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Optimal Precoding Design for Decentralized Detection of Deterministic signals

Optimal Precoding Design for Decentralized Detection of Dete...

引用

ieee 7th sensor array and multichannel signal processing workshop (SAM)

作者： Fang, Jun Li, Hongbin Chen, Zhi Li, Shaoqian Univ Elect Sci & Technol China Natl Key Lab Commun Chengdu 610054 Peoples R China Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ 07030 USA

ISBN: (纸本)9781467310710

We consider a decentralized detection problem in a power-constrained wireless sensor networks (WSNs), in which a number of sensor nodes collaborate to detect the presence of a deterministic vector signal. the signal to be detected is assumed known a priori. Each sensor conducts a local linear processing to convert its observations into one or multiple messages. the messages are conveyed to the fusion center (FC) by an uncoded amplify-and-forward scheme, where a global decision is made. Given a total network transmit power constraint, we investigate the optimal linear processing strategy for each sensor. Our study finds that the optimal linear precoder has the form of a matched filter. Depending on the channel characteristics, one or multiple versions of the filtered/compressed message should be reported to the FC.

关键词： Decentralized detection precoding design wireless sensor networks

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DECENTRALIZED DATA processing AND MANAGEMENT IN SMART GRID VIA GOSSIPING

DECENTRALIZED DATA PROCESSING AND MANAGEMENT IN SMART GRID V...

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ieee 7th sensor array and multichannel signal processing workshop (SAM)

作者： Li, Xiao Wang, Zhifang Scaglione, Anna Univ Calif Davis Dept Elect & Comp Engn Davis CA 95616 USA

ISBN: (纸本)9781467310710

In this paper, we propose a model for a peer-to-peer database system architecture where each server is aware of the power system state vector, and that can provide a scalable architecture for the Smart Grid. the key new component is a decentralized gossip-based state estimation (G-SE) algorithm included as a stored database procedure, which constantly updates via network diffusion, the global state information vector in each server of the distributed database system (DDBS). In addition to provide global situation awareness, state aware DDBS can attain communication and storage efficiency first, by using the temporal and spatial correlation of the state to compress it and, second, by storing the state as side information to store measurement residuals. thanks to the G-SE primitive DDBS are more resilient to network attaks, can reduce network usage and streamline query processing for Smart Grid applications.

关键词： Distributed database system (DDBS) smart grid

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sensor Network Distributed Computation for Direct Position Determination

Sensor Network Distributed Computation for Direct Position D...

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ieee 7th sensor array and multichannel signal processing workshop (SAM)

作者： Pourhomayoun, Mohammad Fowler, Mark L. SUNY Binghamton Dept Elect & Comp Engn Binghamton NY 13902 USA

ISBN: (纸本)9781467310710

Classical localization systems based on TDOA/FDOA use a two-stage estimation approach. In the first stage, pairs of sensors share data to estimate TDOA/FDOA. then, the extracted TDOA/FDOA measurements are used to estimate the emitter location. In some recently published methods, an optimal single-stage approach named Direct Position Determination (DPD) has been proposed to improve the position estimation accuracy. However, unlike the classical two-stage method where the TDOA/FDOA estimation can be distributed across all sensors, DPD processes all the received signals together at a single sensor node. However, when sensors have limited computational capabilities it is desirable to distribute the computation across all sensors. Furthermore, concentrating all the processing into a single node makes the location system less robust to the loss of sensors. In this paper, we develop a distributed localization method with the goal of reducing the computational load on each sensor and increasing the reliability of the system.

关键词： Time difference of arrival

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Distributed Posterior Cramer-Rao Lower Bound for Nonlinear Sequential Bayesian Estimation

Distributed Posterior Cramer-Rao Lower Bound for Nonlinear S...

