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distributed Fault Diagnosis With Overlapping Decompositions: An Adaptive Approximation Approach

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2009年第4期54卷 794-799页

作者： Ferrari, Riccardo M. G. Parisini, Thomas Polycarpou, Marios M. DEEI Univ Trieste Dept Elect Elect & Comp Engn Trieste Italy Univ Cyprus Dept Elect & Comp Engn KIOS Res Ctr Intelligent Syst & Networks CY-1678 Nicosia Cyprus

This technical note deals with the problem of designing a distributed fault detection methodology for distributed (and possibly large-scale) nonlinear dynamical systems that are modelled as the interconnection of several subsystems. The subsystems are allowed to overlap, thus sharing some state components. For each subsystem, a Local Fault Detector is designed, based on the measured local state of the subsystem as well as the transmitted variables of neighboring states that define the subsystem interconnections. The local detection decision is made on the basis of the knowledge of the local subsystem dynamic model and of an adaptive approximation of the interconnection with neighboring subsystems. The use of a specially-designed consensus-based estimator is proposed in order to improve the detectability of faults affecting variables shared among different subsystems. Simulation results provide an evidence of the effectiveness of the proposed distributed fault detection scheme.

关键词： Adaptive estimation distributed detection fault diagnosis large-scale systems nonlinear systems

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distributed In-Network Channel Decoding

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2009年第10期57卷 3970-3983页

作者： Zhu, Hao Giannakis, Georgios B. Cano, Alfonso Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

Average log-likelihood ratios (LLRs) constitute sufficient statistics for centralized maximum-likelihood block decoding as well as for a posteriori probability evaluation which enables bit-wise (possibly iterative) decoding. By acquiring such average LLRs per sensor it becomes possible to perform these decoding tasks in a low-complexity distributed fashion using wireless sensor networks. At affordable communication overhead, the resultant distributed decoders rely on local message exchanges among single-hop neighboring sensors to achieve iteratively consensus on the average LLRs per sensor. Furthermore, the decoders exhibit robustness to non-ideal inter-sensor links affected by additive noise and random link failures. Pairwise error probability bounds benchmark the decoding performance as a function of the number of consensus iterations. Interestingly, simulated tests corroborating the analytical findings demonstrate that only a few consensus iterations suffice for the novel distributed decoders to approach the performance of their centralized counterparts.

关键词： Channel coding decoding distributed detection wireless sensor networks (WSNs)

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Further Results on the Optimality of the Likelihood-Ratio Test for Local Sensor Decision Rules in the Presence of Nonideal Channels

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IEEE TRANSACTIONS ON INFORMATION THEORY 2009年第2期55卷 828-832页

作者： Chen, Hao Chen, Biao Varshney, Pramod K. Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

In this paper, we consider the design of local decision rules for distributed detection systems where decisions from peripheral detectors are transmitted over dependent nonideal channels. Under the conditional independence assumption among multiple sensor observations, we show that the optimal detection performance can be achieved by employing likelihood-ratio quantizers (LRQ) as local decision rules under both the Bayesian criterion and Neyman-Pearson (NP) criterion even for the cases where the channels between the fusion center and local sensors are dependent and noisy. This work generalizes the previous work where independence among such channels was assumed. A person-by-person optimization (PBPO) procedure to obtain the solution is presented along with an illustrative example.

关键词： Bayesian criterion distributed detection likelihood-ratio quantizers (LRQs) Neyman-Pearson (NP) criterion sensor networks

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Biometric Sensor Management: Tradeoffs in Time, Accuracy and Energy

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IEEE SYSTEMS JOURNAL 2009年第4期3卷 389-397页

作者： Veeramachaneni, Kalyan Osadciw, Lisa Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13210 USA

