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Scalable Indoor Localization via Mobile Crowdsourcing and Gaussian Process

SENSORS 2016年第3期16卷 381-381页

作者： Chang, Qiang Li, Qun Shi, Zesen Chen, Wei Wang, Weiping Natl Univ Def Technol Coll Informat Syst & Management Changsha 410073 Hunan Peoples R China

Indoor localization using Received Signal Strength Indication (RSSI) fingerprinting has been extensively studied for decades. The positioning accuracy is highly dependent on the density of the signal database. In areas without calibration data, however, this algorithm breaks down. Building and updating a dense signal database is labor intensive, expensive, and even impossible in some areas. Researchers are continually searching for better algorithms to create and update dense databases more efficiently. In this paper, we propose a scalable indoor positioning algorithm that works both in surveyed and unsurveyed areas. We first propose Minimum Inverse Distance (MID) algorithm to build a virtual database with uniformly distributed virtual Reference Points (RP). The area covered by the virtual RPs can be larger than the surveyed area. A Local Gaussian Process (LGP) is then applied to estimate the virtual RPs' RSSI values based on the crowdsourced training data. Finally, we improve the bayesian algorithm to estimate the user's location using the virtual database. All the parameters are optimized by simulations, and the new algorithm is tested on real-case scenarios. The results show that the new algorithm improves the accuracy by 25.5% in the surveyed area, with an average positioning error below 2.2 m for 80% of the cases. Moreover, the proposed algorithm can localize the users in the neighboring unsurveyed area.

关键词： radio map bayesian algorithm gaussian process indoor localization mobile crowdsourcing WLAN

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bayesian Sparse Estimation of a Radar Scene with Weak and Strong Targets 3

Bayesian Sparse Estimation of a Radar Scene with Weak and St...

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3rd International Workshop on Compressed Sensing Theory and its Application to Radar, Sonar, and Remote Sensing (CoSeRa)

作者： Lasserre, Marie Bidon, Stephanie Besson, Olivier Le Chevalier, Francois Univ Toulouse DEOS ISAE Toulouse France Delft Univ Technol MS3 Delft Netherlands

ISBN: (纸本)9781479974207

We consider the problem of estimating a finite number of atoms from a dictionary embedded in white noise, using a sparse signal representation (SSR) approach, a problem which is relevant in many radar applications. In particular, the estimation of a radar scene consisting of targets with wide amplitude range can be challenging since the sidelobes of a strong target can disrupt the estimation of a weak one. In this paper, we present a bayesian algorithm able to estimate weak targets possibly hidden by strong ones. The main strength of this algorithm lies in a novel sparse-promoting prior distribution which decorrelates sparsity level and target power and makes the estimation process span the whole target power range. This algorithm is implemented through a Monte-Carlo Markov chain. It is successfully evaluated on synthetic and semiexperimental radar data.

关键词： Bayes methods Markov processes Monte Carlo methods compressed sensing estimation theory radar signal processing signal representation white noise bayesian algorithm bayesian sparse estimation Monte-Carlo Markov chain SSR estimation process radar applications radar data radar scene sparse signal representation sparse-promoting prior distribution sparsity level target power Compressed sensing Conferences Estimation Radar remote sensing white noise Bayes methods Compressed sensing signal representation Radar applications radar data Radar remote sensing Radar signal processing estimation theory Markov chain Monte Carlo technique semiconductor relays Targets

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Nonlinear regression using smooth bayesian estimation 40

Nonlinear regression using smooth Bayesian estimation

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Halimi, Abderrahim Mailhes, Corinne Tourneret, Jean-Yves University of Toulouse IRIT/INP-ENSEEIHT/TéSA France

ISBN: (纸本)9781467369978

This paper proposes a new bayesian strategy for the estimation of smooth parameters from nonlinear models. The observed signal is assumed to be corrupted by an independent and non identically (colored) Gaussian distribution. A prior enforcing a smooth temporal evolution of the model parameters is considered. The joint posterior distribution of the unknown parameter vector is then derived. A Gibbs sampler coupled with a Hamiltonian Monte Carlo algorithm is proposed which allows samples distributed according to the posterior of interest to be generated and to estimate the unknown model parameters/hyperparameters. Simulations conducted with synthetic and real satellite altimetric data show the potential of the proposed bayesian model and the corresponding estimation algorithm for nonlinear regression with smooth estimated parameters. © 2015 IEEE.