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ieee 7th sensor array and multichannel signal processing workshop (SAM)

作者： Mohammadi, Arash Asif, Amir York Univ Toronto ON M3J 1P3 Canada

ISBN: (纸本)9781467310710

In distributed sensor networks, the posterior Cramer-Rao lower bound (PCRLB) has recently been used [1] as a selection criteria for sensor management decisions, where new sensor nodes are deployed or existing ones reactivated to optimize the network's performance. Previous algorithms to compute the PCRLB are derived for the centralized [2] and hierarchical architectures [3] using a fusion centre that makes them inappropriate for distributed sensor management. Only recently a suboptimal expression [1] for the distributed architecture has been proposed, which can at times lead to large errors especially in systems with highly non-linear dynamics. the paper derives the optimal PCRLB for the distributed architecture. In other words, we derive a recursive procedure to determine the overall Fisher information matrix (FIM), i.e., the inverse of the PCRLB, from local FIMs of the distributed estimators. the proposed distributed PCRLB is independent of the filtering mechanism used and closely follows its centralized counterpart.

关键词： Bayesian Estimation Distributed signal processing Particle Filters Posterior Cramer Rao Lower Bounds

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Compressed Sensing Radar Surveillance Networks

Compressed Sensing Radar Surveillance Networks

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ieee 7th sensor array and multichannel signal processing workshop (SAM)

作者： Schmidt, Aurora Harley, Joel B. Moura, Jose M. F. Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9781467310710

We study the problem of sensor fusion in a simplified radar surveillance application. A potentially large number of narrowband radars with isotropic antennas monitor a two-dimensional area for an unknown number of targets. We use techniques from compressive sensing to distribute efficient projections of network observations, allowing for reconstruction of the target scene using a single snapshot of sensor data. We avoid the use of a fusion node, allowing all radars to individually estimate target locations after iterative communication with neighboring sensors. We study the robustness of the discretization of continuous target locations, comparing estimation performance of basis pursuit reconstruction methods to a sparse estimator based on a model-robust formulation. We test the approach on simulated scenarios, showing tradeoffs in the resolution of target localization as well as the communication bandwidths required for this inter-radar cooperation scheme.

关键词： distributed radar sensor fusion consensus compressed sensing noise-aware basis pursuit model robust estimation

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thwarting Traffic Analysis: A signal processing Perspective

Thwarting Traffic Analysis: A Signal Processing Perspective

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ieee 7th sensor array and multichannel signal processing workshop (SAM)

作者： Mishra, Abhishek Venkitasubramaniam, Parv Lehigh Univ ECE Dept Bethlehem PA 18015 USA

ISBN: (纸本)9781467310710

Traffic analysis, where eavesdroppers retrieve networking information such as source-destination pairs and paths of data flow, severely compromises user privacy and can equip an adversary to launch more powerful network attacks. Anonymous communication, where users exchange information without revealing the communicating parties is essential in any data network. Chaum Mixing, where relay nodes or proxy servers use cryptographic and batching strategies to mask source identities, is a well technique to provide anonymous communication on the Internet. the mathematical analysis of Chaum mixes and the design of mixing strategies have utilized an information theoretic measure of anonymity based on Shannon entropy. In this work, a statistical signal processing framework is proposed to study optimal mixing strategies and the achievable anonymity. Using the detection time of an adversary as a metric, the maximum achievable anonymity of a mix is characterized as a function of its available memory and the allowed rate of dummy transmissions. It is shown that, in the absence of dummy transmissions, the maximum allowable time for anonymous transmission increases quadratically with the buffer size, and for a fixed buffer size, the system approaches perfect unobservability at a rate inversely proportional to the rate of dummy transmissions.

关键词： Traffic Analysis Anonymity Manikins detection time Transmissions crosscut data flow signal processing Inverse Proportion

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On the consistency of likelihood penalization methods in large sensor networks

On the consistency of likelihood penalization methods in lar...

