computational complexity, demonstrating the computational efficiency of the proposed auxiliary importance sampler. Various simulations are conducted to validate the overall good performance of the proposed algorithms.
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Received Signal Strength based joint parameter estimation algorithm for robust geolocation in los nlos environments
International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Feng Yin, Fredrik Gustafsson, Carsten Fritsche, Abdelhak M. ZoubirAbstract:We consider Received-Signal-Strength-based robust geolocation in mixed line-of-sight/non-line-of-sight propagation environments. Herein, we assume a mode-dependent propagation model with unknown parameters. We propose to jointly estimate the geographical coordinates and propagation model parameters. In order to approximate the maximum-likelihood estimator (MLE), we develop an iterative algorithm based on the well-known expectation and maximization criterion. As compared to the standard ML implementation, the proposed algorithm is simpler to implement and capable of reproducing the MLE. Simulation results show that the proposed algorithm attains the best geolocation accuracy as the number of measurements increases.
Fredrik Gustafsson - One of the best experts on this subject based on the ideXlab platform.
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Bayesian Cooperative Localization Using Received Signal Strength With Unknown Path Loss Exponent: Message Passing Approaches
IEEE Transactions on Signal Processing, 2020Co-Authors: Carsten Fritsche, Fredrik Gustafsson, Abdelhak M. ZoubirAbstract:We propose a Bayesian framework for the Received-Signal-Strength-based cooperative localization problem with unknown path loss exponent. Our purpose is to infer the marginal posterior of each unknown parameter: the position or the path loss exponent. This probabilistic inference problem is solved using message passing algorithms that update messages and beliefs iteratively. For numerical tractability, we combine the variable discretization and Monte-Carlo-based numerical approximation schemes. To further improve computational efficiency, we develop an auxiliary importance sampler that updates the beliefs with the help of an auxiliary variable. An important ingredient of the proposed auxiliary importance sampler is the ability to sample from a normalized likelihood function. To this end, we develop a stochastic sampling strategy that mathematically interprets and corrects an existing heuristic strategy. The proposed message passing algorithms are analyzed systematically in terms of computational complexity, demonstrating the computational efficiency of the proposed auxiliary importance sampler. Various simulations are conducted to validate the overall good performance of the proposed algorithms.
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bayesian cooperative localization using Received Signal Strength with unknown path loss exponent message passing approaches
arXiv: Signal Processing, 2019Co-Authors: Carsten Fritsche, Fredrik Gustafsson, Abdelhak M. ZoubirAbstract:We propose a Bayesian framework for the Received-Signal-Strength-based cooperative localization problem with unknown path loss exponent. Our purpose is to infer the marginal posterior of each unknown parameter: the position or the path loss exponent. This probabilistic inference problem is solved using message passing algorithms that update messages and beliefs iteratively. To enable the numerical tractability, we combine the variable discretization and Monte-Carlo-based numerical approximation schemes. To further improve computational efficiency, we develop an auxiliary importance sampler that updates the beliefs with the help of an auxiliary variable. To sample from a normalized likelihood function, which is an important ingredient of the proposed auxiliary importance sampler, we develop a stochastic sampling strategy that mathematically interprets and corrects an existing heuristic strategy. The proposed message passing algorithms are analyzed systematically in terms of computational complexity, demonstrating the computational efficiency of the proposed auxiliary importance sampler. Various simulations are conducted to validate the overall good performance of the proposed algorithms.
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Received Signal Strength threshold optimization using gaussian processes
IEEE Transactions on Signal Processing, 2017Co-Authors: Feng Yin, Yuxin Zhao, Fredrik Gunnarsson, Fredrik GustafssonAbstract:There is a big trend nowadays to use event-triggered proximity report for indoor positioning. This paper presents a generic Received-Signal-Strength (RSS) threshold optimization framework for generating informative proximity reports. The proposed framework contains five main building blocks, namely the deployment information, RSS model, positioning metric selection, optimization process and management. Among others, we focus on Gaussian process regression (GPR)-based RSS models and positioning metric computation. The optimal RSS threshold is found through minimizing the best achievable localization root-mean-square-error formulated with the aid of fundamental lower bound analysis. Computational complexity is compared for different RSS models and different fundamental lower bounds. The resulting optimal RSS threshold enables enhanced performance of new fashioned low-cost and low-complex proximity report-based positioning algorithms. The proposed framework is validated with real measurements collected in an office area where bluetooth-low-energy (BLE) beacons are deployed.
