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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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Robust signal Detection by Using The EEF

Robust Signal Detection by Using The EEF

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

作者： Kay, Steven Ding, Quan Rangaswamy, Muralidhar Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA Wright Patterson AFB US Air Force Res Lab Columbus OH USA

ISBN: (纸本)9781467310710

In detection theory, the optimal Neyman-Pearson rule applies when the characteristics of the signal and the noise are completely known. However, in many practical scenarios such as multipath or moving targets, only partial knowledge of the signal can be obtained. In this paper, we examine the case when the alternative hypothesis has multiple candidate models, and apply the multimodal sensor integration technique based on the exponentially embedded family to detection. It is shown that our method is asymptotically optimal as it converges to the true underlying model. Furthermore, this method is computationally efficient. We also compare the proposed method with existing classifier combining rules by simulations.

关键词： Detection exponentially embedded family multimodal sensor integration robustness

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Distributed Binary Geometric sensor arrays for Low-Data-Throughput Human Gait Biometrics

Distributed Binary Geometric Sensor Arrays for Low-Data-Thro...

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

作者： Yue, Tianyao Hao, Qi Brady, David J. Duke Univ Dept Elect & Comp Engn Box 90291 Durham NC 27708 USA Univ Alabama Dept Elect & Comp Engn Tuscaloosa AL 35487 USA

ISBN: (纸本)9781467310710

We present a novel sensing paradigm of measuring human gait. The goal of the research is to achieve low-cost gait biometrics systems with minimum data throughput for various sensing modalities. The binary measurements of the system are achieved by using both (1) periodic and (2) pseudo-random sampling structures. As a result, either static or dynamic gait features can be estimated from a one-bit data stream. The simulation results demonstrate the gait information acquisition capability of the proposed binary sensing technology.

关键词： Biometrics

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DECENTRALIZED DATA processing AND MANAGEMENT IN SMART GRID VIA GOSSIPING

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

引用

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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