Automatic self-localization is a critical need for the effective use of ad hoc sensor networks in military or civilian applications. In general, self-localization involves the combination of absolute ...location information (e.g., from a global positioning system) with relative calibration information (e.g., distance measurements between sensors) over regions of the network. Furthermore, it is generally desirable to distribute the computational burden across the network and minimize the amount of intersensor communication. We demonstrate that the information used for sensor localization is fundamentally local with regard to the network topology and use this observation to reformulate the problem within a graphical model framework. We then present and demonstrate the utility of nonparametric belief propagation (NBP), a recent generalization of particle filtering, for both estimating sensor locations and representing location uncertainties. NBP has the advantage that it is easily implemented in a distributed fashion, admits a wide variety of statistical models, and can represent multimodal uncertainty. Using simulations of small to moderately sized sensor networks, we show that NBP may be made robust to outlier measurement errors by a simple model augmentation, and that judicious message construction can result in better estimates. Furthermore, we provide an analysis of NBP's communications requirements, showing that typically only a few messages per sensor are required, and that even low bit-rate approximations of these messages can be used with little or no performance impact.
Determining the structure of dependencies among a set of variables is a common task in many signal and image processing applications, including multitarget tracking and computer vision. In this ...paper, we present an information-theoretic, machine learning approach to problems of this type. We cast this problem as a hypothesis test between factorizations of variables into mutually independent subsets. We show that the likelihood ratio can be written as sums of two sets of Kullback-Leibler (KL) divergence terms. The first set captures the structure of the statistical dependencies within each hypothesis, whereas the second set measures the details of model differences between hypotheses. We then consider the case when the signal prior models are unknown, so that the distributions of interest must be estimated directly from data, showing that the second set of terms is (asymptotically) negligible and quantifying the loss in hypothesis separability when the models are completely unknown. We demonstrate the utility of nonparametric estimation methods for such problems, providing a general framework for determining and distinguishing between dependency structures in highly uncertain environments. Additionally, we develop a machine learning approach for estimating lower bounds on KL divergence and mutual information from samples of high-dimensional random variables for which direct density estimation is infeasible. We present empirical results in the context of three prototypical applications: association of signals generated by sources possessing harmonic behavior, scene correspondence using video imagery, and detection of coherent behavior among sets of moving objects.
Nonparametric belief propagation for self-calibration in sensor networks Ihler, Alexander T.; Fisher, John W.; Moses, Randolph L. ...
Information Processing In Sensor Networks: Proceedings of the third international symposium on Information processing in sensor networks; 26-27 Apr. 2004,
04/2004
Conference Proceeding
Automatic self-calibration of ad-hoc sensor networks is a critical need for their use in military or civilian applications. In general, self-calibration involves the combination of absolute location ...information (e.g. GPS) with relative calibration information (e.g. time delay or received signal strength between sensors) over regions of the network. Furthermore, it is generally desirable to distribute the computational burden across the network and minimize the amount of inter-sensor communication. We demonstrate that the information used for sensor calibration is fundamentally local with regard to the network topology and use this observation to reformulate the problem within a graphical model framework. We then demonstrate the utility of nonparametric belief propagation (NBP), a recent generalization of particle filtering, for both estimating sensor locations and representing location uncertainties. NBP has the advantage that it is easily implemented in a distributed fashion, admits a wide variety of statistical models, and can represent multi-modal uncertainty. We illustrate the performance of NBP on several example networks while comparing to a previously published nonlinear least squares method.
Nonparametric estimators for online signature authentication Ihler, A.T.; Fisher, J.W.; Willsky, A.S.
2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221),
2001, Volume:
6
Conference Proceeding
We present extensions to our previous work in modelling dynamical processes. The approach uses an information theoretic criterion for searching over subspaces of the past observations, combined with ...a nonparametric density characterizing its relation to one-step-ahead prediction and uncertainty. We use this methodology to model handwriting stroke data, specifically signatures, as a dynamical system and show that it is possible to learn a model capturing their dynamics for use either in synthesizing realistic signatures and in discriminating between signatures and forgeries even though no forgeries have been used in constructing the model. This novel approach yields promising results even for small training sets.
Distributed fusion in sensor networks Cetin, M.; Lei Chen; Fisher, J.W. ...
IEEE signal processing magazine,
07/2006, Volume:
23, Issue:
4
Magazine Article
This paper presents an overview of research conducted to bridge the rich field of graphical models with the emerging field of data fusion for sensor networks. Both theoretical issues and prototyping ...applications are discussed in addition to suggesting new lines of reasoning.
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