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检索条件"主题词=distributed detection"

共 624 条记录，以下是61-70 订阅

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distributed detection in Wireless Sensor Networks Using Complex Field Network Coding

Distributed Detection in Wireless Sensor Networks Using Comp...

引用

16th International Conference on Information Fusion (FUSION)

作者： Eritmen, Kayhan Keskinoz, Mehmet Sabanci Univ Dept Elect Engn Istanbul Turkey

ISBN: (纸本)9786058631113

The signal transmission and the information fusion in the wireless sensor networks (WSN) are conventionally assumed to operate over orthogonal channels, which becomes bandwidth and throughput inefficient. To remedy this inefficiency and to improve the detection performance simultaneously, in this work, we first propose to use relaying with complex field network coding (CFNC), which operates over non-orthogonal channels. This provides not only the diversity in space but also diversity in time as well. In this method, each sensor is assigned a unique pre-determined signature and this provides robustness against the multi-access interference. Secondly, we derive the optimal likelihood ratio test (LRT) based fusion rule for the proposed system. Thirdly, we devise a way to choose the sensor signatures. Finally, we evaluate the detection performance of the proposed scheme and compare it with the performance of the conventional method. The simulation results show that we can obtain up to a 105% improvement using distributed detection with CFNC rather than employing the conventional method in literature. This suggests that the distributed detection with CFNC is a promising technique that can be used in next generation WSNs.

关键词： distributed detection fading relaying network coding wireless sensor networks

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distributed detection With A Scan Statistic: Global to Local Inference

Distributed Detection With A Scan Statistic: Global to Local...

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IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM)

作者： Song, Xiufeng Willett, Peter Glaz, Joseph Zhou, Shengli Univ Connecticut Dept Elect & Comp Engn 371 Fairfield WayU-2157 Storrs CT 06269 USA Univ Connecticut Dept Stat Storrs CT 06269 USA

ISBN: (纸本)9781467310710

Sensor networks offer an opportunity for improving submarine detection. Let each sensor firstly make a binary local decision-'0' or '1', and then a fusion center collects them to make a system-level inference without knowledge of probabilities of local detection. An obvious strategy is a counting rule test, which simply counts the total number of 1's and compares it to a threshold. This approach equally considers all network subareas. However, reflected signals from a submarine are highly aspect dependent, and in many instances only sensors in a particular zone could receive the echoes. This paper focuses on the scan statistic, which slides a window across the sensor field, and selects the subarea with the largest number of 1's to make a decision. The scan statistic integrates the spatial distinction of local decisions into detection fusion. With a proper window size, it may suppress subarea interference, and improve system-level performance.

关键词： distributed detection detection fusion scan statistic counting rule test sensor network submarine

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distributed detection of Rayleigh target in K-distributed clutter

Distributed detection of Rayleigh target in K-Distributed cl...

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Conference on Sensor Fusion - Architectures, Algorithms, and Applications II

作者： Gowda, CH Viswanathan, R So Illinois Univ Dept Elect Engn Carbondale IL 62901 USA

ISBN: (纸本)0819428256

detection of target in the presence of Gaussian clutter using multiple radars have been studied in the recent literature. The algorithms considered include pure decision based fusion rules such as OR and AND and tests involving the fusion of partial information, such as normalized test statistic (NTS). However, based on experimental evidence, in many situations, the compound K-distributed model seems to be a good fit for the clutter envelope. In this paper we study through simulation the performances of the NTS, AND, and OR tests for the detection of Rayleigh target in the presence of K-distributed clutter. The results show that for large signal to clutter power ratio and for large shape parameter values, the K-NTS significantly outperforms both the OR and the AND rules.

关键词： distributed detection CFAR detection K-distribution

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distributed detection/LOCALIZATION OF NETWORK ANOMALIES USING RANK TESTS

DISTRIBUTED DETECTION/LOCALIZATION OF NETWORK ANOMALIES USIN...

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15th IEEE/SP Workshop on Statistical Signal Processing

作者： Lung-Yut-Fong, A. Levy-Leduc, C. Cappe, O. Inst Telecom CNRS Telecom ParisTech LTCI 46 Rue Barrault F-75634 Paris 13 France

ISBN: (纸本)9781424427093

We propose an efficient and decentralized method for detecting change-points in high-dimensional data. This issue is of growing concern to the network security community since, in this context, network anomalies such as Denial of Service (DoS) attacks are likely to lead to statistical changes in Internet traffic. Our method proposes a way of distributing a centralized approach called TopRank, described in [1], which consists of a data reduction stage based on record filtering, followed by a nonparametric change-point detection test based on U-statistics. The key point is to aggregate censored time series built locally and to perform a nonparametric test for doubly censored time series resulting from this aggregation. With this new approach, called distributed TopRank in the following, we can address massive data streams and perform network anomaly detection and localization on the fly while limiting the quantity of data exchanged within the network.

