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Koch W.,Fraunhofer Institute for Communication, Information Processing and Ergonomics
IEEE Aerospace and Electronic Systems Magazine

This tutorial paper provides a short introduction to selected aspects of sensor data fusion by discussing characteristic examples. We consider three cases when fusion of sensor data is important: when emphasis is placed on data produced at different instants of time (i.e., target tracking), when data being collected from different sensor sources are important, and when we have data with background information on the sensor performance as well as data. with nonsensor context information. The feedback from data processing to the data acquisition process is illustrated by a sensor management example. © 2010 IEEE. Source

Kaune R.,Fraunhofer Institute for Communication, Information Processing and Ergonomics
15th International Conference on Information Fusion, FUSION 2012

In sensor networks, passive localization can be performed by exploiting the received signals of unknown emitters. In this paper, the Time of Arrival (TOA) measurements are investigated. Often, the unknown time of emission is eliminated by calculating the difference between two TOA measurements where Time Difference of Arrival (TDOA) measurements are obtained. In TOA processing, additionally, the unknown time of emission is to be estimated. Therefore, the target state is extended by the unknown time of emission. A comparison is performed investigating the attainable accuracies for localization based on TDOA and TOA measurements given by the Cramér-Rao Lower Bound (CRLB). Using the Maximum Likelihood estimator, some characteristic features of the cost functions are investigated indicating a better performance of the TOA approach. But counterintuitive, Monte Carlo simulations do not support this indication, but show the comparability of TDOA and TOA localization. © 2012 ISIF (Intl Society of Information Fusi). Source

Nickel U.,Fraunhofer Institute for Communication, Information Processing and Ergonomics
International Journal of Antennas and Propagation

Array processing for radar is well established in the literature, but only few of these algorithms have been implemented in real systems. The reason may be that the impact of these algorithms on the overall system must be well understood. For a successful implementation of array processing methods exploiting the full potential, the desired radar task has to be considered and all processing necessary for this task has to be eventually adapted. In this tutorial paper, we point out several viewpoints which are relevant in this context: the restrictions and the potential provided by different array configurations, the predictability of the transmission function of the array, the constraints for adaptive beamforming, the inclusion of monopulse, detection and tracking into the adaptive beamforming concept, and the assessment of superresolution methods with respect to their application in a radar system. The problems and achieved results are illustrated by examples from previous publications. © 2013 Ulrich Nickel. Source

Wendzel S.,Fraunhofer Institute for Communication, Information Processing and Ergonomics
Communications of the ACM

Surveying unresolved security problems for automated buildings. © 2016 ACM. Source

Feldmann M.,Fraunhofer Institute for Communication, Information Processing and Ergonomics | Koch W.,Fraunhofer Institute for Communication, Information Processing and Ergonomics
IEEE Transactions on Aerospace and Electronic Systems

This correspondence shows that the expression for the marginal density of the kinematic state in [1], which is valid only if the kinematic state is fully observable by the sensor, can be generalized. Our second comment concerns the direct integration of a scaling factor for the extent estimate. Thirdly, we show an equivalent update step based on sequential processing of single measurements (instead of the indirect parameters "mean measurement" and "measurement spread" in [1]). © 1965-2011 IEEE. Source

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