TAILIEUCHUNG - Báo cáo sinh học: " Research Article Tracking Algorithms for Multistatic Sonar Systems"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Research Article Tracking Algorithms for Multistatic Sonar Systems | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 461538 28 pages doi 2010 461538 Research Article Tracking Algorithms for Multistatic Sonar Systems Martina Daun1 and Frank Ehlers2 1 Department of Sensor Data and Information Fusion SDF Fraunhofer FKIE Neuenahrer Strafe 20 53343 Wachtberg Germany 2NATO Undersea Research Centre NURC 19126 La Spezia Italy Correspondence should be addressed to Frank Ehlers frankehlers@ Received 3 December 2009 Revised 5 May 2010 Accepted 23 June 2010 Academic Editor Christoph F. Mecklenbrauker Copyright 2010 M. Daun and F. Ehlers. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Activated reconnaissance systems based on target illumination are of high importance for surveillance tasks where targets are nonemitting. Multistatic configurations where multiple illuminators and multiple receivers are located separately are of particular interest. The fusion of measurements is a prerequisite for extracting and maintaining target tracks. The inherent ambiguity of the data makes the use of adequate algorithms such as multiple hypothesis tracking inevitable. For their design the understanding of the residual clutter the sensor resolution and the characteristic impact of the propagation medium is important. This leads to precise sensor models which are able to determine the performance of the surveillance team. Incorporating these models in multihypothesis tracking leads to a situationally aware data fusion and tracking algorithm. Various implementations of this algorithm are evaluated with the help of simulated and measured data sets. Incorporating model knowledge leads to increased performance but only if the model is in line with the physical reality we need to find a compromise between refined and robust tracking .

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