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Proceedings Paper

Analysis of the NEKF performance in a multisensor environment
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Paper Abstract

One technique that has been applied to the tracking of maneuvering targets is the neural extended Kalman filter. The technique adapts the motion model used by the Kalman filter tracker. This adaptation of the model is performed by a neural network that trains on-line using the same residuals as the track states. The behavior of this technique with multiple sensors providing different measurement types and different update rates has not previously been discussed; previous works have always employed a single sensor, usually providing a position measurement or a range-bearing measurement. In actual applications, multiple sensors are typically employed. These sensors often provide measurements at different rates or with the different accuracy. Such issues can have a detrimental effect on the performance of the neural network. The results of multiple-sensor data with variations in the update rates and measurement accuracy to an NEKF estimation system are analyzed. The analysis is based upon the case of two non-collocated sensors providing range-bearing measurements at varying rates applied to the tracking of an actual aircraft flight trajectory.

Paper Details

Date Published: 15 September 2005
PDF: 10 pages
Proc. SPIE 5913, Signal and Data Processing of Small Targets 2005, 591310 (15 September 2005); doi: 10.1117/12.613061
Show Author Affiliations
Stephen C. Stubberud, The Boeing Co. (United States)
Kathleen A. Kramer, Univ. of San Diego (United States)

Published in SPIE Proceedings Vol. 5913:
Signal and Data Processing of Small Targets 2005
Oliver E. Drummond, Editor(s)

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