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The problem of decision fusion in wireless sensor networks for distributed detection applications has mainly been considered in scenarios where sensor observations are conditionally independent and both local sensor statistics as well as wireless channel conditions are available for fusion rule design. In this paper, kernel-based learning algorithms for the design of decision fusion rules are presented...
Blind localization and tracking of mobile terminals in urban scenarios is an important requirement for offering new location based services, handling emergency cases of non-subscribed users, public safety etc. In this context, we propose a track-before-detect scheme, taking explicit advantage of multipath propagation in an urban terrain by using a priori information about the known locations of the...
We give a range of techniques to effectively apply on-line learning algorithms, such as Perceptron and Winnow, to both on-line and batch fusion problems. Our first technique is a new way to combine the predictions of multiple hypotheses. These hypotheses are selected from the many hypotheses that are generated in the course of on-line learning. Our second technique is to save old instances and use...
A computationally efficient, grid-based estimation method is presented for multiple source identification from distributed sensor data. Under the assumption that the sources are located on a grid over the region of interest, the solution to the problem of multiple source identification, that is, estimation of the number, locations, and intensities of the sources, is represented by a large sparse vector...
This paper presents a theoretical framework for Bayesian estimation in the case of imprecisely known probability density functions. The lack of knowledge about the true density functions is represented by sets of densities. A formal Bayesian estimator for these sets is introduced, which is intractable for infinite sets. To obtain a tractable filter, properties of convex sets in form of convex polytopes...
In this paper, a scaled unscented Kalman filter (SUKF) based on the quaternion concept is designed for integrating inertial navigation system (INS) aided by GPS measurements under large attitude error conditions. In this feedback filter, only the bias effects are considered to be independent states and are used to compensate for navigation errors. To preserve the nonlinear nature of the unit quaternion,...
Linear filtering in the presence of timing uncertainty is considered. In the model assumed here the true measurement times are intermittently available and noisy measurement times are always available. The estimation problem involves jointly estimating the state and the timing error parameters. The optimal Bayesian estimator cannot be found in closed-form so three approximations are proposed. The...
In this paper we focus on targets which, in addition to reflecting signals themselves, also have a trailing path behind them, called a wake. When the detections are fed to a tracking system like the Probabilistic Data Association Filter, the estimated track can be misled and sometimes lose the real target because of the wake. This problem becomes even more severe in multitarget environments where...
This paper deals with the analysis of robustness for bearings-only tracking. We focus on the case where the trajectory of the target follows some assumed parametric model with an unknown additive perturbation. We investigate the parametric estimator obtained using least squares. We prove that the difference between this estimator and the true parameter may be expanded along directions with intensities...
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