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ieee 7th sensor array and multichannel signal processing workshop (SAM)

作者： Vallet, Pascal Loubaton, Philippe Mestre, Xavier Lab Informat Gaspard Monge IGM 5 Bd Descartes F-77454 Marne La Vallee France CTTC Barcelona 08860 Spain

ISBN: (纸本)9781467310710

this paper is devoted to the problem of source detection with large sensor networks, in a context where the number of available samples N and the number of antennas M are of the same order of magnitude. We focus here on the popular likelihood penalization (LP) methods, such as Minimum Description Length (MDL) or Akaike Information Criterion (AIC). Such methods have been widely studied in the context where N >> M, and in particular the consistency of the MDL and the inconsistency of the AIC estimator were established in the asymptotic regime where N -> infinity while M remains constant. We propose here an analysis in the asymptotic regime where M, N both converge to infinity at the same rate, and using results from random matrix theory, we establish conditions on the penalty term to ensure consistency of LP methods in this latter regime. As a consequence, we deduce that the MDL method is always inconsistent while the AIC method can be consistent in certain situations.

关键词： sensor networks

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Ordering for Shift-in-Mean of Gauss-Markov Random Fields with Dependent Observations

Ordering for Shift-in-Mean of Gauss-Markov Random Fields wit...

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ieee 7th sensor array and multichannel signal processing workshop (SAM)

作者： Zhang, Jiangfan Wei, Chuanming Blum, Rick S. Lehigh Univ Bethlehem PA 18015 USA

ISBN: (纸本)9781467310710

Previous work on ordered transmission approaches showed significant transmission savings but focused entirely on cases with statistically independent observations at a set of sensor nodes. Here we take the first steps toward applying ordering to cases with statistically dependent observations. While we focus on a particular signal detection problem, we choose one of the most well studied problems, detection of a shift-in-mean for a multivariate Gaussian distribution. We employ the well developed theory of decomposable graphical models, and focus on cases where the observations are taken at a set of sensor nodes which can be grouped into a set of cliques. We assume the nodes within a clique are physically close, so that inner-clique communications can be considered extremely inexpensive. We present the computation of the overall likelihood ratio as a new sum, distinctly different from the sum over a set of independent variables, which implies it is possible to employ ordering over the cliques in an attempt to limit the number of communications from each clique to the place where the clique data will be combined. We present results that imply we can often save a significant portion of these transmission, which is lower bounded by half of the number of cliques. We describe necessary conditions for the result to hold and provide numerical results indicating these conditions are satisfied in many cases of practical interest.

关键词： sensor nodes

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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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Low Rank Tensor STAP filter based on multilinear SVD

Low Rank Tensor STAP filter based on multilinear SVD

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ieee 7th sensor array and multichannel signal processing workshop (SAM)

作者： Boizard, Maxime Ginolhac, Guillaume Pascal, Frederic Forster, Philippe ENS Cachan SATIE 61 Av President Wilson F-94230 Cachan France SONDRA Super Elect F-91192 Gif Sur Yvette France

ISBN: (纸本)9781467310710

Space Time Adaptive processing. STAP) is a two-dimensional adaptive filtering technique which uses jointly temporal and spatial dimensions to suppress disturbance and to improve target detection. Disturbance contains both the clutter arriving from signal backscattering of the ground and the thermal noise resulting from the sensors noise. In practical cases, the STAP clutter can be considered to have a low rank structure. Using this assumption, a low rank vector STAP filter is derived based on the projector onto the clutter subspace. With new STAP applications like MIMO STAP or polarimetric STAP, the generalization of the classic filters to multidimensional configurations arises. A possible solution consists in keeping the multidimensional structure and in extending the classic filters with multilinear algebra. Using the low-rank structure of the clutter, we propose in this paper a new low-rank tensor STAP filter based on a generalization of the Higher Order Singular Value Decomposition. HOSVD) in order to use at the same time the simple. for example time, spatial, polarimetric, ...) and the combined information. for example spatio-temporal). Results are shown for two cases : classic 2D STAP and 3D polarimetric STAP. In the classic case, vector and tensor filters are equivalent. In the polarimetric case, we show the enhancement of the tensor filter.

关键词： Space time adaptive processing

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