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Received Signal Strength based joint parameter estimation algorithm for robust geolocation in los nlos environments
International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Feng Yin, Fredrik Gustafsson, Carsten Fritsche, Abdelhak M. ZoubirAbstract:We consider Received-Signal-Strength-based robust geolocation in mixed line-of-sight/non-line-of-sight propagation environments. Herein, we assume a mode-dependent propagation model with unknown parameters. We propose to jointly estimate the geographical coordinates and propagation model parameters. In order to approximate the maximum-likelihood estimator (MLE), we develop an iterative algorithm based on the well-known expectation and maximization criterion. As compared to the standard ML implementation, the proposed algorithm is simpler to implement and capable of reproducing the MLE. Simulation results show that the proposed algorithm attains the best geolocation accuracy as the number of measurements increases.
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fingerprinting localization in wireless networks based on Received Signal Strength measurements a case study on wimax networks
IEEE Transactions on Vehicular Technology, 2010Co-Authors: Mussa Bshara, Fredrik Gustafsson, Umut Orguner, L Van BiesenAbstract:This paper considers the problem of fingerprinting localization in wireless networks based on Received-Signal-Strength (RSS) observations. First, the performance of static localization using power maps (PMs) is improved with a new approach called the base-station-strict (BS-strict) methodology, which emphasizes the effect of BS identities in the classical fingerprinting. Second, dynamic motion models with and without road network information are used to further improve the accuracy via particle filters. The likelihood-calculation mechanism proposed for the particle filters is interpreted as a soft version (called BS-soft) of the BS-strict approach applied in the static case. The results of the proposed approaches are illustrated and compared with an example whose data were collected from a WiMAX network in a challenging urban area in the capitol city of Brussels, Belgium.
Carsten Fritsche - One of the best experts on this subject based on the ideXlab platform.
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Bayesian Cooperative Localization Using Received Signal Strength With Unknown Path Loss Exponent: Message Passing Approaches
IEEE Transactions on Signal Processing, 2020Co-Authors: Carsten Fritsche, Fredrik Gustafsson, Abdelhak M. ZoubirAbstract:We propose a Bayesian framework for the Received-Signal-Strength-based cooperative localization problem with unknown path loss exponent. Our purpose is to infer the marginal posterior of each unknown parameter: the position or the path loss exponent. This probabilistic inference problem is solved using message passing algorithms that update messages and beliefs iteratively. For numerical tractability, we combine the variable discretization and Monte-Carlo-based numerical approximation schemes. To further improve computational efficiency, we develop an auxiliary importance sampler that updates the beliefs with the help of an auxiliary variable. An important ingredient of the proposed auxiliary importance sampler is the ability to sample from a normalized likelihood function. To this end, we develop a stochastic sampling strategy that mathematically interprets and corrects an existing heuristic strategy. The proposed message passing algorithms are analyzed systematically in terms of computational complexity, demonstrating the computational efficiency of the proposed auxiliary importance sampler. Various simulations are conducted to validate the overall good performance of the proposed algorithms.
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bayesian cooperative localization using Received Signal Strength with unknown path loss exponent message passing approaches
arXiv: Signal Processing, 2019Co-Authors: Carsten Fritsche, Fredrik Gustafsson, Abdelhak M. ZoubirAbstract:We propose a Bayesian framework for the Received-Signal-Strength-based cooperative localization problem with unknown path loss exponent. Our purpose is to infer the marginal posterior of each unknown parameter: the position or the path loss exponent. This probabilistic inference problem is solved using message passing algorithms that update messages and beliefs iteratively. To enable the numerical tractability, we combine the variable discretization and Monte-Carlo-based numerical approximation schemes. To further improve computational efficiency, we develop an auxiliary importance sampler that updates the beliefs with the help of an auxiliary variable. To sample from a normalized likelihood function, which is an important ingredient of the proposed auxiliary importance sampler, we develop a stochastic sampling strategy that mathematically interprets and corrects an existing heuristic strategy. The proposed message passing algorithms are analyzed systematically in terms of computational complexity, demonstrating the computational efficiency of the proposed auxiliary importance sampler. Various simulations are conducted to validate the overall good performance of the proposed algorithms.
