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Within the hyperspectral community, change detection is a continued area of interest. Interesting changes in imagery typically correspond to changes in material reflectance associated with pixels in the scene. Using a physical model describing the sensor-reaching radiance, change detection can be formulated as a statistical hypothesis test. Complicating the problem of change detection is the presence of shadow, illumination, and atmospheric differences, as well as misregistration and parallax error, which often produce the appearance of change. The proposed physical model incorporates terms to account for both direct and diffuse shadow fractions to help mitigate false alarms associated with shadow differences between scenes. The resulting generalized likelihood ratio test (GLRT) provides an indicator of change at each pixel. The maximum likelihood estimates of the physical model parameters used for the GLRT are obtained from the entire joint data set to take advantage of coupled information existing between pixel measurements. Simulation results using synthetic and real imagery demonstrate the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

Ad hoc localization of wireless sensor nodes is a fundamental problem in wireless sensor networks. Despite the recent proposals for the development of ad hoc localization algorithms, the fundamental behavior in systems using measurements has not been characterized. In this paper, we take a first step toward such a characterization by examining the behavior of error inducing parameters in multihop localization systems in an algorithm independent manner. We first derive the Cramé Rao Bound for Gaussian measurement error for multihop localization systems using distance and angular measurements. Later on, we use these bounds on a carefully controlled set of scenarios to study the trends in the error induced by the measurement technology accuracy, network density, beacon node concentration, and beacon uncertainty. By exposing these trends, the goal of this paper is to develop a fundamental understanding of the error behavior that can provide a set of guidelines to be considered during the design and deployment of multihop localization systems. [ABSTRACT FROM AUTHOR]

This paper discusses reconstruction of three-dimensional surfaces from multiple bistatic synthetic aperture radar (SAR) images. Techniques for surface reconstruction from multiple monostatic SAR images already exist, including interferometric processing and stereo SAR. We generalize these methods to obtain algorithms for bistatic interferometric SAR and bistatic stereo SAR. We also propose a framework for predicting the performance of our multistatic stereo SAR algorithm, and, from this framework, we suggest a metric for use in planning strategic deployment of multistatic assets. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We consider the effect of unknown signal propagation velocity on direction-of-arrival (DOA) estimation performance using an array of sensors. For general arrays, the DOA estimation variance, as measured by the Cramér-Rao bound (CRB), degrades when the propagation velocity is unknown. In this letter, we show that for both two- and three-dimensional arrays, the CRB on the DOA angle is independent of whether or not the propagation velocity is known if and only if the sensor locations satisfy the isotropy conditions given in earlier work. The results hold for both narrowband and wideband signals. [ABSTRACT FROM AUTHOR]

Details a study which presented a Bayesian approach for model-based classification of images with application to synthetic-aperture radar. Physical model for sensor data and feature extraction; Hypothesis testing; Example of Bayes classification.

Presents a convergence study of the frequency sensitive competitive learning (FSCL) algorithm. Approximation of the final phase of FSCL learning; Demonstration of how the convergence conditions involve only the learning rate; Comment that the analysis expands the class of algorithms that have been shown to have convergence characteristics.