关键词： distributed detection change-point rank tests network anomaly sensor network

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distributed detection in UWB Sensor Networks under Non-Orthogonal Nakagami-m Fading

Distributed Detection in UWB Sensor Networks under Non-Ortho...

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IEEE 74th Vehicular Technology Conference (VTC)

作者： Mehbodniya, A. Bielefeld, D. Aissa, S. Mathar, R. Adachi, F. Univ Quebec INRS Montreal PQ H3C 3P8 Canada Rhein Westfal TH Aachen Aachen Germany KAUST Thuwal Saudi Arabia

ISBN: (纸本)9781424483273

Several attractive features of ultra wideband (UWB) communications make it a good candidate for physical-layer of wireless sensor networks (WSN). These features include low power consumption, low complexity and low cost of implementation. In this paper, we present an opportunistic power assignment strategy for distributed detection in parallel fusion WSNs, considering a Nakagami-m fading model for the communication channel and time-hopping (TH) UWB for the transmitter circuit of the sensor nodes. In a parallel fusion WSN, local decisions are made by local sensors and transmitted through wireless channels to a fusion center. The fusion center processes the information and makes the final decision. Simulation results are provided for the global probability of detection error and relative performance gain to evaluate the efficiency of the proposed power assignment strategy in different fading environments.

关键词： UWB Wireless Sensor Networks distributed detection Parallel Fusion Network Nakagami-m Fading

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distributed detection OF SPARSE SIGNALS WITH 1-BIT DATA IN TWO-LEVEL TWO-DEGREE TREE-STRUCTURED SENSOR NETWORKS

DISTRIBUTED DETECTION OF SPARSE SIGNALS WITH 1-BIT DATA IN T...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Li, Chengxi Li, Gang Varshney, Pramod K. Tsinghua Univ Dept Elect Engn Beijing Peoples R China Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY USA

ISBN: (纸本)9781509066315

In this paper, we present a new detector for the detection of sparse stochastic signals using 1-bit data in two-level two-degree tree-structured sensor networks (2L-2D TSNs). Related prior work mostly concentrates on parallel sensor networks (PSNs). However, PSNs may sometime become impractical in many applications including the case where some sensors are beyond the communication range of the fusion center (FC). Therefore, we design the proposed detector for 2L-2D TSNs where information is transmitted hierarchically. To satisfy severe resource constraints, each local sensor performs 1-bit quantization before transmission to the FC. The FC fuses the received 1-bit data employing the locally most powerful test (LMPT). It is shown theoretically and numerically that, compared with the LMPT detector with Q sensors that transmit analog measurements in 2L-2D TSNs, the proposed 1-bit LMPT detector that uses quantization thresholds derived in this paper asymptotically requires 1.74Q sensors to compensate for the performance loss induced by local quantization.

关键词： distributed detection Locally most powerful tests Sparse signals Tree-structured sensor networks 1-bit quantization

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distributed detection in UWB-IR sensor networks with randomization of the number of pulses

Distributed detection in UWB-IR sensor networks with randomi...

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting

作者： Wang, Tsang-Yi Li, Chih-Peng Zhang, Yong-Lin Natl Sun Yat Sen Univ Inst Commun Engn Kaohsiung Taiwan

ISBN: (纸本)9781424416486

In this paper, we consider a distributed detection problem in wireless sensor networks (WSNs) using ultrawide bandwidth (UWB) communications. Due to the severe restrictions on power consumption, energy efficiency becomes a critical design issue in WSNs. UWB technology has low-power transceivers, low-complexity and low-cost circuitry which are well suited to the physical layer requirements for WSNs. In a typical parallel fusion network, local decisions are made by local sensors and transmitted through a wireless channel to a fusion center, where the final decision is made. In this paper, we control the number of UWB pulses to achieve the energy efficient distributed detection. We first theoretically characterize the performance of distributed detection using UWB communications. Based on the analysis, we then obtain the minimum number of the pulses per detection to meet the required performance. To achieve a near-optimal design, we further propose a multiple access technique based on the random number of UWB pulses. Finally. the performance evaluation is provided to demonstrate the advantage of our design.

关键词： distributed detection sensor networks energy efficiency UWB fusion

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distributed detection Fusion via Monte Carlo Importance Sampling 35

Distributed Detection Fusion via Monte Carlo Importance Samp...