关键词： bayesian algorithm Hamiltonian Monte-Carlo MCMC Parameter estimation Radar altimetry

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A prototype precipitation retrieval algorithm over land using passive microwave observations stratified by surface condition and precipitation vertical structure

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JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 2015年第11期120卷 5295-5315页

作者： You, Yalei Wang, Nai-Yu Ferraro, Ralph Univ Maryland Cooperat Inst Climate & Satellite College Pk MD 20742 USA NOAA IMSG STAR NESDIS College Pk MD USA NOAA NESDIS STAR College Pk MD USA

A prototype precipitation retrieval algorithm over land has been developed by utilizing 4year National Mosaic and Multi-Sensor Quantitative Precipitation Estimation and Special Sensor Microwave Imager/Sounder coincident data sets. One of the unique features of this algorithm is using the ancillary parameters (i.e., surface type, surface temperature, land elevation, and ice layer thickness) to stratify the single database into many smaller but more homogeneous databases, in which both the surface condition and precipitation vertical structure are similar. It is found that the probability of detection (POD) increases about 8% and 12% by using stratified databases for rainfall and snowfall detection, respectively. In addition, by considering the relative humidity at lower troposphere and the vertical velocity at 700 hPa in the precipitation detection process, the POD for snowfall detection is further increased by 20.4% from 56.0% to 76.4%. The better result is evident in both ends of the retrieved rain rate when the stratified databases are used, especially when the rain rate is greater than 30 mm/h. Similarly, the retrieved snowfall rate using stratified databases also outperforms that using single database. The correlation between retrieved and observed rain rates from stratified databases is 0.63, while it is 0.42 using the single database. The root-mean-square error is reduced by 50.3% from 2.07 to 0.98 by using stratified databases. The retrieved snow rates from stratified database are also better correlated with observations and possess smaller root-mean-square error. Additionally, the precipitation overestimation from the single database over the western United States is largely mitigated when the stratified databases are utilized. It is further demonstrated that over the majority of the stratified databases, the relationship between precipitation rate and brightness temperature is much closer to that from the corresponding category in the validation databases,

关键词： precipitation retrieval database stratification bayesian algorithm

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Anomaly Network Intrusion Detection Based on Improved Self Adaptive bayesian algorithm

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JOURNAL OF COMPUTERS 2010年第1期5卷 23-31页

作者： Farid, Dewan Md. Rahman, Mohammad Zahidur Jahangirnagar Univ Dept CSE Dhaka 1342 Bangladesh

Recently, research on intrusion detection in computer systems has received much attention to the computational intelligence society. Many intelligence learning algorithms applied to the huge volume of complex and dynamic dataset for the construction of efficient intrusion detection systems (IDSs). Despite of many advances that have been achieved in existing IDSs, there are still some difficulties, such as correct classification of large intrusion detection dataset, unbalanced detection accuracy in the high speed network traffic, and reduce false positives. This paper presents a new approach to the alert classification to reduce false positives in intrusion detection using improved self adaptive bayesian algorithm (ISABA). The proposed approach applied to the security domain of anomaly based network intrusion detection, which correctly classifies different types of attacks of KDD99 benchmark dataset with high classification rates in short response time and reduce false positives using limited computational resources.

关键词： anomaly detection network intrusion detection alert classification bayesian algorithm detection rate false positives

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A Substation Fault Diagnosis Method Based on IEC61850

A Substation Fault Diagnosis Method Based on IEC61850

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General Meeting of the IEEE-Power-and-Energy-Society

作者： Gao Zhanjun Wang Junshan Gao Nuo Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250100 Shandong Peoples R China Shandong Jianzhu Univ Shandong Jianzhu Univ Sch Informat & Elect Jinan Shandong Peoples R China

ISBN: (纸本)9781467380409

Substations play an important role in power systems. The application of the IEC6I850 standard changes the situation of the traditional substation greatly. The information in the intelligent substation is rich, but it is difficult for the operators to digest them in a short time without an effective assistant tool. A fault diagnosis method which uses the alarming information of the primary and secondary system of the intelligent substation is proposed in this paper. This method first uses the bayesian based algorithm to find the possible faulty set HI. And at the same time, the information entropy difference algorithm is used to obtain the fault assumption set H2. Then the fault assumption set H2 is used to calculate the fault correctness and a decision fusion algorithm can locate the fault components accurately. Finally, a fault scenario is served for demonstrating the feasibility and validity of the method presented.

关键词： Intelligent Substation Alarm Information bayesian algorithm Information Entropy Difference Decision Fusion

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UNMIXING MULTITEMPORAL HYPERSPECTRAL IMAGES ACCOUNTING FOR ENDMEMBER VARIABILITY 23

UNMIXING MULTITEMPORAL HYPERSPECTRAL IMAGES ACCOUNTING FOR E...