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Received Signal Strength based joint parameter estimation algorithm for robust geolocation in los nlos environments
International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Feng Yin, Fredrik Gustafsson, Carsten Fritsche, Abdelhak M. ZoubirAbstract:We consider Received-Signal-Strength-based robust geolocation in mixed line-of-sight/non-line-of-sight propagation environments. Herein, we assume a mode-dependent propagation model with unknown parameters. We propose to jointly estimate the geographical coordinates and propagation model parameters. In order to approximate the maximum-likelihood estimator (MLE), we develop an iterative algorithm based on the well-known expectation and maximization criterion. As compared to the standard ML implementation, the proposed algorithm is simpler to implement and capable of reproducing the MLE. Simulation results show that the proposed algorithm attains the best geolocation accuracy as the number of measurements increases.
Youming Li - One of the best experts on this subject based on the ideXlab platform.
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On Received-Signal-Strength Based Localization with Unknown Transmit Power and Path Loss Exponent
IEEE Wireless Communications Letters, 2012Co-Authors: Gang Wang, Youming LiAbstract:In this letter, we consider the Received-Signal-Strength (RSS) based localization problem with unknown transmit power and unknown path loss exponent (PLE). For the case of unknown transmit power, we derive a weighed least squares (WLS) formulation to jointly estimate the sensor node location and the transmit power, based on the unscented transformation (UT). For the case of unknown PLE, we propose an alternating estimation procedure to alternatively estimate the sensor node location and the PLE. The estimation procedure can also be applied to the case when both the transmit power and the PLE are unknown. Simulation results confirm the effectiveness of the proposed method.
Stephen Farrell - One of the best experts on this subject based on the ideXlab platform.
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coronavirus contact tracing evaluating the potential of using bluetooth Received Signal Strength for proximity detection
Computer Communication Review, 2020Co-Authors: Douglas J Leith, Stephen FarrellAbstract:Many countries are deploying Covid-19 contact tracing apps that use Bluetooth Low Energy (LE) to detect proximity within 2m for 15 minutes. However, Bluetooth LE is an unproven technology for this application, raising concerns about the efficacy of these apps. Indeed, measurements indicate that the Bluetooth LE Received Signal Strength can be strongly affected by factors including (i) the model of handset used, (ii) the relative orientation of handsets, (iii) absorption by human bodies, bags etc. and (iv) radio wave reflection from walls, floors, furniture. The impact on Received Signal Strength is comparable with that caused by moving 2m, and so has the potential to seriously affect the reliability of proximity detection. These effects are due the physics of radio propagation and suggest that the development of accurate methods for proximity detection based on Bluetooth LE Received Signal Strength is likely to be challenging. We call for action in three areas. Firstly, measurements are needed that allow the added value of deployed apps within the overall contact tracing system to be evaluated, e.g. data on how many of the people notified by the app would not have been found by manual contact tracing and what fraction of people notified by an app actually test positive for Covid-19. Secondly, the 2m/15 minute proximity limit is only a rough guideline. The real requirement is to use handset sensing to evaluate infection risk and this requires a campaign to collect measurements of both handset sensor data and infection outcomes. Thirdly, a concerted effort is needed to collect controlled Bluetooth LE measurements in a wide range of real-world environments, the data reported here being only a first step in that direction.
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coronavirus contact tracing evaluating the potential of using bluetooth Received Signal Strength for proximity detection
arXiv: Signal Processing, 2020Co-Authors: Douglas J Leith, Stephen FarrellAbstract:We report on measurements of Bluetooth Low Energy (LE) Received Signal Strength taken on mobile handsets in a variety of common, real-world settings. We note that a key difficulty is obtaining the ground truth as to when people are in close proximity to one another. Knowledge of this ground truth is important for accurately evaluating the accuracy with which contact events are detected by Bluetooth LE. We approach this by adopting a scenario-based approach. In summary, we find that the Bluetooth LE Received Signal Strength can vary substantially depending on the relative orientation of handsets, on absorption by the human body, reflection/absorption of radio Signals in buildings and trains. Indeed we observe that the Received Signal Strength need not decrease with increasing distance. This suggests that the development of accurate methods for proximity detection based on Bluetooth LE Received Signal Strength is likely to be challenging. Our measurements also suggest that combining use of Bluetooth LE contact tracing apps with adoption of new social protocols may yield benefits but this requires further investigation. For example, placing phones on the table during meetings is likely to simplify proximity detection using Received Signal Strength. Similarly, carrying handbags with phones placed close to the outside surface. In locations where the complexity of Signal propagation makes proximity detection using Received Signal Strength problematic entry/exit from the location might instead be logged in an app by e.g. scanning a time-varying QR code or the like.