In this paper a novel sensor management algorithm is presented for a biometric sensor network. A distributed detection framework is managed for different energy, accuracy and time requirements. The design variables include sensor thresholds, fusion rule, sensor selection and sensor mode selection. Different sensors are associated with different transaction times. Hence, varying sensor modes can affect the accuracy and energy consumption. Once the sensors and their modes are selected, the accuracy achieved by this subset of sensors is maximized by managing the thresholds and the fusion rule. Risk, time and energy are the three objectives that the system attempts to minimize. The three objectives are tied into a single objective function by weighting them. A hybrid particle swarm optimization algorithm is design the system. The algorithm is a hybrid of continuous, discrete and binary particle swarm. The continuous particle swarm is used to manage the thresholds. The binary particle swarm is used to manage the fusion rule. The discrete particle swarm is used to select the sensors and the sensor mode. The system is adapted for different threat levels that depend on the a priori of imposter in the network. Results show the effectiveness of the proposed method in adapting the system to different requirements under different threat situations.

关键词： Biometrics distributed detection particle swarm optimization sensor management

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Situation assessment and autonomous control and optimisation of biometric sensor network

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INTERNATIONAL JOURNAL OF BIOMETRICS 2009年第4期1卷 495-524页

作者： Veeramachaneni, Kalyan Osadciw, Lisa Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

In this paper an adaptive sensor management algorithm is presented for a biometric sensor network. A distributed detection framework is adapted for varying security requirements in the network, by considering the trade-offs between accuracy and time. Accuracy and time are tied into a single weighted objective function and a particle swarm optimisation algorithm is designed to achieve best possible configurations for a given set of weights. Results are presented for different weights applied to the bi-objective problem. A Bayesian framework is proposed for estimating the a priori of the imposter in real time. This determines the security requirements of the network. The estimation uses the observations collected from the sensors for different individuals accessing the network via the distributed detection framework. The distributed detection framework is redesigned for the new updated a priori, resulting in a closed loop control of a biometric sensor network. Results show that the new adaptive sensor management algorithm leads to lower false acceptance and false rejection rates when compared to a network without the adaptive algorithm.

关键词： biometric sensor network management distributed detection PSO particle swarm optimisation Bayesian learning

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distributed fuzzy CFAR detection for Weibull clutter

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IEICE TRANSACTIONS ON COMMUNICATIONS 2008年第2期E91B卷 543-552页

作者： Zaimbashi, Amir Taban, Mohammad Reza Nayebi, Mohammad Mehdi Yazd Univ Dept Elect Engn Yazd Iran Sharif Univ Technol Tehran Iran Isfahan Univ Technol Informat & Commun Technol Inst Tehran Iran

In distributed detection systems, restricting the output of the local decision to one bit certainly implies a substantial information loss. In this paper, we consider the fuzzy detection, which uses a function called membership function for mapping the observation space of each local detector to a value between 0 and 1, indicating the degree of assurance about presence or absence of a signal. In this case, we examine the problem of distributed Maximum Likelihood (ML) and Order Statistic (OS) constant false alarm rate (CFAR) detections using fuzzy fusion rules such as "Algebraic Product"(AP), "Algebraic Sum"(AS), "Union"(Un) and "Intersection"(IS) in the fusion centre. For the Weibull clutter, the expression of the membership function based on the ML or OS CFAR processors in the local detectors is also obtained. For comparison, we consider a binary distributed detector, which uses the Maximum Likelihood and Algebraic Product (MLAP) or Order Statistic and Algebraic Product (OSAP) CFAR processors as the local detectors. In homogenous and non homogenous situations, multiple targets or clutter edge, the performances of the fuzzy and binary distributed detectors are analyzed and compared. The simulation results indicate the superior and robust performance of the distributed systems using fuzzy detection in the homogenous and non homogenous situations.