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23rd European Signal Processing Conference (EUSIPCO)

作者： Halimi, Abderrahim Dobigeon, Nicolas Tourneret, Jean-Yves McLaughlin, Steve Honeine, Paul Univ Technol Troyes Inst Charles Delaunay CNRS Troyes France Univ Toulouse IRIT INP ENSEEIHT TeSA Toulouse France Heriot Watt Univ Sch Engn & Phys Sci Edinburgh Midlothian Scotland

ISBN: (纸本)9780992862633

This paper proposes an unsupervised bayesian algorithm for unmixing successive hyperspectral images while accounting for temporal and spatial variability of the endmembers. Each image pixel is modeled as a linear combination of the end members weighted by their corresponding abundances. Spatial endmember variability is introduced by considering the normal compositional model that assumes variable endmemhers for each image pixel. A prior enforcing a smooth temporal variation of both endmembers and abundances is considered. The proposed algorithm estimates the mean vectors and covariance matrices of the endmembers and the abundances associated with each image. Since the estimators are difficult to express in closed form, we propose to sample according to the posterior distribution of interest and use the generated samples to build estimators. The performance of the proposed bayesian model and the corresponding estimation algorithm is evaluated by comparison with other unmixing algorithms on synthetic images.

关键词： Hyperspectral unmixing spectral variability temporal and spatial variability bayesian algorithm Hamiltonian Monte-Carlo MCMC methods

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Nonlinear regression using smooth bayesian estimation

Nonlinear regression using smooth Bayesian estimation

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： A. Halimi C. Mailhes J. Y. Tourneret Univ. of Toulouse Toulouse France

ISBN: (纸本)9781467369985

关键词： bayesian algorithm Hamiltonian Monte-Carlo MCMC Parameter estimation Radar altimetry

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Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2015年第12期24卷 4904-4917页

作者： Halimi, Abderrahim Dobigeon, Nicolas Tourneret, Jean-Yves Univ Toulouse F-31071 Toulouse France

This paper presents an unsupervised bayesian algorithm for hyperspectral image unmixing, accounting for endmember variability. The pixels are modeled by a linear combination of endmembers weighted by their corresponding abundances. However, the endmembers are assumed random to consider their variability in the image. An additive noise is also considered in the proposed model, generalizing the normal compositional model. The proposed algorithm exploits the whole image to benefit from both spectral and spatial information. It estimates both the mean and the covariance matrix of each endmember in the image. This allows the behavior of each material to be analyzed and its variability to be quantified in the scene. A spatial segmentation is also obtained based on the estimated abundances. In order to estimate the parameters associated with the proposed bayesian model, we propose to use a Hamiltonian Monte Carlo algorithm. The performance of the resulting unmixing strategy is evaluated through simulations conducted on both synthetic and real data.

关键词： Hyperspectral imagery endmember variability image classification spectral unmixing bayesian algorithm Hamiltonian Monte-Carlo MCMC methods

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UNMIXING MULTITEMPORAL HYPERSPECTRAL IMAGES ACCOUNTING FOR ENDMEMBER VARIABILITY

UNMIXING MULTITEMPORAL HYPERSPECTRAL IMAGES ACCOUNTING FOR E...

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European Signal Processing Conference

作者： Abderrahim Halimi Nicolas Dobigeon Jean-Yves Tourneret Steve McLaughlin Paul Honeine Institut Charles Delaunay (CNRS) Universite de Technologie de Troyes University of Toulouse IRIT/INP-ENSEEIHT/TeSA Heriot-Watt University School of Engineering and Physical Sciences

ISBN: (纸本)9781479988518

This paper proposes an unsupervised bayesian algorithm for unmixing successive hyperspectral images while accounting for temporal and spatial variability of the endmembers. Each image pixel is modeled as a linear combination of the end-members weighted by their corresponding abundances. Spatial endmember variability is introduced by considering the normal compositional model that assumes variable endmembers for each image pixel. A prior enforcing a smooth temporal variation of both endmembers and abundances is considered. The proposed algorithm estimates the mean vectors and covariance matrices of the endmembers and the abundances associated with each image. Since the estimators are difficult to express in closed form, we propose to sample according to the posterior distribution of interest and use the generated samples to build estimators. The performance of the proposed bayesian model and the corresponding estimation algorithm is evaluated by comparison with other unmixing algorithms on synthetic images.

关键词： Hyperspectral unmixing Spectral variability Temporal and spatial variability bayesian algorithm Hamiltonian Monte-Carlo MCMC methods Markov chain monte carlo methods images Posterior distribution accounting Imagery (Psychotherapy) exsolution Estimators Linear Combination closed forms Image

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