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35th Chinese Control Conference (CCC)

作者： Rao, Hang Shen, Xiaojing Zhu, Yunmin Pan, Jianxin Sichuan Univ Dept Math Chengdu 610064 Peoples R China

ISBN: (纸本)9789881563910

distributed detection fusion with high-dimension conditionally dependent observations is known to be a challenging problem. When a fusion rule is fixed, this paper attempts to make progress on this problem for the large sensor networks by proposing a new Monte Carlo framework. Through the Monte Carlo importance sampling, we derive a necessary condition for optimal sensor decision rules in the sense of minimizing the approximated Bayesian cost function. Then, a Gauss-Seidel/personby-person optimization algorithm can be obtained to search the optimal sensor decision rules. It is proved that the discretized algorithm is finitely convergent. The complexity of the new algorithm is O(LN) compared with O(LNL) of the previous algorithm where L is the number of sensors and N is a constant. Thus, the proposed methods allows us to design the large sensor networks with general high-dimension dependent observations. Furthermore, an interesting result is that, for the fixed AND or OR fusion rules, we can analytically derive the optimal solution in the sense of minimizing the approximated Bayesian cost function. In general, the solution of the Gauss-Seidel algorithm is only local optimal. However, in the new framework, we can prove that the solution of Gauss-Seidel algorithm is same as the analytically optimal solution in the case of the AND or OR fusion rule. The typical examples with dependent observations and large number of sensors are examined under this new framework. The results of numerical examples demonstrate the effectiveness of the new algorithm.

关键词： distributed detection Monte Carlo importance sampling dependent observations sensor decision rule fusion rule

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distributed detection in Neural Network based Multihop Wireless Sensor Network

Distributed Detection in Neural Network based Multihop Wirel...

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9th IEEE Sensors Applications Symposium (SAS)

作者： Raval, Jabal Jagyasi, Bhushan Tata Consultancy Serv TCS Innovat Labs Mumbai Bombay Maharashtra India

ISBN: (纸本)9781479921805

In this paper, a Neural Network based data aggregation approach to detect the binary events in a multi-hop Wireless Sensor Network has been proposed. We envision every node in a network as a unit of neuron which gets trained by using the neural network based back propagation algorithm. As compared to the LMS based Adaptive Weighted Aggregation scheme for tree network, the proposed Neural Network based wireless sensor network approach leads to a significant improvement in detection accuracy without much energy losses due to communication and computation overhead. We also compare the detection accuracy of the proposed Neural Network based scheme with that of the non-adaptive Bayesian approach which requires apriori knowledge of the sensor's performance indices.

关键词： Bayes methods backpropagation neural nets telecommunication network topology wireless sensor networks LMS based adaptive weighted aggregation scheme distributed detection neural network based back propagation algorithm neural network based data aggregation approach neural network based multihop wireless sensor network nonadaptive Bayesian approach tree network Biological neural networks Least squares approximations Network topology Neurons Spread spectrum communication Wireless sensor networks

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distributed detection in Noisy Sensor Networks

Distributed Detection in Noisy Sensor Networks

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IEEE International Symposium on Information Theory (ISIT)

作者： Kar, Soummya Tandon, Ravi Poor, H. Vincent Cui, Shuguang Princeton Univ Dept Elect Engn Princeton NJ 08544 USA Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

ISBN: (纸本)9781457705953

This paper considers distributed detection over a noisy network, in which each connected sensor pair can communicate over an additive noise channel. With non-identically distributed generic sensor observations, a mixed time scale recursive algorithm for binary hypothesis testing over such networks is proposed. Under some mild assumptions on network connectivity and global detectability (the positivity of the global or centralized Kullback-Liebler divergence), this algorithm yields asymptotically zero probabilities of Type-I and Type-II errors (henceforth referred to as probabilities of error). When sensor observations are identically distributed, a simplified single time scale version of the proposed algorithm is shown to achieve asymptotically zero probabilities of error. Convergence rate guarantees in terms of asymptotic normality of certain scaled decision variables are provided for this simplified procedure. As an example, a practical Gaussian sensor network is considered, for which the error decay exponents are explicitly characterized in terms of the network and noise parameters.

关键词： AWGN channels Convergence Gaussian sensor network Heuristic algorithms Network topology Noise Noise measurement Sensors Testing additive noise channel asymptotically zero probabilities binary hypothesis testing convergence of numerical methods convergence rate distributed detection distributed generic sensor error decay exponents error statistics global detectability mixed time scale recursive algorithm network connectivity noisy sensor networks probability recursive estimation signal detection wireless sensor networks

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