关键词： distributed detection OS-CFAR ML-CFAR fuzzy Weibull

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distributed structures, sequential optimization, and quantization for detection

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2008年第4期56卷 1740-1745页

作者： Lexa, Michael A. Johnson, Don H. Rice Univ Dept Elect & Comp Engn Houston TX 77251 USA

In the design of distributed quantization systems, one inevitably confronts two types of constraints-those imposed by a distributed system's structure and those imposed by how the distributed system is optimized. Structural constraints are inherent properties of any distributed quantization system and are normally summarized by functional relationships defining the inputs and outputs of their component quantizers. The use of suboptimal optimization methods are often necessitated by the computational complexity encountered in distributed problems. This correspondence briefly explores the impact and interplay of these two types of constraints in the context of distributed quantization for detection. We introduce two structures that exploit interquantizer communications and that represent extremes in terms of their structural constraints. We then develop a sequential optimization scheme that maximizes the Kullback-Leibler divergence, takes advantage of statistical dependencies in the distributed system's output variables, and leads to simple parameterizations of the component quantization rules. We present an illustrative example from which we draw insights into how these constraints influence the quantization boundaries and affect performance relative to lower and upper bounds.

关键词： distributed detection Kullback-Leibler divergence quantization for detection sequential optimization

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Some distributed Algorithms for Quantized Consensus Problem

Some Distributed Algorithms for Quantized Consensus Problem

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5th International Conference on Intelligent Computing

作者： He, Jianping Chen, Wenhai Gao, Lixin Wenzhou Univ Inst Operat Res & Control Sci Wenzhou 325027 Zhejiang Peoples R China

ISBN: (纸本)9783642040696

In this paper, we propose some distributed algorithms for quantized consensus. These algorithms are used to study the distributed averaging problem on arbitrary connected graphs and arbitrary connected weighted graphs, with the additional constraint that the weight value at each node is tin integer. These algorithms can guarantee the system achieve consensus with some moderate assumptions and can use to solve several application problems, such as averaging in a network with finite capacity channels and load balancing in a processor network, which can be modeled as distributed averaging problem.

关键词： Quantization distributed detection Consensus

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OPTIMAL DECISION FUSION IN THROUGH-THE-WALL RADAR IMAGING

OPTIMAL DECISION FUSION IN THROUGH-THE-WALL RADAR IMAGING

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15th IEEE/SP Workshop on Statistical Signal Processing

作者： Debes, Christian Amin, Moeness G. Zoubir, Abdelhak M. Tech Univ Darmstadt Signal Proc Grp Darmstadt Germany Villanova Univ Ctr Adv Commun Villanova PA USA

ISBN: (纸本)9781424427093

We consider the problem of target detection behind walls based on optimum decision fusion using Neyman-Pearson tests. A framework, demonstrating the use of multiple sensor platforms and distributed detection for the emerging application area of Through-the-Wall Radar Imaging is presented. We derive the optimum decision rule at the fusion center for three dissimilar sensors and compare the corresponding target detection results to that achieved when using a centralized decision approach. Real data generated using a two dimensional scanning system is used for the performance comparison.

关键词： Through-the-wall radar imaging distributed detection decision fusion

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Study on distributed Optic Fiber gas Sensors

Study on Distributed Optic Fiber gas Sensors

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IEEE International Conference on Robotics and Biomimetics (ROBIO)

作者： Liu, Jin Yang, Haima Yue, Yan Shanghai Univ Engn Sci Coll Elect & Elect Engn Shanghai 201620 Peoples R China Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China

ISBN: (纸本)9781424447749

Online detection of gas concentrations is important research topic recently. Based on the analysis of near infrared spectral absorption of acetylene, ammonia and carbon monoxide, a system using absorption type optic fiber for high sensitivity distributed detection of gases with wideband light source is demonstrated. Light source modulation harmonic measurement is presented in this paper. Wavelength modulation is realized by three modulated reflective fiber Bragg gratings and piezoelectric ceramics to obtain different narrowband output light. The three center wavelengths of fiber Bragg gratings' narrowband spectra are adapt to the absorption lines of acetylene, ammonia and carbon monoxide and in the region of the optical fiber' low-loss windows. The light is introduced by optic fiber to different gas cells with different gases for distributed detection. The high sensitivity detection of gases can be measured from the second harmonic signal Each ratio of second harmonic signal and transmission signal is used to eliminate the disturbance of light source. Sensitivity is proved and cost is reduced. Visual instrument software Labview is used for programming.

关键词： distributed detection harmonic measurement visual instrument

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